diff --git a/CHANGELOG.md b/CHANGELOG.md
index 370ab430..9b33cb64 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,8 +9,12 @@
   
 ## Changelog
   
-### **HEAD -> main** 2022/02/14 mandic00@live.com
+### **2.6.4** 2022/02/27 mandic00@live.com
 
+
+### **origin/main** 2022/02/17 mandic00@live.com
+
+- refresh
 - add config option wasmplatformfetch
 
 ### **2.6.3** 2022/02/10 mandic00@live.com
diff --git a/README.md b/README.md
index e060839c..07565486 100644
--- a/README.md
+++ b/README.md
@@ -344,7 +344,7 @@ For more info, see [**Configuration Details**](https://github.com/vladmandic/hum
 
 <br><hr><br>
 
-`Human` library is written in `TypeScript` [4.5](https://www.typescriptlang.org/docs/handbook/intro.html)  
+`Human` library is written in `TypeScript` [4.6](https://www.typescriptlang.org/docs/handbook/intro.html)  
 Conforming to latest `JavaScript` [ECMAScript version 2021](https://262.ecma-international.org/) standard  
 Build target is `JavaScript` [EMCAScript version 2018](https://262.ecma-international.org/11.0/)  
 
diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map
index dc71702c..de684c96 100644
--- a/demo/faceid/index.js.map
+++ b/demo/faceid/index.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["index.ts", "indexdb.ts"],
   "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport { Human, TensorLike, FaceResult } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n  modelBasePath: '../../models',\n  filter: { equalization: true }, // lets run with histogram equilizer\n  face: {\n    enabled: true,\n    detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, // return tensor is used to get detected face image\n    description: { enabled: true }, // default model for face descriptor extraction is faceres\n    mobilefacenet: { enabled: false, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n    iris: { enabled: true }, // needed to determine gaze direction\n    emotion: { enabled: false }, // not needed\n    antispoof: { enabled: true }, // enable optional antispoof module\n    liveness: { enabled: true }, // enable optional liveness module\n  },\n  body: { enabled: false },\n  hand: { enabled: false },\n  object: { enabled: false },\n  gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n  minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n  minSize: 224, // min input to face descriptor model before degradation\n  maxTime: 10000, // max time before giving up\n  blinkMin: 10, // minimum duration of a valid blink\n  blinkMax: 800, // maximum duration of a valid blink\n  threshold: 0.5, // minimum similarity\n  mask: humanConfig.face.detector.mask,\n  rotation: humanConfig.face.detector.rotation,\n  cropFactor: humanConfig.face.detector.cropFactor,\n  ...matchOptions,\n};\n\nconst ok = { // must meet all rules\n  faceCount: false,\n  faceConfidence: false,\n  facingCenter: false,\n  lookingCenter: false,\n  blinkDetected: false,\n  faceSize: false,\n  antispoofCheck: false,\n  livenessCheck: false,\n  elapsedMs: 0, // total time while waiting for valid face\n};\nconst allOk = () => ok.faceCount && ok.faceSize && ok.blinkDetected && ok.facingCenter && ok.lookingCenter && ok.faceConfidence && ok.antispoofCheck && ok.livenessCheck;\nconst current: { face: FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n  start: 0,\n  end: 0,\n  time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('fps') as HTMLPreElement,\n  match: document.getElementById('match') as HTMLDivElement,\n  name: document.getElementById('name') as HTMLInputElement,\n  save: document.getElementById('save') as HTMLSpanElement,\n  delete: document.getElementById('delete') as HTMLSpanElement,\n  retry: document.getElementById('retry') as HTMLDivElement,\n  source: document.getElementById('source') as HTMLCanvasElement,\n  ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  // eslint-disable-next-line no-console\n  console.log(...msg);\n};\nconst printFPS = (msg) => dom.fps.innerText = msg; // print status element\n\nasync function webCam() { // initialize webcam\n  printFPS('starting webcam...');\n  // @ts-ignore resizeMode is not yet defined in tslib\n  const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n  dom.video.srcObject = stream;\n  dom.video.play();\n  await ready;\n  dom.canvas.width = dom.video.videoWidth;\n  dom.canvas.height = dom.video.videoHeight;\n  if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n    if (dom.video.paused) dom.video.play();\n    else dom.video.pause();\n  };\n}\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (current.face && current.face.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const now = human.now();\n    fps.detect = 1000 / (now - timestamp.detect);\n    timestamp.detect = now;\n    requestAnimationFrame(detectionLoop); // start new frame immediately\n  }\n}\n\nasync function validationLoop(): Promise<FaceResult> { // main screen refresh loop\n  const interpolated = await human.next(human.result); // smoothen result using last-known results\n  await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n  await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n  const now = human.now();\n  fps.draw = 1000 / (now - timestamp.draw);\n  timestamp.draw = now;\n  printFPS(`fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status\n  ok.faceCount = human.result.face.length === 1; // must be exactly detected face\n  if (ok.faceCount) { // skip the rest if no face\n    const gestures: string[] = Object.values(human.result.gesture).map((gesture) => gesture.gesture); // flatten all gestures\n    if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n    if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n    ok.blinkDetected = ok.blinkDetected || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n    if (ok.blinkDetected && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n    ok.facingCenter = gestures.includes('facing center');\n    ok.lookingCenter = gestures.includes('looking center'); // must face camera and look at camera\n    ok.faceConfidence = (human.result.face[0].boxScore || 0) > options.minConfidence && (human.result.face[0].faceScore || 0) > options.minConfidence && (human.result.face[0].genderScore || 0) > options.minConfidence;\n    ok.antispoofCheck = (human.result.face[0].real || 0) > options.minConfidence;\n    ok.livenessCheck = (human.result.face[0].live || 0) > options.minConfidence;\n    ok.faceSize = human.result.face[0].box[2] >= options.minSize && human.result.face[0].box[3] >= options.minSize;\n  }\n  let y = 32;\n  for (const [key, val] of Object.entries(ok)) {\n    let el = document.getElementById(`ok-${key}`);\n    if (!el) {\n      el = document.createElement('div');\n      el.innerText = key;\n      el.className = 'ok';\n      el.style.top = `${y}px`;\n      dom.ok.appendChild(el);\n    }\n    if (typeof val === 'boolean') el.style.backgroundColor = val ? 'lightgreen' : 'lightcoral';\n    else el.innerText = `${key}:${val}`;\n    y += 28;\n  }\n  if (allOk()) { // all criteria met\n    dom.video.pause();\n    return human.result.face[0];\n  }\n  if (ok.elapsedMs > options.maxTime) { // give up\n    dom.video.pause();\n    return human.result.face[0];\n  } else { // run again\n    ok.elapsedMs = Math.trunc(human.now() - startTime);\n    return new Promise((resolve) => {\n      setTimeout(async () => {\n        const res = await validationLoop(); // run validation loop until conditions are met\n        if (res) resolve(human.result.face[0]); // recursive promise resolve\n      }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n    });\n  }\n}\n\nasync function saveRecords() {\n  if (dom.name.value.length > 0) {\n    const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n    const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n    await indexDb.save(rec);\n    log('saved face record:', rec.name);\n  } else {\n    log('invalid name');\n  }\n}\n\nasync function deleteRecord() {\n  if (current.record && current.record.id > 0) {\n    await indexDb.remove(current.record);\n  }\n}\n\nasync function detectFace() {\n  dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n  if (!current.face || !current.face.tensor || !current.face.embedding) return false;\n  // eslint-disable-next-line no-console\n  console.log('face record:', current.face);\n  human.tf.browser.toPixels(current.face.tensor as unknown as TensorLike, dom.canvas);\n  if (await indexDb.count() === 0) {\n    log('face database is empty');\n    document.body.style.background = 'black';\n    dom.delete.style.display = 'none';\n    return false;\n  }\n  const db = await indexDb.load();\n  const descriptors = db.map((rec) => rec.descriptor);\n  const res = await human.match(current.face.embedding, descriptors, matchOptions);\n  current.record = db[res.index] || null;\n  if (current.record) {\n    log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n    dom.name.value = current.record.name;\n    dom.source.style.display = '';\n    dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n  }\n  document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n  return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n  ok.faceCount = false;\n  ok.faceConfidence = false;\n  ok.facingCenter = false;\n  ok.blinkDetected = false;\n  ok.faceSize = false;\n  ok.antispoofCheck = false;\n  ok.livenessCheck = false;\n  ok.elapsedMs = 0;\n  dom.match.style.display = 'none';\n  dom.retry.style.display = 'none';\n  dom.source.style.display = 'none';\n  document.body.style.background = 'black';\n  await webCam();\n  await detectionLoop(); // start detection loop\n  startTime = human.now();\n  current.face = await validationLoop(); // start validation loop\n  dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n  dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n  dom.source.width = dom.canvas.width;\n  dom.source.height = dom.canvas.height;\n  dom.canvas.style.width = '';\n  dom.match.style.display = 'flex';\n  dom.save.style.display = 'flex';\n  dom.delete.style.display = 'flex';\n  dom.retry.style.display = 'block';\n  if (!allOk()) { // is all criteria met?\n    log('did not find valid face');\n    return false;\n  } else {\n    return detectFace();\n  }\n}\n\nasync function init() {\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n  printFPS('loading...');\n  log('known face records:', await indexDb.count());\n  await webCam(); // start webcam\n  await human.load(); // preload all models\n  printFPS('initializing...');\n  dom.retry.addEventListener('click', main);\n  dom.save.addEventListener('click', saveRecords);\n  dom.delete.addEventListener('click', deleteRecord);\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport type FaceRecord = { id: number, name: string, descriptor: number[], image: ImageData };\n\n// eslint-disable-next-line no-console\nconst log = (...msg) => console.log('indexdb', ...msg);\n\nexport async function open() {\n  if (db) return true;\n  return new Promise((resolve) => {\n    const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n    request.onerror = (evt) => log('error:', evt);\n    request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n      log('create:', evt.target);\n      db = (evt.target as IDBOpenDBRequest).result;\n      db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n    };\n    request.onsuccess = (evt) => { // open\n      db = (evt.target as IDBOpenDBRequest).result as IDBDatabase;\n      log('open:', db);\n      resolve(true);\n    };\n  });\n}\n\nexport async function load(): Promise<FaceRecord[]> {\n  const faceDB: Array<FaceRecord> = [];\n  if (!db) await open(); // open or create if not already done\n  return new Promise((resolve) => {\n    const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n    cursor.onerror = (evt) => log('load error:', evt);\n    cursor.onsuccess = (evt) => {\n      if ((evt.target as IDBRequest).result) {\n        faceDB.push((evt.target as IDBRequest).result.value);\n        (evt.target as IDBRequest).result.continue();\n      } else {\n        resolve(faceDB);\n      }\n    };\n  });\n}\n\nexport async function count(): Promise<number> {\n  if (!db) await open(); // open or create if not already done\n  return new Promise((resolve) => {\n    const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n    store.onerror = (evt) => log('count error:', evt);\n    store.onsuccess = () => resolve(store.result);\n  });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n  if (!db) await open(); // open or create if not already done\n  const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n  db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n  log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n  if (!db) await open(); // open or create if not already done\n  db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n  log('delete:', faceRecord);\n}\n"],
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 }
diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map
index a07f857d..d092c8cf 100644
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["index.ts"],
   "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport { Human, Config } from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial<Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'webgpu' as const,\n  // async: true,\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false },\n  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env['perfadd'] = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('status') as HTMLPreElement,\n  perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detect: 0, draw: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  // eslint-disable-next-line no-console\n  console.log(...msg);\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function webCam() { // initialize webcam\n  status('starting webcam...');\n  // @ts-ignore resizeMode is not yet defined in tslib\n  const options: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(options);\n  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n  dom.video.srcObject = stream;\n  dom.video.play();\n  await ready;\n  dom.canvas.width = dom.video.videoWidth;\n  dom.canvas.height = dom.video.videoHeight;\n  const track: MediaStreamTrack = stream.getVideoTracks()[0];\n  const capabilities: MediaTrackCapabilities | string = track.getCapabilities ? track.getCapabilities() : '';\n  const settings: MediaTrackSettings | string = track.getSettings ? track.getSettings() : '';\n  const constraints: MediaTrackConstraints | string = track.getConstraints ? track.getConstraints() : '';\n  log('video:', dom.video.videoWidth, dom.video.videoHeight, track.label, { stream, track, settings, constraints, capabilities });\n  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n    if (dom.video.paused) dom.video.play();\n    else dom.video.pause();\n  };\n}\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    // console.log('profiling data:', await human.profile(dom.video));\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n    timestamp.tensors = tensors;\n  }\n  const now = human.now();\n  fps.detect = 1000 / (now - timestamp.detect);\n  timestamp.detect = now;\n  requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n  if (!dom.video.paused) {\n    const interpolated = await human.next(human.result); // smoothen result using last-known results\n    await human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.draw = 1000 / (now - timestamp.draw);\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detect.toFixed(1).padStart(5, ' ')} detect | ${fps.draw.toFixed(1).padStart(5, ' ')} draw`); // write status\n  // requestAnimationFrame(drawLoop); // refresh at screen refresh rate\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function main() { // main entry point\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('platform:', human.env.platform, '| agent:', human.env.agent);\n  status('loading...');\n  await human.load(); // preload all models\n  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n  log('loaded models:', Object.values(human.models).filter((model) => model !== null).length);\n  status('initializing...');\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await webCam(); // start webcam\n  await detectionLoop(); // start detection loop\n  await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index 99da7260..eaee1797 100644
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
@@ -4,7 +4,7 @@
   author: <https://github.com/vladmandic>'
 */
 
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l0,qe=[],jo=0,So=0,Nt=Number.MAX_SAFE_INTEGER;async function Wo(e){return R.initial&&(l0=null),l0?e.debug&&g("cached model:",l0.modelUrl):l0=await N(e.face.ssrnet.modelPathAge),l0}async function Lt(e,t,o,A){var a,i,x,d;if(!l0)return{age:0};let n=Nt<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),s=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>P()-So;return t.skipAllowed&&n&&s&&jo===A&&((x=qe[o])==null?void 0:x.age)&&((d=qe[o])==null?void 0:d.age)>0?(Nt++,qe[o]):(Nt=0,new Promise(async l=>{if(!(l0==null?void 0:l0.inputs)||!l0.inputs[0]||!l0.inputs[0].shape)return;let y={};y.resize=r.image.resizeBilinear(e,[l0.inputs[0].shape[2],l0.inputs[0].shape[1]],!1),y.enhance=r.mul(y.resize,L.tf255);let c={age:0};if(t.face.ssrnet.enabled&&(y.age=l0.execute(y.enhance)),y.age){let f=await y.age.data();c.age=Math.trunc(10*f[0])/10}Object.keys(y).forEach(f=>r.dispose(y[f])),qe[o]=c,jo=A,So=P(),l(c)}))}var h0,Ue=[],Io=0,Oo=0,Bt=Number.MAX_SAFE_INTEGER,Gt=[.2989,.587,.114];async function 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A=t.shape[1],n=t.shape[2],s=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return r.image.cropAndResize(t,s,[0],o)}function VA(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function lt(e,t=1.5){let o=Ne(e),A=it(e),n=[t*A[0]/2,t*A[1]/2],s=[o[0]-n[0],o[1]-n[1]],a=[o[0]+n[0],o[1]+n[1]];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function yt(e){let t=Ne(e),o=it(e),n=Math.max(...o)/2,s=[t[0]-n,t[1]-n],a=[t[0]+n,t[1]+n];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function Kr(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function ZA(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Kr(o)}var GA=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Z0(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function Qr(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function HA(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let s=0;s<A;s++)o[n].push(Z0(e[n],Qr(t,s)))}return o}function b5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],s=GA(t[0],t[1]),a=HA(s,n),i=GA(-t[0],-t[1]);return HA(a,i)}function XA(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-Z0(t[0],o),-Z0(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function g5(e,t){return[Z0(e,t[0]),Z0(e,t[1])]}var 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Object.keys(o).forEach(n=>r.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=r.reshape(t,[-1,7,2]),A.div=r.div(A.reshape,this.inputSizeTensor),A.landmarks=r.add(A.div,this.anchors[o]);let n=r.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(s=>r.dispose(A[s])),n}async predict(t,o){let A={};A.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=r.div(A.resize,L.tf127),A.image=r.sub(A.div,L.tf1),A.batched=this.model.execute(A.image),A.predictions=r.squeeze(A.batched),A.slice=r.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=r.sigmoid(A.slice),A.scores=r.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=r.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await r.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await A.nms.array(),a=[];for(let i of s){let x={};x.box=r.slice(A.norm,[i,0],[1,-1]),x.slice=r.slice(A.predictions,[i,5],[1,14]),x.norm=this.normalizeLandmarks(x.slice,i),x.palmLandmarks=r.reshape(x.norm,[-1,2]);let d=await x.box.data(),l=d.slice(0,2),y=d.slice(2,4),c=await x.palmLandmarks.array(),f={startPoint:l,endPoint:y,palmLandmarks:c,confidence:n[i]},h=VA(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(x).forEach(m=>r.dispose(x[m]))}return Object.keys(A).forEach(i=>r.dispose(A[i])),a}};var e2=5,UA=1.65,JA=[0,5,9,13,17,1,2],t2=0,o2=2,YA=0,M5=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),A=t.map(a=>a[1]),n=[Math.min(...o),Math.min(...A)],s=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:s}}getBoxForPalmLandmarks(t,o){let A=t.map(s=>g5([...s,1],o)),n=this.calculateLandmarksBoundingBox(A);return lt(yt(n),e2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=lt(yt(o),UA);A.palmLandmarks=[];for(let n=0;n<JA.length;n++)A.palmLandmarks.push(t[JA[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let s=it(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(f=>[a[0]*(f[0]-this.inputSize/2),a[1]*(f[1]-this.inputSize/2),a[2]*f[2]]),x=b5(A,[0,0]),d=i.map(f=>[...g5(f,x),f[2]]),l=XA(n),y=[...Ne(o),1],c=[Z0(y,l[0]),Z0(y,l[1])];return d.map(f=>[Math.trunc(f[0]+c[0]),Math.trunc(f[1]+c[1]),Math.trunc(f[2])])}async estimateHands(t,o){let A=!1,n,s=(o.hand.skipTime||0)>P()-YA,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(n=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,n&&n.length>0&&(n.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...n],this.storedBoxes.length>0&&(A=!0));let i=[];for(let x=0;x<this.storedBoxes.length;x++){let d=this.storedBoxes[x];if(!!d)if(o.hand.landmarks){let l=o.hand.rotation?ZA(d.palmLandmarks[t2],d.palmLandmarks[o2]):0,y=Ne(d),c=[y[0]/t.shape[2],y[1]/t.shape[1]],f=o.hand.rotation&&R.kernels.includes("rotatewithoffset")?r.image.rotateWithOffset(t,l,0,c):t.clone(),h=b5(-l,y),m=A?this.getBoxForPalmLandmarks(d.palmLandmarks,h):d,v=FA(m,f,[this.inputSize,this.inputSize]),b=r.div(v,L.tf255);r.dispose(v),r.dispose(f);let[M,p]=this.handPoseModel.execute(b);YA=P(),r.dispose(b);let u=(await M.data())[0];if(r.dispose(M),u>=o.hand.minConfidence/4){let E=r.reshape(p,[-1,3]),W=await E.array();r.dispose(p),r.dispose(E);let C=this.transformRawCoords(W,m,l,h),S=this.getBoxForHandLandmarks(C);this.storedBoxes[x]={...S,confidence:u};let O={landmarks:C,confidence:u,boxConfidence:d.confidence,fingerConfidence:u,box:{topLeft:S.startPoint,bottomRight:S.endPoint}};i.push(O)}else this.storedBoxes[x]=null;r.dispose(p)}else{let l=lt(yt(d),UA),y={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:l.startPoint,bottomRight:l.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(x=>x!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var i0={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>i0.nameMapping[e],getPoints:e=>i0.pointsMapping[e]},X0={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>X0.nameMapping[e]},U={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>U.nameMapping[e]},$0=class{constructor(t){w(this,"name");w(this,"curls");w(this,"directions");w(this,"weights");w(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,o,A){typeof 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v=Math.sqrt(n*n+i*i),b=Math.sqrt(s*s+x*x),M=Math.sqrt(a*a+d*d),p=Math.max(v,b,M),u=e[0],E=e[1],W=o[0],C=o[1];p===v?(W=o[0],C=o[1]):p===M&&(u=t[0],E=t[1]);let V=$A([u,E],[W,C]),B=_A(V,oe.TOTAL_ANGLE_VOTE_POWER);c+=B[0],f+=B[1],h+=B[2];for(let t0 of A){let z=_A(t0,oe.SINGLE_ANGLE_VOTE_POWER);c+=z[0],f+=z[1],h+=z[2]}let H;return c===Math.max(c,f,h)?H=tn(x,i,d,y):h===Math.max(f,h)?H=en(s,n,a,l):H=l2(x,i,d,y,s,n,a,l),H}function on(e){let t=[],o=[],A=[],n=[];if(!e)return{curls:A,directions:n};for(let s of i0.all){let a=i0.getPoints(s),i=[],x=[];for(let d of a){let l=e[d[0]],y=e[d[1]],c=$A(l,y),f=c[0],h=c[1];i.push(f),x.push(h)}t.push(i),o.push(x)}for(let s of i0.all){let a=s===i0.thumb?1:0,i=i0.getPoints(s),x=e[i[a][0]],d=e[i[a+1][1]],l=e[i[3][1]],y=i2(x,d,l),c=y2(x,d,l,t[s].slice(a));A[s]=y,n[s]=c}return{curls:A,directions:n}}function xt(e){if(!e||e.length===0)return null;let t=on(e),o={};for(let A of i0.all)o[i0.getName(A)]={curl:X0.getName(t.curls[A]),direction:U.getName(t.directions[A])};return o}function An(e){let t=[];if(!e||e.length===0)return t;let o=on(e);for(let A of KA){let n=A.matchAgainst(o.curls,o.directions);n>=a2&&t.push({name:A.name,confidence:n})}return t}var nn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},he,be,rn;async function R5(e,t){let o=await rn.estimateHands(e,t);if(!o)return[];let A=[];for(let n=0;n<o.length;n++){let s={};if(o[n].landmarks)for(let l of Object.keys(nn))s[l]=nn[l].map(y=>o[n].landmarks[y]);let a=o[n].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],x=[0,0,0,0];if(a&&a.length>0){for(let l of a)l[0]<i[0]&&(i[0]=l[0]),l[1]<i[1]&&(i[1]=l[1]),l[0]>i[2]&&(i[2]=l[0]),l[1]>i[3]&&(i[3]=l[1]);i[2]-=i[0],i[3]-=i[1],x=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=o[n].box?[Math.trunc(Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.max(0,o[n].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[n].box.bottomRight[0])-Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[n].box.bottomRight[1])-Math.max(0,o[n].box.topLeft[1]))]:[0,0,0,0],x=[o[n].box.topLeft[0]/(e.shape[2]||0),o[n].box.topLeft[1]/(e.shape[1]||0),(o[n].box.bottomRight[0]-o[n].box.topLeft[0])/(e.shape[2]||0),(o[n].box.bottomRight[1]-o[n].box.topLeft[1])/(e.shape[1]||0)];let d=xt(a);A.push({id:n,score:Math.round(100*o[n].confidence)/100,boxScore:Math.round(100*o[n].boxConfidence)/100,fingerScore:Math.round(100*o[n].fingerConfidence)/100,label:"hand",box:i,boxRaw:x,keypoints:a,annotations:s,landmarks:d})}return A}async function w5(e){var o,A;R.initial&&(he=null,be=null),!he||!be?[he,be]=await Promise.all([e.hand.enabled?N((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?N((A=e.hand.skeleton)==null?void 0:A.modelPath):null]):(e.debug&&g("cached model:",he.modelUrl),e.debug&&g("cached model:",be.modelUrl));let t=new P5(he);return rn=new M5(t,be),[he,be]}var $=[null,null],x2=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],J0=[[0,0],[0,0]],c2=["hand","fist","pinch","point","face","tip","pinchtip"],an=4,ln=1.6,d2=512,f2=1.4,ct=Number.MAX_SAFE_INTEGER,k5=0,L0=[0,0],J={boxes:[],hands:[]},yn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function xn(e){var t;if(R.initial&&($[0]=null),$[0])e.debug&&g("cached model:",$[0].modelUrl);else{dt(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),$[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values($[0].modelSignature.inputs);J0[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,J0[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[0]}async function cn(e){var t;if(R.initial&&($[1]=null),$[1])e.debug&&g("cached model:",$[1].modelUrl);else{$[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values($[1].modelSignature.inputs);J0[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,J0[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[1]}async function m2(e,t){let o=[];if(!e||!$[0])return o;let A={},n=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,d2),a=Math.round(s*n/8)*8;A.resize=r.image.resizeBilinear(e,[s,a]),A.cast=r.cast(A.resize,"int32"),[A.rawScores,A.rawBoxes]=await $[0].executeAsync(A.cast,x2),A.boxes=r.squeeze(A.rawBoxes,[0,2]),A.scores=r.squeeze(A.rawScores,[0]);let i=r.unstack(A.scores,1);r.dispose(i[an]),i.splice(an,1),A.filtered=r.stack(i,1),r.dispose(i),A.max=r.max(A.filtered,1),A.argmax=r.argMax(A.filtered,1);let x=0;A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await A.nms.data(),l=await A.max.data(),y=await A.argmax.data();for(let c of Array.from(d)){let f=r.slice(A.boxes,c,1),h=await f.data();r.dispose(f);let m=[h[1],h[0],h[3]-h[1],h[2]-h[0]],v=tt(m,f2),b=[Math.trunc(m[0]*L0[0]),Math.trunc(m[1]*L0[1]),Math.trunc(m[2]*L0[0]),Math.trunc(m[3]*L0[1])],M=l[c],p=c2[y[c]],u={id:x++,score:M,box:b,boxRaw:v,label:p};o.push(u)}return Object.keys(A).forEach(c=>r.dispose(A[c])),o.sort((c,f)=>f.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function E5(e,t,o){let A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&$[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=r.image.cropAndResize(e,[s],[0],[J0[1][0],J0[1][1]],"bilinear"),n.div=r.div(n.crop,L.tf255),[n.score,n.keypoints]=$[1].execute(n.div,["Identity_1","Identity"]);let a=(await n.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){A.fingerScore=i,n.reshaped=r.reshape(n.keypoints,[-1,3]);let l=(await n.reshaped.array()).map(y=>[y[0]/J0[1][1],y[1]/J0[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);A.keypoints=l.map(y=>[L0[0]*(y[0]+t.boxRaw[0]),L0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),A.landmarks=xt(A.keypoints);for(let y of Object.keys(yn))A.annotations[y]=yn[y].map(c=>A.landmarks&&A.keypoints[c]?A.keypoints[c]:null)}Object.keys(n).forEach(x=>r.dispose(n[x]))}return A}async function z5(e,t){var n,s;if(!$[0]||!$[1]||!((n=$[0])==null?void 0:n.inputs[0].shape)||!((s=$[1])==null?void 0:s.inputs[0].shape))return[];L0=[e.shape[2]||0,e.shape[1]||0],ct++;let o=(t.hand.skipTime||0)>P()-k5,A=ct<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?J.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>P()-k5,x=ct<3*(t.hand.skipFrames||0);t.skipAllowed&&J.hands.length===t.hand.maxDetected?J.hands=await Promise.all(J.boxes.map(l=>E5(e,l,t))):t.skipAllowed&&i&&x&&J.hands.length>0?J.hands=await Promise.all(J.boxes.map(l=>E5(e,l,t))):(J.boxes=await m2(e,t),k5=P(),J.hands=await Promise.all(J.boxes.map(l=>E5(e,l,t))),ct=0);let d=[...J.boxes];if(J.boxes.length=0,t.cacheSensitivity>0)for(let l=0;l<J.hands.length;l++){let y=nA(J.hands[l].keypoints,L0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&J.hands[l].fingerScore&&J.hands[l].fingerScore>(t.hand.minConfidence||0)){let c=tt(y.box,ln),f=tt(y.boxRaw,ln);J.boxes.push({...d[l],box:c,boxRaw:f})}}for(let l=0;l<J.hands.length;l++){let y=S0(J.hands[l].keypoints,L0);J.hands[l].box=y.box,J.hands[l].boxRaw=y.boxRaw}a(J.hands)})}var r0,ft=[],j5=Number.MAX_SAFE_INTEGER,fn=0,mn=0;async function pn(e){var t;return R.initial&&(r0=null),r0?e.debug&&g("cached model:",r0.modelUrl):r0=await N((t=e.face.liveness)==null?void 0:t.modelPath),r0}async function S5(e,t,o,A){var a,i;if(!r0)return 0;let n=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>P()-mn,s=j5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&n&&s&&fn===A&&ft[o]?(j5++,ft[o]):(j5=0,new Promise(async x=>{let d=r.image.resizeBilinear(e,[(r0==null?void 0:r0.inputs[0].shape)?r0.inputs[0].shape[2]:0,(r0==null?void 0:r0.inputs[0].shape)?r0.inputs[0].shape[1]:0],!1),l=r0==null?void 0:r0.execute(d),y=(await l.data())[0];ft[o]=Math.round(100*y)/100,fn=A,mn=P(),r.dispose([d,l]),x(ft[o])}))}var Le={};Ze(Le,{connected:()=>pt,horizontal:()=>W5,kpt:()=>mt,relative:()=>I5,vertical:()=>C5});var mt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],W5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],C5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],I5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],pt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var hn=.005,d0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function O5(e){for(let t of W5){let o=e.keypoints.findIndex(n=>n.part===t[0]),A=e.keypoints.findIndex(n=>n.part===t[1]);if(e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[0]<e.keypoints[A].position[0]){let n=e.keypoints[o];e.keypoints[o]=e.keypoints[A],e.keypoints[A]=n}}for(let t of C5){let o=e.keypoints.findIndex(n=>n&&n.part===t[0]),A=e.keypoints.findIndex(n=>n&&n.part===t[1]);e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[1]<e.keypoints[A].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of I5){let A=e.keypoints.findIndex(d=>d&&d.part===t[0]),n=e.keypoints.findIndex(d=>d&&d.part===t[1]),s=e.keypoints.findIndex(d=>d&&d.part===o[0]),a=e.keypoints.findIndex(d=>d&&d.part===o[1]);if(!e.keypoints[s]||!e.keypoints[a])continue;let i=e.keypoints[A]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[A].position[0])]:[0,0],x=e.keypoints[n]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0];if(i[0]>i[1]||x[0]>x[1]){let d=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=d}}}function bn(e){for(let t=0;t<e.length;t++)if(e[t]&&d0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-d0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-d0.keypoints[t].positionRaw[1])];o[0]<hn&&o[1]<hn?e[t]=d0.keypoints[t]:d0.keypoints[t]=e[t]}else d0.keypoints[t]=e[t];return e}function gn(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;d0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=r.pad(e,d0.padding),o.resize=r.image.resizeBilinear(o.pad,[t,t]);let A=r.cast(o.resize,"int32");return Object.keys(o).forEach(n=>r.dispose(o[n])),A}function Pn(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+d0.padding[2][0]+d0.padding[2][1])/t[0]-d0.padding[2][0],A.position[1]*(t[1]+d0.padding[1][0]+d0.padding[1][1])/t[1]-d0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=S0(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var f0,ut=0,N5=Number.MAX_SAFE_INTEGER,Ae={boxes:[],bodies:[],last:0};async function Mn(e){return R.initial&&(f0=null),f0?e.debug&&g("cached model:",f0.modelUrl):(dt(["size"],e),f0=await N(e.body.modelPath)),ut=f0.inputs[0].shape?f0.inputs[0].shape[2]:0,ut<64&&(ut=256),f0}async function u2(e,t,o){let A=e[0][0],n=[],s=0;for(let l=0;l<A.length;l++)if(s=A[l][2],s>t.body.minConfidence){let y=[A[l][1],A[l][0]];n.push({score:Math.round(100*s)/100,part:mt[l],positionRaw:y,position:[Math.round((o.shape[2]||0)*y[0]),Math.round((o.shape[1]||0)*y[1])]})}s=n.reduce((l,y)=>y.score>l?y.score:l,0);let a=[],i=S0(n.map(l=>l.position),[o.shape[2],o.shape[1]]),x={};for(let[l,y]of Object.entries(pt)){let c=[];for(let f=0;f<y.length-1;f++){let h=n.find(v=>v.part===y[f]),m=n.find(v=>v.part===y[f+1]);h&&m&&h.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&c.push([h.position,m.position])}x[l]=c}let d={id:0,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:n,annotations:x};return O5(d),a.push(d),a}async function h2(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let s=e[0][n],a=Math.round(100*s[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let y=0;y<17;y++){let c=s[3*y+2];if(c>t.body.minConfidence){let f=[s[3*y+1],s[3*y+0]];i.push({part:mt[y],score:Math.round(100*c)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let x=S0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),d={};for(let[y,c]of Object.entries(pt)){let f=[];for(let h=0;h<c.length-1;h++){let m=i.find(b=>b.part===c[h]),v=i.find(b=>b.part===c[h+1]);m&&v&&m.score>(t.body.minConfidence||0)&&v.score>(t.body.minConfidence||0)&&f.push([m.position,v.position])}d[y]=f}let l={id:n,score:a,box:x.box,boxRaw:x.boxRaw,keypoints:[...i],annotations:d};O5(l),A.push(l)}}return A.sort((n,s)=>s.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function L5(e,t){if(!f0||!(f0==null?void 0:f0.inputs[0].shape))return[];t.skipAllowed||(Ae.boxes.length=0),N5++;let o=(t.body.skipTime||0)>P()-Ae.last,A=N5<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ae.bodies:new Promise(async n=>{let s={};N5=0,s.input=gn(e,ut),s.res=f0==null?void 0:f0.execute(s.input),Ae.last=P();let a=await s.res.array();Ae.bodies=s.res.shape[2]===17?await u2(a,t,e):await h2(a,t,e);for(let i of Ae.bodies)Pn(i,[e.shape[2]||1,e.shape[1]||1]),bn(i.keypoints);Object.keys(s).forEach(i=>r.dispose(s[i])),n(Ae.bodies)})}var ge,ht=[],Tn=0,B5=Number.MAX_SAFE_INTEGER,gt=0,bt=2.5;async function Rn(e){if(!ge||R.initial){ge=await N(e.object.modelPath);let t=Object.values(ge.modelSignature.inputs);gt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&g("cached model:",ge.modelUrl);return ge}async function b2(e,t,o){let A=0,n=[];for(let x of[1,2,4])r.tidy(async()=>{let d=x*13,l=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)===xe.length)),y=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)<xe.length)),f=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await l.array();for(let m=0;m<l.shape[0];m++)for(let v=0;v<l.shape[1];v++){let b=h[m][v];if(b>(o.object.minConfidence||0)&&v!==61){let M=(.5+Math.trunc(m%d))/d,p=(.5+Math.trunc(m/d))/d,u=f[m].map(H=>H*(d/x/gt)),[E,W]=[M-bt/x*u[0],p-bt/x*u[1]],[C,S]=[M+bt/x*u[2]-E,p+bt/x*u[3]-W],O=[E,W,C,S];O=O.map(H=>Math.max(0,Math.min(H,1)));let V=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],B={id:A++,score:Math.round(100*b)/100,class:v+1,label:xe[v].label,box:V.map(H=>Math.trunc(H)),boxRaw:O};n.push(B)}}});e.forEach(x=>r.dispose(x));let s=n.map(x=>[x.boxRaw[1],x.boxRaw[0],x.boxRaw[3],x.boxRaw[2]]),a=n.map(x=>x.score),i=[];if(s&&s.length>0){let x=await r.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await x.data(),r.dispose(x)}return n=n.filter((x,d)=>i.includes(d)).sort((x,d)=>d.score-x.score),n}async function G5(e,t){let o=(t.object.skipTime||0)>P()-Tn,A=B5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&ht.length>0?(B5++,ht):(B5=0,!R.kernels.includes("mod")||!R.kernels.includes("sparsetodense")?ht:new Promise(async n=>{let s=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[gt,gt],!1),i=r.div(a,L.tf255),x=i.transpose([0,3,1,2]);r.dispose(i),r.dispose(a);let d;t.object.enabled&&(d=ge.execute(x)),Tn=P(),r.dispose(x);let l=await b2(d,s,t);ht=l,n(l)}))}var Ge=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],g2=Ge.length,Be=Ge.reduce((e,t,o)=>(e[t]=o,e),{}),P2=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Ha=P2.map(([e,t])=>[Be[e],Be[t]]),kn=[["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"]];function En(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(A,i),minX:Math.min(n,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function zn(e,[t,o],[A,n]){let s=t/A,a=o/n,i=(d,l)=>({id:l,score:d.score,boxRaw:[d.box[0]/n,d.box[1]/A,d.box[2]/n,d.box[3]/A],box:[Math.trunc(d.box[0]*a),Math.trunc(d.box[1]*s),Math.trunc(d.box[2]*a),Math.trunc(d.box[3]*s)],keypoints:d.keypoints.map(({score:y,part:c,position:f})=>({score:y,part:c,position:[Math.trunc(f.x*a),Math.trunc(f.y*s)],positionRaw:[f.x/A,f.y/A]})),annotations:{}});return e.map((d,l)=>i(d,l))}var H5=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=A}};function 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A=t.shape[1],n=t.shape[2],s=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return r.image.cropAndResize(t,s,[0],o)}function VA(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function lt(e,t=1.5){let o=Ne(e),A=it(e),n=[t*A[0]/2,t*A[1]/2],s=[o[0]-n[0],o[1]-n[1]],a=[o[0]+n[0],o[1]+n[1]];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function yt(e){let t=Ne(e),o=it(e),n=Math.max(...o)/2,s=[t[0]-n,t[1]-n],a=[t[0]+n,t[1]+n];return{startPoint:s,endPoint:a,palmLandmarks:e.palmLandmarks}}function Kr(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function ZA(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Kr(o)}var GA=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Z0(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function Qr(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function HA(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let s=0;s<A;s++)o[n].push(Z0(e[n],Qr(t,s)))}return o}function b5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],s=GA(t[0],t[1]),a=HA(s,n),i=GA(-t[0],-t[1]);return HA(a,i)}function XA(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-Z0(t[0],o),-Z0(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function g5(e,t){return[Z0(e,t[0]),Z0(e,t[1])]}var 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Object.keys(o).forEach(n=>r.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=r.reshape(t,[-1,7,2]),A.div=r.div(A.reshape,this.inputSizeTensor),A.landmarks=r.add(A.div,this.anchors[o]);let n=r.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(s=>r.dispose(A[s])),n}async predict(t,o){let A={};A.resize=r.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=r.div(A.resize,L.tf127),A.image=r.sub(A.div,L.tf1),A.batched=this.model.execute(A.image),A.predictions=r.squeeze(A.batched),A.slice=r.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=r.sigmoid(A.slice),A.scores=r.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=r.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await r.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let s=await A.nms.array(),a=[];for(let i of s){let x={};x.box=r.slice(A.norm,[i,0],[1,-1]),x.slice=r.slice(A.predictions,[i,5],[1,14]),x.norm=this.normalizeLandmarks(x.slice,i),x.palmLandmarks=r.reshape(x.norm,[-1,2]);let d=await x.box.data(),l=d.slice(0,2),y=d.slice(2,4),c=await x.palmLandmarks.array(),f={startPoint:l,endPoint:y,palmLandmarks:c,confidence:n[i]},h=VA(f,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);a.push(h),Object.keys(x).forEach(m=>r.dispose(x[m]))}return Object.keys(A).forEach(i=>r.dispose(A[i])),a}};var e2=5,UA=1.65,JA=[0,5,9,13,17,1,2],t2=0,o2=2,YA=0,M5=class{constructor(t,o){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let o=t.map(a=>a[0]),A=t.map(a=>a[1]),n=[Math.min(...o),Math.min(...A)],s=[Math.max(...o),Math.max(...A)];return{startPoint:n,endPoint:s}}getBoxForPalmLandmarks(t,o){let A=t.map(s=>g5([...s,1],o)),n=this.calculateLandmarksBoundingBox(A);return lt(yt(n),e2)}getBoxForHandLandmarks(t){let o=this.calculateLandmarksBoundingBox(t),A=lt(yt(o),UA);A.palmLandmarks=[];for(let n=0;n<JA.length;n++)A.palmLandmarks.push(t[JA[n]].slice(0,2));return A}transformRawCoords(t,o,A,n){let s=it(o),a=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],i=t.map(f=>[a[0]*(f[0]-this.inputSize/2),a[1]*(f[1]-this.inputSize/2),a[2]*f[2]]),x=b5(A,[0,0]),d=i.map(f=>[...g5(f,x),f[2]]),l=XA(n),y=[...Ne(o),1],c=[Z0(y,l[0]),Z0(y,l[1])];return d.map(f=>[Math.trunc(f[0]+c[0]),Math.trunc(f[1]+c[1]),Math.trunc(f[2])])}async estimateHands(t,o){let A=!1,n,s=(o.hand.skipTime||0)>P()-YA,a=this.skipped<(o.hand.skipFrames||0);o.skipAllowed&&s&&a&&(n=await this.handDetector.predict(t,o),this.skipped=0),o.skipAllowed&&this.skipped++,n&&n.length>0&&(n.length!==this.detectedHands&&this.detectedHands!==o.hand.maxDetected||!o.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...n],this.storedBoxes.length>0&&(A=!0));let i=[];for(let x=0;x<this.storedBoxes.length;x++){let d=this.storedBoxes[x];if(!!d)if(o.hand.landmarks){let l=o.hand.rotation?ZA(d.palmLandmarks[t2],d.palmLandmarks[o2]):0,y=Ne(d),c=[y[0]/t.shape[2],y[1]/t.shape[1]],f=o.hand.rotation&&R.kernels.includes("rotatewithoffset")?r.image.rotateWithOffset(t,l,0,c):t.clone(),h=b5(-l,y),m=A?this.getBoxForPalmLandmarks(d.palmLandmarks,h):d,v=FA(m,f,[this.inputSize,this.inputSize]),b=r.div(v,L.tf255);r.dispose(v),r.dispose(f);let[M,p]=this.handPoseModel.execute(b);YA=P(),r.dispose(b);let u=(await M.data())[0];if(r.dispose(M),u>=o.hand.minConfidence/4){let E=r.reshape(p,[-1,3]),W=await E.array();r.dispose(p),r.dispose(E);let C=this.transformRawCoords(W,m,l,h),S=this.getBoxForHandLandmarks(C);this.storedBoxes[x]={...S,confidence:u};let O={landmarks:C,confidence:u,boxConfidence:d.confidence,fingerConfidence:u,box:{topLeft:S.startPoint,bottomRight:S.endPoint}};i.push(O)}else this.storedBoxes[x]=null;r.dispose(p)}else{let l=lt(yt(d),UA),y={confidence:d.confidence,boxConfidence:d.confidence,fingerConfidence:0,box:{topLeft:l.startPoint,bottomRight:l.endPoint},landmarks:[]};i.push(y)}}return this.storedBoxes=this.storedBoxes.filter(x=>x!==null),this.detectedHands=i.length,i.length>o.hand.maxDetected&&(i.length=o.hand.maxDetected),i}};var i0={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>i0.nameMapping[e],getPoints:e=>i0.pointsMapping[e]},X0={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>X0.nameMapping[e]},U={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>U.nameMapping[e]},$0=class{constructor(t){w(this,"name");w(this,"curls");w(this,"directions");w(this,"weights");w(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,o,A){typeof 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en(e,t,o,A){let n;return A===Math.abs(e)?e>0?n=U.horizontalLeft:n=U.horizontalRight:A===Math.abs(t)?t>0?n=U.horizontalLeft:n=U.horizontalRight:o>0?n=U.horizontalLeft:n=U.horizontalRight,n}function tn(e,t,o,A){let n;return A===Math.abs(e)?e<0?n=U.verticalDown:n=U.verticalUp:A===Math.abs(t)?t<0?n=U.verticalDown:n=U.verticalUp:o<0?n=U.verticalDown:n=U.verticalUp,n}function l2(e,t,o,A,n,s,a,i){let x,d=tn(e,t,o,A),l=en(n,s,a,i);return d===U.verticalUp?l===U.horizontalLeft?x=U.diagonalUpLeft:x=U.diagonalUpRight:l===U.horizontalLeft?x=U.diagonalDownLeft:x=U.diagonalDownRight,x}function y2(e,t,o,A){let n=e[0]-t[0],s=e[0]-o[0],a=t[0]-o[0],i=e[1]-t[1],x=e[1]-o[1],d=t[1]-o[1],l=Math.max(Math.abs(n),Math.abs(s),Math.abs(a)),y=Math.max(Math.abs(i),Math.abs(x),Math.abs(d)),c=0,f=0,h=0,m=y/(l+1e-5);m>1.5?c+=oe.DISTANCE_VOTE_POWER:m>.66?f+=oe.DISTANCE_VOTE_POWER:h+=oe.DISTANCE_VOTE_POWER;let 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i=o[n].box?[Math.trunc(Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.max(0,o[n].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,o[n].box.bottomRight[0])-Math.max(0,o[n].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,o[n].box.bottomRight[1])-Math.max(0,o[n].box.topLeft[1]))]:[0,0,0,0],x=[o[n].box.topLeft[0]/(e.shape[2]||0),o[n].box.topLeft[1]/(e.shape[1]||0),(o[n].box.bottomRight[0]-o[n].box.topLeft[0])/(e.shape[2]||0),(o[n].box.bottomRight[1]-o[n].box.topLeft[1])/(e.shape[1]||0)];let d=xt(a);A.push({id:n,score:Math.round(100*o[n].confidence)/100,boxScore:Math.round(100*o[n].boxConfidence)/100,fingerScore:Math.round(100*o[n].fingerConfidence)/100,label:"hand",box:i,boxRaw:x,keypoints:a,annotations:s,landmarks:d})}return A}async function w5(e){var o,A;R.initial&&(he=null,be=null),!he||!be?[he,be]=await Promise.all([e.hand.enabled?N((o=e.hand.detector)==null?void 0:o.modelPath):null,e.hand.landmarks?N((A=e.hand.skeleton)==null?void 0:A.modelPath):null]):(e.debug&&g("cached model:",he.modelUrl),e.debug&&g("cached model:",be.modelUrl));let t=new P5(he);return rn=new M5(t,be),[he,be]}var $=[null,null],x2=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],J0=[[0,0],[0,0]],c2=["hand","fist","pinch","point","face","tip","pinchtip"],an=4,ln=1.6,d2=512,f2=1.4,ct=Number.MAX_SAFE_INTEGER,k5=0,L0=[0,0],J={boxes:[],hands:[]},yn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function xn(e){var t;if(R.initial&&($[0]=null),$[0])e.debug&&g("cached model:",$[0].modelUrl);else{dt(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),$[0]=await N((t=e.hand.detector)==null?void 0:t.modelPath);let o=Object.values($[0].modelSignature.inputs);J0[0][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,J0[0][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[0]}async function cn(e){var t;if(R.initial&&($[1]=null),$[1])e.debug&&g("cached model:",$[1].modelUrl);else{$[1]=await N((t=e.hand.skeleton)==null?void 0:t.modelPath);let o=Object.values($[1].modelSignature.inputs);J0[1][0]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[1].size):0,J0[1][1]=Array.isArray(o)?parseInt(o[0].tensorShape.dim[2].size):0}return $[1]}async function m2(e,t){let o=[];if(!e||!$[0])return o;let A={},n=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,d2),a=Math.round(s*n/8)*8;A.resize=r.image.resizeBilinear(e,[s,a]),A.cast=r.cast(A.resize,"int32"),[A.rawScores,A.rawBoxes]=await $[0].executeAsync(A.cast,x2),A.boxes=r.squeeze(A.rawBoxes,[0,2]),A.scores=r.squeeze(A.rawScores,[0]);let i=r.unstack(A.scores,1);r.dispose(i[an]),i.splice(an,1),A.filtered=r.stack(i,1),r.dispose(i),A.max=r.max(A.filtered,1),A.argmax=r.argMax(A.filtered,1);let x=0;A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await A.nms.data(),l=await A.max.data(),y=await A.argmax.data();for(let c of Array.from(d)){let f=r.slice(A.boxes,c,1),h=await f.data();r.dispose(f);let m=[h[1],h[0],h[3]-h[1],h[2]-h[0]],v=tt(m,f2),b=[Math.trunc(m[0]*L0[0]),Math.trunc(m[1]*L0[1]),Math.trunc(m[2]*L0[0]),Math.trunc(m[3]*L0[1])],M=l[c],p=c2[y[c]],u={id:x++,score:M,box:b,boxRaw:v,label:p};o.push(u)}return Object.keys(A).forEach(c=>r.dispose(A[c])),o.sort((c,f)=>f.score-c.score),o.length>(t.hand.maxDetected||1)&&(o.length=t.hand.maxDetected||1),o}async function E5(e,t,o){let A={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&$[1]&&o.hand.landmarks&&t.score>(o.hand.minConfidence||0)){let n={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];n.crop=r.image.cropAndResize(e,[s],[0],[J0[1][0],J0[1][1]],"bilinear"),n.div=r.div(n.crop,L.tf255),[n.score,n.keypoints]=$[1].execute(n.div,["Identity_1","Identity"]);let a=(await n.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(o.hand.minConfidence||0)){A.fingerScore=i,n.reshaped=r.reshape(n.keypoints,[-1,3]);let l=(await n.reshaped.array()).map(y=>[y[0]/J0[1][1],y[1]/J0[1][0],y[2]||0]).map(y=>[y[0]*t.boxRaw[2],y[1]*t.boxRaw[3],y[2]||0]);A.keypoints=l.map(y=>[L0[0]*(y[0]+t.boxRaw[0]),L0[1]*(y[1]+t.boxRaw[1]),y[2]||0]),A.landmarks=xt(A.keypoints);for(let y of Object.keys(yn))A.annotations[y]=yn[y].map(c=>A.landmarks&&A.keypoints[c]?A.keypoints[c]:null)}Object.keys(n).forEach(x=>r.dispose(n[x]))}return A}async function z5(e,t){var n,s;if(!$[0]||!$[1]||!((n=$[0])!=null&&n.inputs[0].shape)||!((s=$[1])!=null&&s.inputs[0].shape))return[];L0=[e.shape[2]||0,e.shape[1]||0],ct++;let o=(t.hand.skipTime||0)>P()-k5,A=ct<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?J.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>P()-k5,x=ct<3*(t.hand.skipFrames||0);t.skipAllowed&&J.hands.length===t.hand.maxDetected?J.hands=await Promise.all(J.boxes.map(l=>E5(e,l,t))):t.skipAllowed&&i&&x&&J.hands.length>0?J.hands=await Promise.all(J.boxes.map(l=>E5(e,l,t))):(J.boxes=await m2(e,t),k5=P(),J.hands=await Promise.all(J.boxes.map(l=>E5(e,l,t))),ct=0);let d=[...J.boxes];if(J.boxes.length=0,t.cacheSensitivity>0)for(let l=0;l<J.hands.length;l++){let y=nA(J.hands[l].keypoints,L0);if(y.box[2]/(e.shape[2]||1)>.05&&y.box[3]/(e.shape[1]||1)>.05&&J.hands[l].fingerScore&&J.hands[l].fingerScore>(t.hand.minConfidence||0)){let c=tt(y.box,ln),f=tt(y.boxRaw,ln);J.boxes.push({...d[l],box:c,boxRaw:f})}}for(let l=0;l<J.hands.length;l++){let y=S0(J.hands[l].keypoints,L0);J.hands[l].box=y.box,J.hands[l].boxRaw=y.boxRaw}a(J.hands)})}var r0,ft=[],j5=Number.MAX_SAFE_INTEGER,fn=0,mn=0;async function pn(e){var t;return R.initial&&(r0=null),r0?e.debug&&g("cached model:",r0.modelUrl):r0=await N((t=e.face.liveness)==null?void 0:t.modelPath),r0}async function S5(e,t,o,A){var a,i;if(!r0)return 0;let n=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>P()-mn,s=j5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&n&&s&&fn===A&&ft[o]?(j5++,ft[o]):(j5=0,new Promise(async x=>{let d=r.image.resizeBilinear(e,[r0!=null&&r0.inputs[0].shape?r0.inputs[0].shape[2]:0,r0!=null&&r0.inputs[0].shape?r0.inputs[0].shape[1]:0],!1),l=r0==null?void 0:r0.execute(d),y=(await l.data())[0];ft[o]=Math.round(100*y)/100,fn=A,mn=P(),r.dispose([d,l]),x(ft[o])}))}var Le={};Ze(Le,{connected:()=>pt,horizontal:()=>W5,kpt:()=>mt,relative:()=>I5,vertical:()=>C5});var mt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],W5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],C5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],I5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],pt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var hn=.005,d0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function O5(e){for(let t of W5){let o=e.keypoints.findIndex(n=>n.part===t[0]),A=e.keypoints.findIndex(n=>n.part===t[1]);if(e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[0]<e.keypoints[A].position[0]){let n=e.keypoints[o];e.keypoints[o]=e.keypoints[A],e.keypoints[A]=n}}for(let t of C5){let o=e.keypoints.findIndex(n=>n&&n.part===t[0]),A=e.keypoints.findIndex(n=>n&&n.part===t[1]);e.keypoints[o]&&e.keypoints[A]&&e.keypoints[o].position[1]<e.keypoints[A].position[1]&&e.keypoints.splice(o,1)}for(let[t,o]of I5){let A=e.keypoints.findIndex(d=>d&&d.part===t[0]),n=e.keypoints.findIndex(d=>d&&d.part===t[1]),s=e.keypoints.findIndex(d=>d&&d.part===o[0]),a=e.keypoints.findIndex(d=>d&&d.part===o[1]);if(!e.keypoints[s]||!e.keypoints[a])continue;let i=e.keypoints[A]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[A].position[0])]:[0,0],x=e.keypoints[n]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0])]:[0,0];if(i[0]>i[1]||x[0]>x[1]){let d=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=d}}}function bn(e){for(let t=0;t<e.length;t++)if(e[t]&&d0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-d0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-d0.keypoints[t].positionRaw[1])];o[0]<hn&&o[1]<hn?e[t]=d0.keypoints[t]:d0.keypoints[t]=e[t]}else d0.keypoints[t]=e[t];return e}function gn(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;d0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=r.pad(e,d0.padding),o.resize=r.image.resizeBilinear(o.pad,[t,t]);let A=r.cast(o.resize,"int32");return Object.keys(o).forEach(n=>r.dispose(o[n])),A}function Pn(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+d0.padding[2][0]+d0.padding[2][1])/t[0]-d0.padding[2][0],A.position[1]*(t[1]+d0.padding[1][0]+d0.padding[1][1])/t[1]-d0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=S0(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var f0,ut=0,N5=Number.MAX_SAFE_INTEGER,Ae={boxes:[],bodies:[],last:0};async function Mn(e){return R.initial&&(f0=null),f0?e.debug&&g("cached model:",f0.modelUrl):(dt(["size"],e),f0=await N(e.body.modelPath)),ut=f0.inputs[0].shape?f0.inputs[0].shape[2]:0,ut<64&&(ut=256),f0}async function u2(e,t,o){let A=e[0][0],n=[],s=0;for(let l=0;l<A.length;l++)if(s=A[l][2],s>t.body.minConfidence){let y=[A[l][1],A[l][0]];n.push({score:Math.round(100*s)/100,part:mt[l],positionRaw:y,position:[Math.round((o.shape[2]||0)*y[0]),Math.round((o.shape[1]||0)*y[1])]})}s=n.reduce((l,y)=>y.score>l?y.score:l,0);let a=[],i=S0(n.map(l=>l.position),[o.shape[2],o.shape[1]]),x={};for(let[l,y]of Object.entries(pt)){let c=[];for(let f=0;f<y.length-1;f++){let h=n.find(v=>v.part===y[f]),m=n.find(v=>v.part===y[f+1]);h&&m&&h.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&c.push([h.position,m.position])}x[l]=c}let d={id:0,score:s,box:i.box,boxRaw:i.boxRaw,keypoints:n,annotations:x};return O5(d),a.push(d),a}async function h2(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let s=e[0][n],a=Math.round(100*s[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let y=0;y<17;y++){let c=s[3*y+2];if(c>t.body.minConfidence){let f=[s[3*y+1],s[3*y+0]];i.push({part:mt[y],score:Math.round(100*c)/100,positionRaw:f,position:[Math.round((o.shape[2]||0)*f[0]),Math.round((o.shape[1]||0)*f[1])]})}}let x=S0(i.map(y=>y.position),[o.shape[2],o.shape[1]]),d={};for(let[y,c]of Object.entries(pt)){let f=[];for(let h=0;h<c.length-1;h++){let m=i.find(b=>b.part===c[h]),v=i.find(b=>b.part===c[h+1]);m&&v&&m.score>(t.body.minConfidence||0)&&v.score>(t.body.minConfidence||0)&&f.push([m.position,v.position])}d[y]=f}let l={id:n,score:a,box:x.box,boxRaw:x.boxRaw,keypoints:[...i],annotations:d};O5(l),A.push(l)}}return A.sort((n,s)=>s.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function L5(e,t){if(!f0||!(f0!=null&&f0.inputs[0].shape))return[];t.skipAllowed||(Ae.boxes.length=0),N5++;let o=(t.body.skipTime||0)>P()-Ae.last,A=N5<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ae.bodies:new Promise(async n=>{let s={};N5=0,s.input=gn(e,ut),s.res=f0==null?void 0:f0.execute(s.input),Ae.last=P();let a=await s.res.array();Ae.bodies=s.res.shape[2]===17?await u2(a,t,e):await h2(a,t,e);for(let i of Ae.bodies)Pn(i,[e.shape[2]||1,e.shape[1]||1]),bn(i.keypoints);Object.keys(s).forEach(i=>r.dispose(s[i])),n(Ae.bodies)})}var ge,ht=[],Tn=0,B5=Number.MAX_SAFE_INTEGER,gt=0,bt=2.5;async function Rn(e){if(!ge||R.initial){ge=await N(e.object.modelPath);let t=Object.values(ge.modelSignature.inputs);gt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&g("cached model:",ge.modelUrl);return ge}async function b2(e,t,o){let A=0,n=[];for(let x of[1,2,4])r.tidy(async()=>{let d=x*13,l=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)===xe.length)),y=r.squeeze(e.find(m=>m.shape[1]===d**2&&(m.shape[2]||0)<xe.length)),f=await y.reshape([-1,4,y.shape[1]/4]).argMax(2).array(),h=await l.array();for(let m=0;m<l.shape[0];m++)for(let v=0;v<l.shape[1];v++){let b=h[m][v];if(b>(o.object.minConfidence||0)&&v!==61){let M=(.5+Math.trunc(m%d))/d,p=(.5+Math.trunc(m/d))/d,u=f[m].map(H=>H*(d/x/gt)),[E,W]=[M-bt/x*u[0],p-bt/x*u[1]],[C,S]=[M+bt/x*u[2]-E,p+bt/x*u[3]-W],O=[E,W,C,S];O=O.map(H=>Math.max(0,Math.min(H,1)));let V=[O[0]*t[0],O[1]*t[1],O[2]*t[0],O[3]*t[1]],B={id:A++,score:Math.round(100*b)/100,class:v+1,label:xe[v].label,box:V.map(H=>Math.trunc(H)),boxRaw:O};n.push(B)}}});e.forEach(x=>r.dispose(x));let s=n.map(x=>[x.boxRaw[1],x.boxRaw[0],x.boxRaw[3],x.boxRaw[2]]),a=n.map(x=>x.score),i=[];if(s&&s.length>0){let x=await r.image.nonMaxSuppressionAsync(s,a,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);i=await x.data(),r.dispose(x)}return n=n.filter((x,d)=>i.includes(d)).sort((x,d)=>d.score-x.score),n}async function G5(e,t){let o=(t.object.skipTime||0)>P()-Tn,A=B5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&ht.length>0?(B5++,ht):(B5=0,!R.kernels.includes("mod")||!R.kernels.includes("sparsetodense")?ht:new Promise(async n=>{let s=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[gt,gt],!1),i=r.div(a,L.tf255),x=i.transpose([0,3,1,2]);r.dispose(i),r.dispose(a);let d;t.object.enabled&&(d=ge.execute(x)),Tn=P(),r.dispose(x);let l=await b2(d,s,t);ht=l,n(l)}))}var Ge=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],g2=Ge.length,Be=Ge.reduce((e,t,o)=>(e[t]=o,e),{}),P2=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Ha=P2.map(([e,t])=>[Be[e],Be[t]]),kn=[["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"]];function En(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:s},{position:{x:a,y:i}})=>({maxX:Math.max(o,a),maxY:Math.max(A,i),minX:Math.min(n,a),minY:Math.min(s,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function zn(e,[t,o],[A,n]){let s=t/A,a=o/n,i=(d,l)=>({id:l,score:d.score,boxRaw:[d.box[0]/n,d.box[1]/A,d.box[2]/n,d.box[3]/A],box:[Math.trunc(d.box[0]*a),Math.trunc(d.box[1]*s),Math.trunc(d.box[2]*a),Math.trunc(d.box[3]*s)],keypoints:d.keypoints.map(({score:y,part:c,position:f})=>({score:y,part:c,position:[Math.trunc(f.x*a),Math.trunc(f.y*s)],positionRaw:[f.x/A,f.y/A]})),annotations:{}});return e.map((d,l)=>i(d,l))}var H5=class{constructor(t,o){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new Array(t),this.numberOfElements=-1,this.getElementValue=o}enqueue(t){this.priorityQueue[++this.numberOfElements]=t,this.swim(this.numberOfElements)}dequeue(){let t=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,t}empty(){return this.numberOfElements===-1}size(){return this.numberOfElements+1}all(){return this.priorityQueue.slice(0,this.numberOfElements+1)}max(){return this.priorityQueue[0]}swim(t){for(;t>0&&this.less(Math.floor(t/2),t);)this.exchange(t,Math.floor(t/2)),t=Math.floor(t/2)}sink(t){for(;2*t<=this.numberOfElements;){let o=2*t;if(o<this.numberOfElements&&this.less(o,o+1)&&o++,!this.less(t,o))break;this.exchange(t,o),t=o}}getValueAt(t){return this.getElementValue(this.priorityQueue[t])}less(t,o){return this.getValueAt(t)<this.getValueAt(o)}exchange(t,o){let A=this.priorityQueue[t];this.priorityQueue[t]=this.priorityQueue[o],this.priorityQueue[o]=A}};function F5(e,t,o,A){return{y:A.get(e,t,o),x:A.get(e,t,o+g2)}}function V5(e,t,o){let{heatmapY:A,heatmapX:n,id:s}=e,{y:a,x:i}=F5(A,n,s,o);return{x:e.heatmapX*t+i,y:e.heatmapY*t+a}}function Z5(e,t,o){return e<t?t:e>o?o:e}function jn(e,t,o,A){let n=o-e,s=A-t;return n*n+s*s}function X5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var v0,v2=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],Pt=1,Pe=16,T2=50**2;function Sn(e,t,o,A,n,s,a=2){let i=b=>({y:s.get(b.y,b.x,e),x:s.get(b.y,b.x,s.shape[2]/2+e)}),x=(b,M,p)=>({y:Z5(Math.round(b.y/Pe),0,M-1),x:Z5(Math.round(b.x/Pe),0,p-1)}),[d,l]=A.shape,y=x(t.position,d,l),c=i(y),h=X5(t.position,c);for(let b=0;b<a;b++){let M=x(h,d,l),p=F5(M.y,M.x,o,n);h=X5({x:M.x*Pe,y:M.y*Pe},{x:p.x,y:p.y})}let m=x(h,d,l),v=A.get(m.y,m.x,o);return{position:h,part:Ge[o],score:v}}function R2(e,t,o,A,n){let 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y=Math.abs(l.annotations.leftEyeIris[3][0]-l.annotations.leftEyeIris[1][0])/2,c=Math.abs(l.annotations.leftEyeIris[4][1]-l.annotations.leftEyeIris[2][1])/2;n.ellipse(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1],y,c,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(l.annotations&&l.annotations.rightEyeIris&&l.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let y=Math.abs(l.annotations.rightEyeIris[3][0]-l.annotations.rightEyeIris[1][0])/2,c=Math.abs(l.annotations.rightEyeIris[4][1]-l.annotations.rightEyeIris[2][1])/2;n.ellipse(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1],y,c,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((s=l.rotation)==null?void 0:s.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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             M ${l.box[0]+l.box[2]/2} ${l.box[1]}
             C
               ${y} ${l.box[1]},
@@ -108,7 +108,7 @@ var kt=Object.defineProperty;var rr=Object.getOwnPropertyDescriptor;var sr=Objec
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A=o0(B0,o);if(!t||!e)return;let n=ne(e);if(!!n){n.lineJoin="round";for(let a=0;a<t.length;a++){if(n.strokeStyle=A.color,n.fillStyle=A.color,n.lineWidth=A.lineWidth,n.font=A.font,A.drawBoxes&&t[a].box&&((s=t[a].box)==null?void 0:s.length)===4&&(He(n,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],A),A.drawLabels&&(A.shadowColor&&A.shadowColor!==""&&(n.fillStyle=A.shadowColor,n.fillText(`body ${100*t[a].score}%`,t[a].box[0]+3,1+t[a].box[1]+A.lineHeight,t[a].box[2])),n.fillStyle=A.labelColor,n.fillText(`body ${100*t[a].score}%`,t[a].box[0]+2,0+t[a].box[1]+A.lineHeight,t[a].box[2]))),A.drawPoints&&t[a].keypoints)for(let i=0;i<t[a].keypoints.length;i++)!t[a].keypoints[i].score||t[a].keypoints[i].score===0||(n.fillStyle=A.useDepth&&t[a].keypoints[i].position[2]?`rgba(${127.5+2*(t[a].keypoints[i].position[2]||0)}, ${127.5-2*(t[a].keypoints[i].position[2]||0)}, 255, 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SSRNet:"),e.config.async?(A=((W=e.config.face.ssrnet)==null?void 0:W.enabled)?Lt(c[k].tensor||r.tensor([]),e.config,k,c.length):null,s=((C=e.config.face.ssrnet)==null?void 0:C.enabled)?Ht(c[k].tensor||r.tensor([]),e.config,k,c.length):null):(e.state="run:ssrnet",o=P(),A=((S=e.config.face.ssrnet)==null?void 0:S.enabled)?await Lt(c[k].tensor||r.tensor([]),e.config,k,c.length):null,s=((O=e.config.face.ssrnet)==null?void 0:O.enabled)?await Ht(c[k].tensor||r.tensor([]),e.config,k,c.length):null,e.performance.ssrnet=Math.trunc(P()-o)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=((V=e.config.face.mobilefacenet)==null?void 0:V.enabled)?x5(c[k].tensor||r.tensor([]),e.config,k,c.length):null:(e.state="run:mobilefacenet",o=P(),i=((B=e.config.face.mobilefacenet)==null?void 0:B.enabled)?await x5(c[k].tensor||r.tensor([]),e.config,k,c.length):null,e.performance.mobilefacenet=Math.trunc(P()-o)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?l=((H=e.config.face.description)==null?void 0:H.enabled)?u5(c[k].tensor||r.tensor([]),e.config,k,c.length):null:(e.state="run:description",o=P(),l=((t0=e.config.face.description)==null?void 0:t0.enabled)?await u5(c[k].tensor||r.tensor([]),e.config,k,c.length):null,e.performance.description=R.perfadd?(e.performance.description||0)+Math.trunc(P()-o):Math.trunc(P()-o)),e.analyze("End Description:"),e.config.async&&([A,s,a,i,l,n,x,d]=await Promise.all([A,s,a,i,l,n,x,d])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&A&&s&&(l={...l,age:A.age,gender:s.gender,genderScore:s.genderScore}),((m0=e.config.face.gear)==null?void 0:m0.enabled)&&n&&(l={...l,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((G0=e.config.face.mobilefacenet)==null?void 0:G0.enabled)&&i&&(l.descriptor=i),!((T=e.config.face.iris)==null?void 0:T.enabled);let Y=c[k].annotations&&c[k].annotations.leftEyeIris&&c[k].annotations.leftEyeIris[0]&&c[k].annotations.rightEyeIris&&c[k].annotations.rightEyeIris[0]&&c[k].annotations.leftEyeIris.length>0&&c[k].annotations.rightEyeIris.length>0&&c[k].annotations.leftEyeIris[0]!==null&&c[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[k].annotations.leftEyeIris[3][0]-c[k].annotations.leftEyeIris[1][0]),Math.abs(c[k].annotations.rightEyeIris[4][1]-c[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,e0=((Q=e.config.face.detector)==null?void 0:Q.return)?r.squeeze(c[k].tensor):null;r.dispose(c[k].tensor),c[k].tensor&&delete c[k].tensor;let I={...c[k],id:k};(l==null?void 0:l.age)&&(I.age=l.age),(l==null?void 0:l.gender)&&(I.gender=l.gender),(l==null?void 0:l.genderScore)&&(I.genderScore=l==null?void 0:l.genderScore),(l==null?void 0:l.descriptor)&&(I.embedding=l==null?void 0:l.descriptor),(l==null?void 0:l.race)&&(I.race=l==null?void 0:l.race),a&&(I.emotion=a),x&&(I.real=x),d&&(I.live=d),Y&&Y!==0&&(I.iris=Math.trunc(500/Y/11.7)/100),X&&(I.rotation=X),e0&&(I.tensor=e0),y.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var Yn=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(x=>x.part==="leftWrist"),n=e[o].keypoints.find(x=>x.part==="rightWrist"),s=e[o].keypoints.find(x=>x.part==="nose");s&&A&&n&&A.position[1]<s.position[1]&&n.position[1]<s.position[1]?t.push({body:o,gesture:"i give up"}):s&&A&&A.position[1]<s.position[1]?t.push({body:o,gesture:"raise left hand"}):s&&n&&n.position[1]<s.position[1]&&t.push({body:o,gesture:"raise right hand"});let a=e[o].keypoints.find(x=>x.part==="leftShoulder"),i=e[o].keypoints.find(x=>x.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},Kn=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let x=e[o].mesh[152][2]||0;Math.abs(x)>10&&t.push({face:o,gesture:`head ${x<0?"up":"down"}`})}return t},Qn=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],s=Math.abs(A*n),a=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],i=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],x=Math.abs(a*i),d=!1;Math.abs(s-x)/Math.max(s,x)<.25&&(d=!0,t.push({iris:o,gesture:"facing center"}));let y=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],c=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(y>.06||c>.06)&&(d=!1),y>c?y>.05&&t.push({iris:o,gesture:"looking right"}):c>.05&&t.push({iris:o,gesture:"looking left"});let f=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],h=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(h<.01||f<.01||h>.022||f>.022)&&(d=!1),(h<.01||f<.01)&&t.push({iris:o,gesture:"looking down"}),(h>.022||f>.022)&&t.push({iris:o,gesture:"looking up"}),d&&t.push({iris:o,gesture:"looking center"})}return t},_n=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,s]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(s)&&s[0]&&A.push({name:n.toLowerCase(),position:s[0]});if(A&&A.length>0){let n=A.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:o,gesture:`${n.name} forward`});let s=A.reduce((a,i)=>a.position[1]<i.position[1]?a:i);t.push({hand:o,gesture:`${s.name} up`})}if(e[o].keypoints){let n=An(e[o].keypoints);for(let s of n)t.push({hand:o,gesture:s.name})}}return t};var j={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},ro=0;function $n(e,t){var a,i,x,d,l,y,c,f,h,m,v,b,M,p,u,E,W,C,S,O,V,B,H,t0,z,m0,G0;let o=P();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let A=Date.now()-e.timestamp,n=A<1e3?8-Math.log(A+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let T=0;T<e.body.length;T++){let Q=e.body[T].box.map((I,F)=>((n-1)*j.body[T].box[F]+I)/n),k=e.body[T].boxRaw.map((I,F)=>((n-1)*j.body[T].boxRaw[F]+I)/n),X=e.body[T].keypoints.map((I,F)=>{var k0,E0,Re,we,re,so,ao,io,lo;return{score:I.score,part:I.part,position:[j.body[T].keypoints[F]?((n-1)*(j.body[T].keypoints[F].position[0]||0)+(I.position[0]||0))/n:I.position[0],j.body[T].keypoints[F]?((n-1)*(j.body[T].keypoints[F].position[1]||0)+(I.position[1]||0))/n:I.position[1],j.body[T].keypoints[F]?((n-1)*(j.body[T].keypoints[F].position[2]||0)+(I.position[2]||0))/n:I.position[2]],positionRaw:[j.body[T].keypoints[F]?((n-1)*(j.body[T].keypoints[F].positionRaw[0]||0)+(I.positionRaw[0]||0))/n:I.positionRaw[0],j.body[T].keypoints[F]?((n-1)*(j.body[T].keypoints[F].positionRaw[1]||0)+(I.positionRaw[1]||0))/n:I.positionRaw[1],j.body[T].keypoints[F]?((n-1)*(j.body[T].keypoints[F].positionRaw[2]||0)+(I.positionRaw[2]||0))/n:I.positionRaw[2]],distance:[j.body[T].keypoints[F]?((n-1)*(((k0=j.body[T].keypoints[F].distance)==null?void 0:k0[0])||0)+(((E0=I.distance)==null?void 0:E0[0])||0))/n:(Re=I.distance)==null?void 0:Re[0],j.body[T].keypoints[F]?((n-1)*(((we=j.body[T].keypoints[F].distance)==null?void 0:we[1])||0)+(((re=I.distance)==null?void 0:re[1])||0))/n:(so=I.distance)==null?void 0:so[1],j.body[T].keypoints[F]?((n-1)*(((ao=j.body[T].keypoints[F].distance)==null?void 0:ao[2])||0)+(((io=I.distance)==null?void 0:io[2])||0))/n:(lo=I.distance)==null?void 0:lo[2]]}}),Y={},e0={connected:{}};((i=(a=t.body)==null?void 0:a.modelPath)==null?void 0:i.includes("efficientpose"))?e0=nt:((d=(x=t.body)==null?void 0:x.modelPath)==null?void 0:d.includes("blazepose"))?e0=$e:((y=(l=t.body)==null?void 0:l.modelPath)==null?void 0:y.includes("movenet"))&&(e0=Le);for(let[I,F]of Object.entries(e0.connected)){let k0=[];for(let E0=0;E0<F.length-1;E0++){let Re=X.find(re=>re.part===F[E0]),we=X.find(re=>re.part===F[E0+1]);Re&&we&&k0.push([Re.position,we.position])}Y[I]=k0}j.body[T]={...e.body[T],box:Q,boxRaw:k,keypoints:X,annotations:Y}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let T=0;T<e.hand.length;T++){let Q=e.hand[T].box.map((e0,I)=>((n-1)*j.hand[T].box[I]+e0)/n),k=e.hand[T].boxRaw.map((e0,I)=>((n-1)*j.hand[T].boxRaw[I]+e0)/n);j.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(j.hand[T].keypoints=e.hand[T].keypoints);let X=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((e0,I)=>e0.map((F,k0)=>((n-1)*(j.hand[T].keypoints[I][k0]||1)+(F||0))/n)):[],Y={};if(Object.keys(j.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)j.hand[T].annotations=e.hand[T].annotations,Y=j.hand[T].annotations;else if(e.hand[T].annotations)for(let e0 of Object.keys(e.hand[T].annotations))Y[e0]=e.hand[T].annotations[e0]&&e.hand[T].annotations[e0][0]?e.hand[T].annotations[e0].map((I,F)=>I.map((k0,E0)=>((n-1)*j.hand[T].annotations[e0][F][E0]+k0)/n)):null;j.hand[T]={...e.hand[T],box:Q,boxRaw:k,keypoints:X,annotations:Y}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let Q=e.face[T].box.map((X,Y)=>((n-1)*j.face[T].box[Y]+X)/n),k=e.face[T].boxRaw.map((X,Y)=>((n-1)*j.face[T].boxRaw[Y]+X)/n);if(e.face[T].rotation){let X={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};X.matrix=(c=e.face[T].rotation)==null?void 0:c.matrix,X.angle={roll:((n-1)*(((h=(f=j.face[T].rotation)==null?void 0:f.angle)==null?void 0:h.roll)||0)+(((v=(m=e.face[T].rotation)==null?void 0:m.angle)==null?void 0:v.roll)||0))/n,yaw:((n-1)*(((M=(b=j.face[T].rotation)==null?void 0:b.angle)==null?void 0:M.yaw)||0)+(((u=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:u.yaw)||0))/n,pitch:((n-1)*(((W=(E=j.face[T].rotation)==null?void 0:E.angle)==null?void 0:W.pitch)||0)+(((S=(C=e.face[T].rotation)==null?void 0:C.angle)==null?void 0:S.pitch)||0))/n},X.gaze={bearing:((n-1)*(((V=(O=j.face[T].rotation)==null?void 0:O.gaze)==null?void 0:V.bearing)||0)+(((H=(B=e.face[T].rotation)==null?void 0:B.gaze)==null?void 0:H.bearing)||0))/n,strength:((n-1)*(((z=(t0=j.face[T].rotation)==null?void 0:t0.gaze)==null?void 0:z.strength)||0)+(((G0=(m0=e.face[T].rotation)==null?void 0:m0.gaze)==null?void 0:G0.strength)||0))/n},j.face[T]={...e.face[T],rotation:X,box:Q,boxRaw:k}}j.face[T]={...e.face[T],box:Q,boxRaw:k}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let T=0;T<e.object.length;T++){let Q=e.object[T].box.map((X,Y)=>((n-1)*j.object[T].box[Y]+X)/n),k=e.object[T].boxRaw.map((X,Y)=>((n-1)*j.object[T].boxRaw[Y]+X)/n);j.object[T]={...e.object[T],box:Q,boxRaw:k}}if(e.persons){let T=e.persons;if(!j.persons||T.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(T));else for(let Q=0;Q<T.length;Q++)j.persons[Q].box=T[Q].box.map((k,X)=>((n-1)*j.persons[Q].box[X]+k)/n)}e.gesture&&(j.gesture=e.gesture);let s=P();return ro=R.perfadd?ro+Math.round(s-o):Math.round(s-o),e.performance&&(j.performance={...e.performance,interpolate:ro}),j}function vt(e,t,o={order:2,multiplier:25}){let A=0;for(let n=0;n<e.length;n++){let s=!o.order||o.order===2?e[n]-t[n]:Math.abs(e[n]-t[n]);A+=!o.order||o.order===2?s*s:s**o.order}return(o.multiplier||20)*A}var er=(e,t,o,A)=>{if(e===0)return 1;let n=t===2?Math.sqrt(e):e**(1/t),s=(1-n/100-o)/(A-o);return Math.max(Math.min(s,1),0)};function tr(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let A=vt(e,t,o);return er(A,o.order||2,o.min||0,o.max||1)}function or(e,t,o={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let A=Number.MAX_SAFE_INTEGER,n=-1;for(let a=0;a<t.length;a++){let i=vt(e,t[a],o);if(i<A&&(A=i,n=a),A<(o.threshold||0))break}let s=er(A,o.order||2,o.min||0,o.max||1);return{index:n,distance:A,similarity:s}}function Ar(e,t,o,A,n){var i,x,d,l,y,c,f,h,m,v,b,M,p,u,E,W;let s=0,a=[];for(let C of e){let S={id:s++,face:C,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)C.box[0]>z.box[0]&&C.box[0]<z.box[0]+z.box[2]&&C.box[1]+C.box[3]>z.box[1]&&C.box[1]+C.box[3]<z.box[1]+z.box[3]&&(S.body=z);if(S.body)for(let z of o)z.box[0]+z.box[2]>S.body.box[0]&&z.box[0]+z.box[2]<S.body.box[0]+S.body.box[2]&&z.box[1]+z.box[3]>S.body.box[1]&&z.box[1]+z.box[3]<S.body.box[1]+S.body.box[3]&&S.hands&&(S.hands.left=z),z.box[0]<S.body.box[0]+S.body.box[2]&&z.box[0]>S.body.box[0]&&z.box[1]+z.box[3]>S.body.box[1]&&z.box[1]+z.box[3]<S.body.box[1]+S.body.box[3]&&S.hands&&(S.hands.right=z);for(let z of A)z.face!==void 0&&z.face===C.id?(i=S.gestures)==null||i.push(z):z.iris!==void 0&&z.iris===C.id?(x=S.gestures)==null||x.push(z):z.body!==void 0&&z.body===((d=S.body)==null?void 0:d.id)?(l=S.gestures)==null||l.push(z):z.hand!==void 0&&z.hand===((c=(y=S.hands)==null?void 0:y.left)==null?void 0:c.id)?(f=S.gestures)==null||f.push(z):z.hand!==void 0&&z.hand===((m=(h=S.hands)==null?void 0:h.right)==null?void 0:m.id)&&((v=S.gestures)==null||v.push(z));let O=[],V=[],B=z=>{z&&z.length===4&&(O.push(z[0],z[0]+z[2]),V.push(z[1],z[1]+z[3]))};B((b=S.face)==null?void 0:b.box),B((M=S.body)==null?void 0:M.box),B((u=(p=S.hands)==null?void 0:p.left)==null?void 0:u.box),B((W=(E=S.hands)==null?void 0:E.right)==null?void 0:W.box);let H=Math.min(...O),t0=Math.min(...V);S.box=[H,t0,Math.max(...O)-H,Math.max(...V)-t0],n&&n[1]&&n[2]&&(S.boxRaw=[S.box[0]/n[2],S.box[1]/n[1],S.box[2]/n[2],S.box[3]/n[1]]),a.push(S)}return a}var Tt=`
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A=o0(B0,o);if(!t||!e)return;let n=ne(e);if(!!n){n.lineJoin="round";for(let a=0;a<t.length;a++){if(n.strokeStyle=A.color,n.fillStyle=A.color,n.lineWidth=A.lineWidth,n.font=A.font,A.drawBoxes&&t[a].box&&((s=t[a].box)==null?void 0:s.length)===4&&(He(n,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],A),A.drawLabels&&(A.shadowColor&&A.shadowColor!==""&&(n.fillStyle=A.shadowColor,n.fillText(`body ${100*t[a].score}%`,t[a].box[0]+3,1+t[a].box[1]+A.lineHeight,t[a].box[2])),n.fillStyle=A.labelColor,n.fillText(`body ${100*t[a].score}%`,t[a].box[0]+2,0+t[a].box[1]+A.lineHeight,t[a].box[2]))),A.drawPoints&&t[a].keypoints)for(let i=0;i<t[a].keypoints.length;i++)!t[a].keypoints[i].score||t[a].keypoints[i].score===0||(n.fillStyle=A.useDepth&&t[a].keypoints[i].position[2]?`rgba(${127.5+2*(t[a].keypoints[i].position[2]||0)}, ${127.5-2*(t[a].keypoints[i].position[2]||0)}, 255, 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Y=c[k].annotations&&c[k].annotations.leftEyeIris&&c[k].annotations.leftEyeIris[0]&&c[k].annotations.rightEyeIris&&c[k].annotations.rightEyeIris[0]&&c[k].annotations.leftEyeIris.length>0&&c[k].annotations.rightEyeIris.length>0&&c[k].annotations.leftEyeIris[0]!==null&&c[k].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(c[k].annotations.leftEyeIris[3][0]-c[k].annotations.leftEyeIris[1][0]),Math.abs(c[k].annotations.rightEyeIris[4][1]-c[k].annotations.rightEyeIris[2][1]))/t.shape[2]:0,e0=(Q=e.config.face.detector)!=null&&Q.return?r.squeeze(c[k].tensor):null;r.dispose(c[k].tensor),c[k].tensor&&delete c[k].tensor;let I={...c[k],id:k};l!=null&&l.age&&(I.age=l.age),l!=null&&l.gender&&(I.gender=l.gender),l!=null&&l.genderScore&&(I.genderScore=l==null?void 0:l.genderScore),l!=null&&l.descriptor&&(I.embedding=l==null?void 0:l.descriptor),l!=null&&l.race&&(I.race=l==null?void 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a=e[o].keypoints.find(x=>x.part==="leftShoulder"),i=e[o].keypoints.find(x=>x.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},Kn=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));i>10&&t.push({face:o,gesture:`mouth ${Math.trunc(i)}% open`});let x=e[o].mesh[152][2]||0;Math.abs(x)>10&&t.push({face:o,gesture:`head ${x<0?"up":"down"}`})}return t},Qn=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],s=Math.abs(A*n),a=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],i=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],x=Math.abs(a*i),d=!1;Math.abs(s-x)/Math.max(s,x)<.25&&(d=!0,t.push({iris:o,gesture:"facing center"}));let y=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],c=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(y>.06||c>.06)&&(d=!1),y>c?y>.05&&t.push({iris:o,gesture:"looking 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j={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},ro=0;function $n(e,t){var a,i,x,d,l,y,c,f,h,m,v,b,M,p,u,E,W,C,S,O,V,B,H,t0,z,m0,G0;let o=P();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let A=Date.now()-e.timestamp,n=A<1e3?8-Math.log(A+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let T=0;T<e.body.length;T++){let Q=e.body[T].box.map((I,F)=>((n-1)*j.body[T].box[F]+I)/n),k=e.body[T].boxRaw.map((I,F)=>((n-1)*j.body[T].boxRaw[F]+I)/n),X=e.body[T].keypoints.map((I,F)=>{var 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0);Ee(this,Ve,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!ke(this,Fe))return;let o=this.tf.engine().state.numTensors,A=ke(this,Te);ze(this,Te,o);let n=o-A;n!==0&&g(...t,n)});Ee(this,wt,t=>{if(!ke(this,Ve))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof se))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",tr);w(this,"distance",vt);w(this,"match",or);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new 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J5,this.draw={options:B0,canvas:(o,A)=>Dn(o,A),face:(o,A,n)=>$5(o,A,n),body:(o,A,n)=>eo(o,A,n),hand:(o,A,n)=>to(o,A,n),gesture:(o,A,n)=>_5(o,A,n),object:(o,A,n)=>oo(o,A,n),person:(o,A,n)=>Xn(o,A,n),all:(o,A,n)=>qn(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=IA,this.faceUVMap=OA,this.gl=q,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Y0)),this.config.backend=t}validate(t){return Et(Y0,t||this.config)}now(){return P()}image(t,o=!0){return ie(t,this.config,o)}async segmentation(t,o){return On(t,o,this.config)}enhance(t){return p5(t)}compare(t,o){return Mo(this.config,t,o)}async init(){await Mt(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=P(),A=Object.values(this.models).filter(a=>a).length;t&&(this.config=o0(this.config,t)),this.env.initial&&(this.config.debug&&g(`version: 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A.kernels)n[i.name]?n[i.name]+=i.kernelTimeMs:n[i.name]=i.kernelTimeMs;let s=[];Object.entries(n).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,x)=>x.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async A=>{var v,b,M,p,u,E,W,C,S,O,V,B,H,t0,z,m0,G0,T,Q,k,X,Y;this.state="config";let n;this.config=o0(this.config,o),this.state="check";let s=ke(this,wt).call(this,t);s&&(g(s,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:P(),persons:[],error:s}));let a=P();await Mt(this),await this.load(),n=P(),this.state="image";let i=await ie(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(P()-n):Math.trunc(P()-n),this.analyze("Get Image:"),!i.tensor){this.config.debug&&g("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:P(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=P(),this.config.skipAllowed=await Po(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(P()-n):Math.trunc(P()-n),this.analyze("Check Changed:");let x=[],d=[],l=[],y=[];this.state="detect:face",this.config.async?(x=this.config.face.enabled?no(this,i.tensor):[],this.performance.face&&delete this.performance.face):(n=P(),x=this.config.face.enabled?await no(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(x=await x),this.analyze("Start Body:"),this.state="detect:body";let c=this.config.body.maxDetected===-1?o0(this.config,{body:{maxDetected:this.config.face.enabled?1*x.length:1}}):this.config;this.config.async?(((v=this.config.body.modelPath)==null?void 0:v.includes("posenet"))?d=this.config.body.enabled?D5(i.tensor,c):[]:((b=this.config.body.modelPath)==null?void 0:b.includes("blazepose"))?d=this.config.body.enabled?e5(i.tensor,c):[]:((M=this.config.body.modelPath)==null?void 0:M.includes("efficientpose"))?d=this.config.body.enabled?a5(i.tensor,c):[]:((p=this.config.body.modelPath)==null?void 0:p.includes("movenet"))&&(d=this.config.body.enabled?L5(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(n=P(),((u=this.config.body.modelPath)==null?void 0:u.includes("posenet"))?d=this.config.body.enabled?await D5(i.tensor,c):[]:((E=this.config.body.modelPath)==null?void 0:E.includes("blazepose"))?d=this.config.body.enabled?await e5(i.tensor,c):[]:((W=this.config.body.modelPath)==null?void 0:W.includes("efficientpose"))?d=this.config.body.enabled?await a5(i.tensor,c):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(d=this.config.body.enabled?await L5(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?o0(this.config,{hand:{maxDetected:this.config.face.enabled?2*x.length:1}}):this.config;this.config.async?(((O=(S=this.config.hand.detector)==null?void 0:S.modelPath)==null?void 0:O.includes("handdetect"))?l=this.config.hand.enabled?R5(i.tensor,f):[]:((B=(V=this.config.hand.detector)==null?void 0:V.modelPath)==null?void 0:B.includes("handtrack"))&&(l=this.config.hand.enabled?z5(i.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(n=P(),((t0=(H=this.config.hand.detector)==null?void 0:H.modelPath)==null?void 0:t0.includes("handdetect"))?l=this.config.hand.enabled?await R5(i.tensor,f):[]:((m0=(z=this.config.hand.detector)==null?void 0:z.modelPath)==null?void 0:m0.includes("handtrack"))&&(l=this.config.hand.enabled?await z5(i.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((G0=this.config.object.modelPath)==null?void 0:G0.includes("nanodet"))?y=this.config.object.enabled?G5(i.tensor,this.config):[]:((T=this.config.object.modelPath)==null?void 0:T.includes("centernet"))&&(y=this.config.object.enabled?A5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=P(),((Q=this.config.object.modelPath)==null?void 0:Q.includes("nanodet"))?y=this.config.object.enabled?await G5(i.tensor,this.config):[]:((k=this.config.object.modelPath)==null?void 0:k.includes("centernet"))&&(y=this.config.object.enabled?await A5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([x,d,l,y]=await Promise.all([x,d,l,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(n=P(),h=[...Kn(x),...Yn(d),..._n(l),...Qn(x)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(P()-a):Math.trunc(P()-a);let m=((Y=(X=this.process)==null?void 0:X.tensor)==null?void 0:Y.shape)||[];this.result={face:x,body:d,hand:l,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Ar(x,d,l,h,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Te=new WeakMap,Fe=new WeakMap,Ve=new WeakMap,wt=new WeakMap;export{J2 as Human,J2 as default,Y0 as defaults,R as env};
+2Q==`;async function X2(e){let t=(n,s="application/octet-stream")=>fetch(`data:${s};base64,${n}`).then(a=>a.blob()),o,A;switch(e.config.warmup){case"face":o=await t(Tt);break;case"body":case"full":o=await t(Rt);break;default:o=null}if(o){let n=await createImageBitmap(o);A=await e.detect(n,e.config),n.close()}return A}async function D2(e){return new Promise(t=>{let o;switch(e.config.warmup){case"face":o="data:image/jpeg;base64,"+Tt;break;case"full":case"body":o="data:image/jpeg;base64,"+Rt;break;default:o=null}let A;if(typeof Image!="undefined")A=new Image;else if(R.Image)A=new R.Image;else return;A.onload=async()=>{let n=s0(A.naturalWidth,A.naturalHeight);if(!n)g("Warmup: Canvas not found"),t(void 0);else{let s=n.getContext("2d");s&&s.drawImage(A,0,0);let a=await e.image(n),i=await e.detect(a.tensor,e.config);t(i)}},o?A.src=o:t(void 0)})}async function q2(e){let t=n=>Buffer.from(n,"base64"),o;e.config.warmup==="face"?o=t(Tt):o=t(Rt);let A;if("node"in r){let n=r.node.decodeJpeg(o),s=n.expandDims(0);e.tf.dispose(n),A=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&g("Warmup tfjs-node not loaded");return A}async function nr(e,t){let o=P();if(e.state="warmup",t&&(e.config=o0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:P(),persons:[],error:null};let A;return new Promise(async n=>{typeof createImageBitmap=="function"?A=await X2(e):typeof Image!="undefined"||R.Canvas!==void 0?A=await D2(e):A=await q2(e);let s=P();e.config.debug&&g("Warmup",e.config.warmup,Math.round(s-o),"ms"),e.emit("warmup"),n(A)})}var Te,Fe,Ve,wt,J2=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env");w(this,"draw");w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");Ee(this,Te,void 0);Ee(this,Fe,void 0);Ee(this,Ve,void 0);w(this,"gl");w(this,"analyze",(...t)=>{if(!ke(this,Fe))return;let o=this.tf.engine().state.numTensors,A=ke(this,Te);ze(this,Te,o);let n=o-A;n!==0&&g(...t,n)});Ee(this,wt,t=>{if(!ke(this,Ve))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof se))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});w(this,"similarity",tr);w(this,"distance",vt);w(this,"match",or);w(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});this.env=R,Y0.wasmPath=je["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${r.version_core}/dist/`,Y0.modelBasePath=R.browser?"../models/":"file://models/",Y0.backend=R.browser?"humangl":"tensorflow",this.version=Wt,Object.defineProperty(this,"version",{value:Wt}),this.config=JSON.parse(JSON.stringify(Y0)),Object.seal(this.config),t&&(this.config=o0(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",To(this.config),this.tf=r,this.state="idle",ze(this,Te,0),ze(this,Fe,!1),ze(this,Ve,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new J5,this.draw={options:B0,canvas:(o,A)=>Dn(o,A),face:(o,A,n)=>$5(o,A,n),body:(o,A,n)=>eo(o,A,n),hand:(o,A,n)=>to(o,A,n),gesture:(o,A,n)=>_5(o,A,n),object:(o,A,n)=>oo(o,A,n),person:(o,A,n)=>Xn(o,A,n),all:(o,A,n)=>qn(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=IA,this.faceUVMap=OA,this.gl=q,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Y0)),this.config.backend=t}validate(t){return Et(Y0,t||this.config)}now(){return P()}image(t,o=!0){return ie(t,this.config,o)}async segmentation(t,o){return On(t,o,this.config)}enhance(t){return p5(t)}compare(t,o){return Mo(this.config,t,o)}async init(){await Mt(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=P(),A=Object.values(this.models).filter(a=>a).length;t&&(this.config=o0(this.config,t)),this.env.initial&&(this.config.debug&&g(`version: ${this.version}`),this.config.debug&&g(`tfjs version: ${this.tf.version["tfjs-core"]}`),await Mt(this)||g("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&g("configuration:",this.config),this.config.debug&&g("environment:",this.env),this.config.debug&&g("tf flags:",this.tf.ENV.flags))),await Ln(this),this.env.initial&&this.config.debug&&g("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(a=>a).length!==A&&(await Bn(this),this.emit("load"));let s=Math.trunc(P()-o);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return $n(t,this.config)}async warmup(t){let o=P(),A=await nr(this,t),n=P();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let i of A.kernels)n[i.name]?n[i.name]+=i.kernelTimeMs:n[i.name]=i.kernelTimeMs;let s=[];Object.entries(n).forEach(i=>s.push({name:i[0],ms:i[1]})),s.sort((i,x)=>x.ms-i.ms),s.length=20;let a={};for(let i of s)a[i.name]=i.ms;return a}async detect(t,o){return this.state="detect",new Promise(async A=>{var v,b,M,p,u,E,W,C,S,O,V,B,H,t0,z,m0,G0,T,Q,k,X,Y;this.state="config";let n;this.config=o0(this.config,o),this.state="check";let s=ke(this,wt).call(this,t);s&&(g(s,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:P(),persons:[],error:s}));let a=P();await Mt(this),await this.load(),n=P(),this.state="image";let i=await ie(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(P()-n):Math.trunc(P()-n),this.analyze("Get Image:"),!i.tensor){this.config.debug&&g("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:P(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=P(),this.config.skipAllowed=await Po(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(P()-n):Math.trunc(P()-n),this.analyze("Check Changed:");let x=[],d=[],l=[],y=[];this.state="detect:face",this.config.async?(x=this.config.face.enabled?no(this,i.tensor):[],this.performance.face&&delete this.performance.face):(n=P(),x=this.config.face.enabled?await no(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(x=await x),this.analyze("Start Body:"),this.state="detect:body";let c=this.config.body.maxDetected===-1?o0(this.config,{body:{maxDetected:this.config.face.enabled?1*x.length:1}}):this.config;this.config.async?((v=this.config.body.modelPath)!=null&&v.includes("posenet")?d=this.config.body.enabled?D5(i.tensor,c):[]:(b=this.config.body.modelPath)!=null&&b.includes("blazepose")?d=this.config.body.enabled?e5(i.tensor,c):[]:(M=this.config.body.modelPath)!=null&&M.includes("efficientpose")?d=this.config.body.enabled?a5(i.tensor,c):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(d=this.config.body.enabled?L5(i.tensor,c):[]),this.performance.body&&delete this.performance.body):(n=P(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?d=this.config.body.enabled?await D5(i.tensor,c):[]:(E=this.config.body.modelPath)!=null&&E.includes("blazepose")?d=this.config.body.enabled?await e5(i.tensor,c):[]:(W=this.config.body.modelPath)!=null&&W.includes("efficientpose")?d=this.config.body.enabled?await a5(i.tensor,c):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(d=this.config.body.enabled?await L5(i.tensor,c):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let f=this.config.hand.maxDetected===-1?o0(this.config,{hand:{maxDetected:this.config.face.enabled?2*x.length:1}}):this.config;this.config.async?((O=(S=this.config.hand.detector)==null?void 0:S.modelPath)!=null&&O.includes("handdetect")?l=this.config.hand.enabled?R5(i.tensor,f):[]:(B=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&B.includes("handtrack")&&(l=this.config.hand.enabled?z5(i.tensor,f):[]),this.performance.hand&&delete this.performance.hand):(n=P(),(t0=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&t0.includes("handdetect")?l=this.config.hand.enabled?await R5(i.tensor,f):[]:(m0=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&m0.includes("handtrack")&&(l=this.config.hand.enabled?await z5(i.tensor,f):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((G0=this.config.object.modelPath)!=null&&G0.includes("nanodet")?y=this.config.object.enabled?G5(i.tensor,this.config):[]:(T=this.config.object.modelPath)!=null&&T.includes("centernet")&&(y=this.config.object.enabled?A5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=P(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?y=this.config.object.enabled?await G5(i.tensor,this.config):[]:(k=this.config.object.modelPath)!=null&&k.includes("centernet")&&(y=this.config.object.enabled?await A5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([x,d,l,y]=await Promise.all([x,d,l,y])),this.state="detect:gesture";let h=[];this.config.gesture.enabled&&(n=P(),h=[...Kn(x),...Yn(d),..._n(l),...Qn(x)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(P()-n):Math.trunc(P()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(P()-a):Math.trunc(P()-a);let m=((Y=(X=this.process)==null?void 0:X.tensor)==null?void 0:Y.shape)||[];this.result={face:x,body:d,hand:l,gesture:h,object:y,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Ar(x,d,l,h,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Te=new WeakMap,Fe=new WeakMap,Ve=new WeakMap,wt=new WeakMap;export{J2 as Human,J2 as default,Y0 as defaults,R as env};
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.esm-nobundle.js.map b/dist/human.esm-nobundle.js.map
index 772d1df7..8d52710e 100644
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/models.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/util/draw.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nexport*from\"@tensorflow/tfjs/dist/index.js\";export*from\"@tensorflow/tfjs-backend-webgl/dist/index.js\";var r=\"3.13.0\",a=\"3.13.0\",e=\"3.13.0\",o=\"3.13.0\",t=\"3.13.0\",s=\"3.13.0\",f=\"3.13.0\",v=\"3.13.0\",j={tfjs:r,\"tfjs-core\":a,\"tfjs-data\":e,\"tfjs-layers\":o,\"tfjs-converter\":t,\"tfjs-backend-cpu\":s,\"tfjs-backend-webgl\":f,\"tfjs-backend-wasm\":v};import{Tensor as b}from\"@tensorflow/tfjs/dist/index.js\";import{GraphModel as i}from\"@tensorflow/tfjs-converter/dist/index\";export{i as GraphModel,b as Tensor,j as version};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        return { tensor: null, canvas: inCanvas };\n      }\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  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0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.2;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const [contours, confidence, contourCoords] = model.execute(face.tensor as Tensor) as Array<Tensor>; // first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      tf.dispose([contourCoords, coordsReshaped, confidence, contours]);\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.iris?.enabled) rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // augment results with iris\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.mesh?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 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0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep, now, log } from './util';\nimport { env } from './env';\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Point } from '../result';\nimport type { AnyCanvas } from '../exports';\n\n/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n\nlet drawTime = 0;\n\nconst getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nconst rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * (pt[2] || 0))}, ${127.5 - (2 * (pt[2] || 0))}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (i * z)}, ${127.5 - (i * z)}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see {@link similarity}\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - {@link distance} calculated `distance` of given descriptor to the best match\n * - {@link similarity} calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human } from './human';\nimport type { Tensor } from './tfjs/types';\nimport { env } from './util/env';\n\nasync function warmupBitmap(instance: Human) {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  let res;\n  return new Promise(async (resolve) => {\n    if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n    else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n    else res = await warmupNode(instance);\n    const t1 = now();\n    if (instance.config.debug) log('Warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './util/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default };\n"],
-  "mappings": 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+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nexport*from\"@tensorflow/tfjs/dist/index.js\";export*from\"@tensorflow/tfjs-backend-webgl/dist/index.js\";var r=\"3.14.0\",a=\"3.14.0\",e=\"3.14.0\",o=\"3.14.0\",t=\"3.14.0\",s=\"3.14.0\",f=\"3.14.0\",v=\"3.14.0\",j={tfjs:r,\"tfjs-core\":a,\"tfjs-data\":e,\"tfjs-layers\":o,\"tfjs-converter\":t,\"tfjs-backend-cpu\":s,\"tfjs-backend-webgl\":f,\"tfjs-backend-wasm\":v};import{Tensor as b}from\"@tensorflow/tfjs/dist/index.js\";import{GraphModel as i}from\"@tensorflow/tfjs-converter/dist/index\";export{i as GraphModel,b as Tensor,j as version};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        return { tensor: null, canvas: inCanvas };\n      }\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  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0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.2;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const [contours, confidence, contourCoords] = model.execute(face.tensor as Tensor) as Array<Tensor>; // first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      tf.dispose([contourCoords, coordsReshaped, confidence, contours]);\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.iris?.enabled) rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // augment results with iris\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.mesh?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.078125, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 0.109375, y: 0.171875 },\n  { x: 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0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep, now, log } from './util';\nimport { env } from './env';\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Point } from '../result';\nimport type { AnyCanvas } from '../exports';\n\n/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n\nlet drawTime = 0;\n\nconst getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nconst rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * (pt[2] || 0))}, ${127.5 - (2 * (pt[2] || 0))}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (i * z)}, ${127.5 - (i * z)}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see {@link similarity}\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - {@link distance} calculated `distance` of given descriptor to the best match\n * - {@link similarity} calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human } from './human';\nimport type { Tensor } from './tfjs/types';\nimport { env } from './util/env';\n\nasync function warmupBitmap(instance: Human) {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  let res;\n  return new Promise(async (resolve) => {\n    if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n    else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n    else res = await warmupNode(instance);\n    const t1 = now();\n    if (instance.config.debug) log('Warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './util/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default };\n"],
+  "mappings": 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   "names": []
 }
diff --git a/dist/human.esm.js b/dist/human.esm.js
index 5bd052f2..9d4247b1 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -215,207 +215,207 @@ var config = {
 // dist/tfjs.esm.js
 var tfjs_esm_exports = {};
 __export(tfjs_esm_exports, {
-  Abs: () => io,
-  Acos: () => sl,
-  Acosh: () => rl,
-  AdadeltaOptimizer: () => hb,
-  AdagradOptimizer: () => fb,
-  AdamOptimizer: () => mb,
-  AdamaxOptimizer: () => gb,
+  Abs: () => ao,
+  Acos: () => nl,
+  Acosh: () => sl,
+  AdadeltaOptimizer: () => pb,
+  AdagradOptimizer: () => hb,
+  AdamOptimizer: () => fb,
+  AdamaxOptimizer: () => mb,
   Add: () => kr,
   AddN: () => xa,
-  All: () => al,
-  Any: () => il,
+  All: () => rl,
+  Any: () => al,
   ArgMax: () => wa,
-  ArgMin: () => ol,
-  Asin: () => ul,
-  Asinh: () => ll,
-  Atan: () => cl,
-  Atan2: () => pl,
-  Atanh: () => dl,
+  ArgMin: () => il,
+  Asin: () => ol,
+  Asinh: () => ul,
+  Atan: () => ll,
+  Atan2: () => dl,
+  Atanh: () => cl,
   AvgPool: () => ka,
-  AvgPool3D: () => qd,
-  AvgPool3DGrad: () => ig,
-  AvgPoolGrad: () => ag,
+  AvgPool3D: () => Hd,
+  AvgPool3DGrad: () => ag,
+  AvgPoolGrad: () => rg,
   BackendWasm: () => ide,
   BatchMatMul: () => Ia,
-  BatchToSpaceND: () => oo,
-  Bincount: () => og,
-  BroadcastArgs: () => ug,
+  BatchToSpaceND: () => io,
+  Bincount: () => ig,
+  BroadcastArgs: () => og,
   BroadcastTo: () => w$,
   Callback: () => mW,
   CallbackList: () => TL,
   Cast: () => Sa,
   Ceil: () => Ca,
   ClipByValue: () => Ir,
-  Complex: () => jd,
-  ComplexAbs: () => Kd,
-  Concat: () => uo,
+  Complex: () => qd,
+  ComplexAbs: () => jd,
+  Concat: () => oo,
   Conv2D: () => Na,
-  Conv2DBackpropFilter: () => lg,
+  Conv2DBackpropFilter: () => ug,
   Conv2DBackpropInput: () => Ta,
-  Conv3D: () => Xd,
-  Conv3DBackpropFilterV2: () => cg,
-  Conv3DBackpropInputV2: () => dg,
+  Conv3D: () => Kd,
+  Conv3DBackpropFilterV2: () => lg,
+  Conv3DBackpropInputV2: () => cg,
   Cos: () => $a,
   Cosh: () => _a,
-  CropAndResize: () => co,
-  Cumsum: () => lo,
+  CropAndResize: () => lo,
+  Cumsum: () => uo,
   CustomCallback: () => AL,
-  DataStorage: () => Ud,
-  DenseBincount: () => pg,
-  DepthToSpace: () => po,
+  DataStorage: () => Wd,
+  DenseBincount: () => dg,
+  DepthToSpace: () => co,
   DepthwiseConv2dNative: () => Aa,
-  DepthwiseConv2dNativeBackpropFilter: () => hg,
-  DepthwiseConv2dNativeBackpropInput: () => fg,
-  Diag: () => mg,
-  Dilation2D: () => Yd,
-  Dilation2DBackpropFilter: () => Yf,
-  Dilation2DBackpropInput: () => Xf,
+  DepthwiseConv2dNativeBackpropFilter: () => pg,
+  DepthwiseConv2dNativeBackpropInput: () => hg,
+  Diag: () => fg,
+  Dilation2D: () => Xd,
+  Dilation2DBackpropFilter: () => Xf,
+  Dilation2DBackpropInput: () => Kf,
   ENV: () => jw,
   EarlyStopping: () => gW,
-  Einsum: () => Qd,
+  Einsum: () => Yd,
   Elu: () => Ra,
-  EluGrad: () => gg,
+  EluGrad: () => mg,
   Environment: () => m$,
-  Equal: () => ho,
-  Erf: () => hl,
+  Equal: () => po,
+  Erf: () => pl,
   Exp: () => Da,
-  ExpandDims: () => fo,
-  Expm1: () => mo,
-  FFT: () => bg,
-  Fill: () => fl,
-  FlipLeftRight: () => go,
+  ExpandDims: () => ho,
+  Expm1: () => fo,
+  FFT: () => gg,
+  Fill: () => hl,
+  FlipLeftRight: () => mo,
   Floor: () => Fa,
   FloorDiv: () => Oa,
-  FromPixels: () => fd,
+  FromPixels: () => hd,
   FusedBatchNorm: () => Pa,
   FusedConv2D: () => sa,
   FusedDepthwiseConv2D: () => ra,
-  GPGPUContext: () => jf,
-  GatherNd: () => yo,
-  GatherV2: () => bo,
+  GPGPUContext: () => qf,
+  GatherNd: () => bo,
+  GatherV2: () => go,
   GraphModel: () => w4,
-  Greater: () => vo,
+  Greater: () => yo,
   GreaterEqual: () => za,
   History: () => _L,
-  IFFT: () => yg,
+  IFFT: () => bg,
   Identity: () => Ma,
-  Imag: () => Zd,
+  Imag: () => Qd,
   InputSpec: () => Dt,
-  IsFinite: () => ml,
-  IsInf: () => gl,
-  IsNan: () => bl,
-  KernelBackend: () => nl,
-  LRN: () => ep,
-  LRNGrad: () => xg,
+  IsFinite: () => fl,
+  IsInf: () => ml,
+  IsNan: () => gl,
+  KernelBackend: () => tl,
+  LRN: () => Jd,
+  LRNGrad: () => vg,
   LayerVariable: () => xL,
   LayersModel: () => pr,
   LeakyRelu: () => La,
-  Less: () => xo,
-  LessEqual: () => wo,
-  LinSpace: () => vg,
+  Less: () => vo,
+  LessEqual: () => xo,
+  LinSpace: () => yg,
   Log: () => Ba,
-  Log1p: () => yl,
+  Log1p: () => bl,
   LogSoftmax: () => k$,
-  LogicalAnd: () => ko,
-  LogicalNot: () => vl,
-  LogicalOr: () => Jd,
+  LogicalAnd: () => wo,
+  LogicalNot: () => yl,
+  LogicalOr: () => Zd,
   MathBackendCPU: () => RS,
   MathBackendWebGL: () => A1,
   Max: () => Va,
   MaxPool: () => Ua,
-  MaxPool3D: () => tp,
-  MaxPool3DGrad: () => kg,
-  MaxPoolGrad: () => wg,
-  MaxPoolWithArgmax: () => Ig,
+  MaxPool3D: () => ep,
+  MaxPool3DGrad: () => wg,
+  MaxPoolGrad: () => xg,
+  MaxPoolWithArgmax: () => kg,
   Maximum: () => Wa,
   Mean: () => Ga,
   Min: () => Ha,
   Minimum: () => qa,
   MirrorPad: () => ja,
-  Mod: () => xl,
-  MomentumOptimizer: () => bb,
-  Multinomial: () => Sg,
+  Mod: () => vl,
+  MomentumOptimizer: () => gb,
+  Multinomial: () => Ig,
   Multiply: () => Ka,
-  Neg: () => Io,
-  NonMaxSuppressionV3: () => Co,
-  NonMaxSuppressionV4: () => wl,
-  NonMaxSuppressionV5: () => No,
-  NotEqual: () => So,
+  Neg: () => ko,
+  NonMaxSuppressionV3: () => So,
+  NonMaxSuppressionV4: () => xl,
+  NonMaxSuppressionV5: () => Co,
+  NotEqual: () => Io,
   OP_SCOPE_SUFFIX: () => e_,
-  OneHot: () => $o,
-  OnesLike: () => To,
+  OneHot: () => To,
+  OnesLike: () => No,
   Optimizer: () => $r,
   OptimizerConstructors: () => Vr,
-  Pack: () => _o,
+  Pack: () => $o,
   PadV2: () => Xa,
   Pool: () => pde,
   Pow: () => Ya,
   Prelu: () => Qa,
-  Prod: () => Ao,
-  RMSPropOptimizer: () => yb,
+  Prod: () => _o,
+  RMSPropOptimizer: () => bb,
   RNN: () => _r,
-  Range: () => kl,
+  Range: () => wl,
   Rank: () => H$,
-  Real: () => np,
+  Real: () => tp,
   RealDiv: () => Ea,
-  Reciprocal: () => Il,
+  Reciprocal: () => kl,
   Reduction: () => YF,
   Relu: () => Za,
   Relu6: () => ei,
-  Reshape: () => Eo,
+  Reshape: () => Ao,
   ResizeBilinear: () => Ja,
-  ResizeBilinearGrad: () => Ng,
-  ResizeNearestNeighbor: () => Sl,
-  ResizeNearestNeighborGrad: () => Cg,
-  Reverse: () => Ro,
-  RotateWithOffset: () => qo,
-  Round: () => Do,
+  ResizeBilinearGrad: () => Cg,
+  ResizeNearestNeighbor: () => Il,
+  ResizeNearestNeighborGrad: () => Sg,
+  Reverse: () => Eo,
+  RotateWithOffset: () => Ho,
+  Round: () => Ro,
   Rsqrt: () => ti,
-  SGDOptimizer: () => Ip,
-  ScatterNd: () => Fo,
-  Select: () => Oo,
-  Selu: () => Cl,
-  Sequential: () => Wb,
+  SGDOptimizer: () => kp,
+  ScatterNd: () => Do,
+  Select: () => Fo,
+  Selu: () => Sl,
+  Sequential: () => Vb,
   Sigmoid: () => si,
-  Sign: () => Nl,
+  Sign: () => Cl,
   Sin: () => ni,
-  Sinh: () => zo,
-  Slice: () => Po,
+  Sinh: () => Po,
+  Slice: () => Oo,
   Softmax: () => ii,
-  Softplus: () => Tl,
-  SpaceToBatchND: () => Mo,
-  SparseFillEmptyRows: () => sp,
-  SparseReshape: () => $l,
-  SparseSegmentMean: () => rp,
-  SparseSegmentSum: () => ap,
-  SparseToDense: () => ip,
-  SplitV: () => Lo,
+  Softplus: () => Nl,
+  SpaceToBatchND: () => zo,
+  SparseFillEmptyRows: () => np,
+  SparseReshape: () => Tl,
+  SparseSegmentMean: () => sp,
+  SparseSegmentSum: () => rp,
+  SparseToDense: () => ap,
+  SplitV: () => Mo,
   Sqrt: () => ri,
-  Square: () => _l,
+  Square: () => $l,
   SquaredDifference: () => oi,
   Step: () => di,
-  StridedSlice: () => Bo,
-  StringNGrams: () => op,
-  StringSplit: () => Tg,
-  StringToHashBucketFast: () => $g,
+  StridedSlice: () => Lo,
+  StringNGrams: () => ip,
+  StringSplit: () => Ng,
+  StringToHashBucketFast: () => Tg,
   Sub: () => ui,
   Sum: () => ai,
   SymbolicTensor: () => $s,
-  Tan: () => Vo,
+  Tan: () => Bo,
   Tanh: () => li,
   Tensor: () => et,
   TensorBuffer: () => Vt,
   Tile: () => Sr,
-  TopK: () => Wo,
-  Transform: () => Uo,
+  TopK: () => Vo,
+  Transform: () => Wo,
   Transpose: () => ci,
-  Unique: () => _g,
-  Unpack: () => Go,
-  UnsortedSegmentSum: () => up,
-  Variable: () => gd,
-  ZerosLike: () => Ho,
+  Unique: () => $g,
+  Unpack: () => Uo,
+  UnsortedSegmentSum: () => op,
+  Variable: () => md,
+  ZerosLike: () => Go,
   _FusedMatMul: () => na,
   abs: () => Mt,
   acos: () => OA,
@@ -423,29 +423,29 @@ __export(tfjs_esm_exports, {
   add: () => ie,
   addN: () => LA,
   all: () => Bk,
-  any: () => dm,
-  argMax: () => Hu,
+  any: () => cm,
+  argMax: () => Gu,
   argMin: () => GA,
   asin: () => qA,
   asinh: () => KA,
   atan: () => YA,
   atan2: () => ZA,
   atanh: () => eE,
-  avgPool: () => Ug,
+  avgPool: () => Wg,
   avgPool3d: () => Gk,
   backend: () => $A,
   backend_util: () => N,
   basicLSTMCell: () => Ade,
-  batchNorm: () => ju,
+  batchNorm: () => qu,
   batchNorm2d: () => vE,
   batchNorm3d: () => wE,
   batchNorm4d: () => IE,
-  batchToSpaceND: () => Gg,
+  batchToSpaceND: () => Ug,
   bincount: () => Hk,
   booleanMaskAsync: () => rpe,
   broadcastArgs: () => NE,
-  broadcastTo: () => nd,
-  broadcast_util: () => jo,
+  broadcastTo: () => td,
+  broadcast_util: () => qo,
   browser: () => xk,
   buffer: () => De,
   callbacks: () => fpe,
@@ -466,7 +466,7 @@ __export(tfjs_esm_exports, {
   conv3d: () => Kk,
   conv3dTranspose: () => qE,
   copyRegisteredKernels: () => mde,
-  cos: () => qg,
+  cos: () => Hg,
   cosh: () => Yk,
   cosineWindow: () => wI,
   cumsum: () => Qk,
@@ -475,9 +475,9 @@ __export(tfjs_esm_exports, {
   denseBincount: () => QE,
   deprecationWarn: () => Mk,
   depthToSpace: () => JE,
-  depthwiseConv2d: () => hp,
+  depthwiseConv2d: () => pp,
   deregisterOp: () => gpe,
-  device_util: () => dp,
+  device_util: () => cp,
   diag: () => Ede,
   dilation2d: () => sR,
   disableDeprecationWarnings: () => vde,
@@ -488,7 +488,7 @@ __export(tfjs_esm_exports, {
   dot: () => Rde,
   dropout: () => H3,
   einsum: () => dR,
-  elu: () => fp,
+  elu: () => hp,
   enableDebugMode: () => yde,
   enableProdMode: () => bde,
   enclosingPowerOfTwo: () => q3,
@@ -500,29 +500,29 @@ __export(tfjs_esm_exports, {
   expandDims: () => On,
   expm1: () => yR,
   eye: () => Zk,
-  fft: () => ub,
-  fill: () => Ol,
+  fft: () => ob,
+  fill: () => Fl,
   findBackend: () => Tde,
   findBackendFactory: () => $de,
-  floor: () => mp,
+  floor: () => fp,
   floorDiv: () => Lk,
   forceHalfFloat: () => DX,
   fused: () => da,
-  gather: () => Ku,
+  gather: () => ju,
   gatherND: () => W3,
   gather_util: () => kk,
   getBackend: () => Cde,
-  getGradient: () => ex,
-  getKernel: () => Qf,
-  getKernelsForBackend: () => Zf,
+  getGradient: () => Jv,
+  getKernel: () => Yf,
+  getKernelsForBackend: () => Qf,
   getThreadsCount: () => Npe,
   gpgpu_util: () => wK,
   grad: () => Ode,
   grads: () => Pde,
   greater: () => Wn,
-  greaterEqual: () => Ko,
-  ifft: () => Id,
-  imag: () => jg,
+  greaterEqual: () => jo,
+  ifft: () => kd,
+  imag: () => qg,
   image: () => ds,
   inTopKAsync: () => ipe,
   initializers: () => PM,
@@ -535,42 +535,42 @@ __export(tfjs_esm_exports, {
   keep: () => Ht,
   kernel_impls: () => xs,
   layers: () => yL,
-  leakyRelu: () => Kg,
+  leakyRelu: () => jg,
   less: () => Jk,
-  lessEqual: () => Xo,
+  lessEqual: () => Ko,
   linalg: () => AO,
   linspace: () => DR,
   loadGraphModel: () => bpe,
   loadLayersModel: () => ppe,
   localResponseNormalization: () => OR,
   log: () => Kn,
-  log1p: () => Xg,
+  log1p: () => Kg,
   logSigmoid: () => Lde,
   logSoftmax: () => eI,
   logSumExp: () => XR,
   logicalAnd: () => Ds,
-  logicalNot: () => Zg,
+  logicalNot: () => Qg,
   logicalOr: () => rI,
   logicalXor: () => Bde,
   losses: () => lpe,
   matMul: () => We,
   math: () => Q_,
   max: () => As,
-  maxPool: () => Jg,
+  maxPool: () => Zg,
   maxPool3d: () => aI,
   maxPoolWithArgmax: () => sD,
   maximum: () => Tr,
   mean: () => It,
-  memory: () => cm,
+  memory: () => lm,
   meshgrid: () => Vde,
   metrics: () => KV,
-  min: () => hm,
-  minimum: () => bp,
+  min: () => pm,
+  minimum: () => gp,
   mirrorPad: () => lD,
   mod: () => dD,
   model: () => cpe,
   models: () => cW,
-  moments: () => eb,
+  moments: () => Jg,
   movingAverage: () => ape,
   mul: () => V,
   multiRNNCell: () => Wde,
@@ -578,8 +578,8 @@ __export(tfjs_esm_exports, {
   neg: () => kt,
   nextFrame: () => RO,
   norm: () => vI,
-  notEqual: () => Xu,
-  oneHot: () => vd,
+  notEqual: () => Ku,
+  oneHot: () => yd,
   ones: () => zn,
   onesLike: () => Xn,
   op: () => L,
@@ -591,22 +591,22 @@ __export(tfjs_esm_exports, {
   pad4d: () => jde,
   pool: () => Kde,
   pow: () => ca,
-  prelu: () => nb,
+  prelu: () => tb,
   print: () => F_,
   prod: () => iI,
   profile: () => wde,
   rand: () => Xde,
   randomGamma: () => Yde,
   randomNormal: () => zD,
-  randomUniform: () => zl,
-  range: () => Yu,
+  randomUniform: () => Pl,
+  range: () => Xu,
   ready: () => Sde,
-  real: () => wd,
+  real: () => xd,
   reciprocal: () => VD,
-  registerBackend: () => pp,
+  registerBackend: () => dp,
   registerCallbackConstructor: () => hpe,
   registerGradient: () => S$,
-  registerKernel: () => Al,
+  registerKernel: () => _l,
   registerOp: () => mpe,
   regularizers: () => dW,
   relu: () => Xs,
@@ -618,7 +618,7 @@ __export(tfjs_esm_exports, {
   reverse2d: () => Zde,
   reverse3d: () => Jde,
   reverse4d: () => epe,
-  rfft: () => lb,
+  rfft: () => ub,
   round: () => uI,
   rsqrt: () => lI,
   scalar: () => Ie,
@@ -635,22 +635,22 @@ __export(tfjs_esm_exports, {
   setWasmPaths: () => Spe,
   setWebGLContext: () => p5,
   setdiff1dAsync: () => t3,
-  shared: () => Qy,
+  shared: () => Yy,
   sigmoid: () => qs,
   sign: () => s3,
   signal: () => upe,
   sin: () => dI,
   sinh: () => pI,
   slice: () => He,
-  slice1d: () => ab,
+  slice1d: () => rb,
   slice2d: () => hI,
-  slice3d: () => ib,
-  slice4d: () => kd,
+  slice3d: () => ab,
+  slice4d: () => wd,
   slice_util: () => wt,
-  softmax: () => ob,
-  softplus: () => Pl,
-  spaceToBatchND: () => tb,
-  sparse: () => Wc,
+  softmax: () => ib,
+  softplus: () => Ol,
+  spaceToBatchND: () => eb,
+  sparse: () => Vc,
   sparseToDense: () => xI,
   spectral: () => ope,
   split: () => Ln,
@@ -659,17 +659,17 @@ __export(tfjs_esm_exports, {
   squaredDifference: () => mI,
   squeeze: () => mr,
   stack: () => Qn,
-  step: () => yp,
+  step: () => bp,
   stridedSlice: () => k3,
-  string: () => zf,
+  string: () => Pf,
   sub: () => ge,
   sum: () => ye,
-  sumOutType: () => cp,
+  sumOutType: () => lp,
   tan: () => S3,
-  tanh: () => qu,
+  tanh: () => Hu,
   tensor: () => hs,
   tensor1d: () => Qt,
-  tensor2d: () => Ki,
+  tensor2d: () => ji,
   tensor3d: () => sA,
   tensor4d: () => tpe,
   tensor5d: () => npe,
@@ -682,8 +682,8 @@ __export(tfjs_esm_exports, {
   topk: () => N3,
   train: () => Fi,
   transpose: () => qe,
-  truncatedNormal: () => cb,
-  unique: () => dx,
+  truncatedNormal: () => lb,
+  unique: () => cx,
   unregisterGradient: () => fde,
   unregisterKernel: () => hde,
   unsortedSegmentSum: () => A3,
@@ -710,26 +710,26 @@ __export(tfjs_esm_exports, {
   zerosLike: () => je
 });
 var AT = Object.create;
-var Vd = Object.defineProperty;
+var Bd = Object.defineProperty;
 var ET = Object.getOwnPropertyDescriptor;
 var Ow = Object.getOwnPropertyNames;
 var RT = Object.getPrototypeOf;
 var DT = Object.prototype.hasOwnProperty;
-var FT = (e) => Vd(e, "__esModule", { value: true });
+var FT = (e) => Bd(e, "__esModule", { value: true });
 var zt = (e, t) => function() {
   return t || (0, e[Ow(e)[0]])((t = { exports: {} }).exports, t), t.exports;
 };
 var Ae = (e, t) => {
   for (var n in t)
-    Vd(e, n, { get: t[n], enumerable: true });
+    Bd(e, n, { get: t[n], enumerable: true });
 };
 var OT = (e, t, n, s) => {
   if (t && typeof t == "object" || typeof t == "function")
     for (let r of Ow(t))
-      !DT.call(e, r) && (n || r !== "default") && Vd(e, r, { get: () => t[r], enumerable: !(s = ET(t, r)) || s.enumerable });
+      !DT.call(e, r) && (n || r !== "default") && Bd(e, r, { get: () => t[r], enumerable: !(s = ET(t, r)) || s.enumerable });
   return e;
 };
-var ya = (e, t) => OT(FT(Vd(e != null ? AT(RT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
+var ya = (e, t) => OT(FT(Bd(e != null ? AT(RT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
 var PT = zt({ "src/node_modules/long/src/long.js"(e, t) {
   t.exports = s;
   var n = null;
@@ -1302,15 +1302,15 @@ var qT = zt({ "src/node_modules/seedrandom/seedrandom.js"(e, t) {
       }) : r["seed" + u] = f;
   })(typeof self != "undefined" ? self : e, [], Math);
 } });
-var Wd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
+var Vd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
   var n = LT(), s = BT(), r = VT(), a = WT(), i = UT(), o = GT(), u = qT();
   u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = i, u.tychei = o, t.exports = u;
 } });
 var Pw = zt({ "(disabled):src/node_modules/string_decoder/index.js"() {
 } });
-var eg = zt({ "(disabled):fs"() {
+var Jm = zt({ "(disabled):fs"() {
 } });
-var dd = zt({ "(disabled):path"() {
+var cd = zt({ "(disabled):path"() {
 } });
 var jT = zt({ "(disabled):worker_threads"() {
 } });
@@ -1324,25 +1324,25 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
     return typeof __filename != "undefined" && (s = s || __filename), function(r) {
       r = r || {};
       function a() {
-        return Ce.buffer != tn && ns(Ce.buffer), ic;
+        return Ce.buffer != tn && ns(Ce.buffer), ac;
       }
       function i() {
-        return Ce.buffer != tn && ns(Ce.buffer), oc;
+        return Ce.buffer != tn && ns(Ce.buffer), ic;
       }
       function o() {
-        return Ce.buffer != tn && ns(Ce.buffer), hu;
+        return Ce.buffer != tn && ns(Ce.buffer), pu;
       }
       function u() {
-        return Ce.buffer != tn && ns(Ce.buffer), uc;
+        return Ce.buffer != tn && ns(Ce.buffer), oc;
       }
       function l() {
-        return Ce.buffer != tn && ns(Ce.buffer), lc;
+        return Ce.buffer != tn && ns(Ce.buffer), uc;
       }
       function c() {
-        return Ce.buffer != tn && ns(Ce.buffer), cc;
+        return Ce.buffer != tn && ns(Ce.buffer), lc;
       }
       function p() {
-        return Ce.buffer != tn && ns(Ce.buffer), dc;
+        return Ce.buffer != tn && ns(Ce.buffer), cc;
       }
       var d = typeof r != "undefined" ? r : {}, h, f;
       d.ready = new Promise(function(S, D) {
@@ -1358,14 +1358,14 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }
       var P, R, F, $;
       function z(S) {
-        if (S instanceof ku)
+        if (S instanceof wu)
           return;
         ee("exiting due to exception: " + S);
       }
       var W, q, K;
       if (C) {
-        k ? E = dd().dirname(E) + "/" : E = __dirname + "/", K = () => {
-          q || (W = eg(), q = dd());
+        k ? E = cd().dirname(E) + "/" : E = __dirname + "/", K = () => {
+          q || (W = Jm(), q = cd());
         }, P = function(B, Q) {
           return K(), B = q.normalize(B), W.readFileSync(B, Q ? void 0 : "utf8");
         }, F = (D) => {
@@ -1376,7 +1376,7 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             ue ? Q(ue) : B(pe.buffer);
           });
         }, process.argv.length > 1 && (y = process.argv[1].replace(/\\/g, "/")), b = process.argv.slice(2), process.on("uncaughtException", function(D) {
-          if (!(D instanceof ku))
+          if (!(D instanceof wu))
             throw D;
         }), process.on("unhandledRejection", function(D) {
           throw D;
@@ -1433,8 +1433,8 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         for (var ue = 0; ue < Te.length; ++ue)
           pe.push(bt[Te[ue]]);
         be == "v" ? pe.push(0) : pe = pe.concat([1, bt[be]]), pe[1] = pe.length - 2;
-        var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), Bc = new WebAssembly.Instance(os, { e: { f: S } }), Iu = Bc.exports.f;
-        return Iu;
+        var is = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(pe, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0])), os = new WebAssembly.Module(is), Lc = new WebAssembly.Instance(os, { e: { f: S } }), ku = Lc.exports.f;
+        return ku;
       }
       var re = [], le;
       function me() {
@@ -1471,8 +1471,8 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var pe = { string: function(Nn) {
           var Di = 0;
           if (Nn != null && Nn !== 0) {
-            var Zv = (Nn.length << 2) + 1;
-            Di = Ri(Zv), Ls(Nn, Di, Zv);
+            var Qv = (Nn.length << 2) + 1;
+            Di = Ri(Qv), Ls(Nn, Di, Qv);
           }
           return Di;
         }, array: function(Nn) {
@@ -1485,14 +1485,14 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var Te = In(S), bt = [], is = 0;
         if (Q)
           for (var os = 0; os < Q.length; os++) {
-            var Bc = pe[B[os]];
-            Bc ? (is === 0 && (is = Af()), bt[os] = Bc(Q[os])) : bt[os] = Q[os];
+            var Lc = pe[B[os]];
+            Lc ? (is === 0 && (is = _f()), bt[os] = Lc(Q[os])) : bt[os] = Q[os];
           }
-        var Iu = Te.apply(null, bt);
+        var ku = Te.apply(null, bt);
         function _T(Nn) {
-          return is !== 0 && Pc(is), be(Nn);
+          return is !== 0 && Oc(is), be(Nn);
         }
-        return Iu = _T(Iu), Iu;
+        return ku = _T(ku), ku;
       }
       function Jt(S, D, B, Q) {
         B = B || [];
@@ -1581,7 +1581,7 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function Bs(S, D) {
         a().set(S, D);
       }
-      function pu(S, D, B) {
+      function du(S, D, B) {
         for (var Q = 0; Q < S.length; ++Q)
           a()[D++ >> 0] = S.charCodeAt(Q);
         B || (a()[D >> 0] = 0);
@@ -1589,58 +1589,58 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       function wi(S, D) {
         return S % D > 0 && (S += D - S % D), S;
       }
-      var tn, ic, oc, hu, uc, lc, Rv, cc, dc;
+      var tn, ac, ic, pu, oc, uc, Ev, lc, cc;
       T && (tn = d.buffer);
       function ns(S) {
-        tn = S, d.HEAP8 = ic = new Int8Array(S), d.HEAP16 = hu = new Int16Array(S), d.HEAP32 = lc = new Int32Array(S), d.HEAPU8 = oc = new Uint8Array(S), d.HEAPU16 = uc = new Uint16Array(S), d.HEAPU32 = Rv = new Uint32Array(S), d.HEAPF32 = cc = new Float32Array(S), d.HEAPF64 = dc = new Float64Array(S);
+        tn = S, d.HEAP8 = ac = new Int8Array(S), d.HEAP16 = pu = new Int16Array(S), d.HEAP32 = uc = new Int32Array(S), d.HEAPU8 = ic = new Uint8Array(S), d.HEAPU16 = oc = new Uint16Array(S), d.HEAPU32 = Ev = new Uint32Array(S), d.HEAPF32 = lc = new Float32Array(S), d.HEAPF64 = cc = new Float64Array(S);
       }
-      var pc = d.INITIAL_MEMORY || 16777216;
+      var dc = d.INITIAL_MEMORY || 16777216;
       if (T)
         Ce = d.wasmMemory, tn = d.buffer;
       else if (d.wasmMemory)
         Ce = d.wasmMemory;
-      else if (Ce = new WebAssembly.Memory({ initial: pc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer))
+      else if (Ce = new WebAssembly.Memory({ initial: dc / 65536, maximum: 32768, shared: true }), !(Ce.buffer instanceof SharedArrayBuffer))
         throw ee("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), C && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory");
-      Ce && (tn = Ce.buffer), pc = tn.byteLength, ns(tn);
-      var Dn, ki = [], er = [], th = [], hc = [], Fr = false, nh = false, fc = 0;
+      Ce && (tn = Ce.buffer), dc = tn.byteLength, ns(tn);
+      var Dn, ki = [], er = [], eh = [], pc = [], Fr = false, th = false, hc = 0;
       function Or() {
-        return tt || fc > 0;
+        return tt || hc > 0;
       }
       function nn() {
         if (d.preRun)
           for (typeof d.preRun == "function" && (d.preRun = [d.preRun]); d.preRun.length; )
-            Dv(d.preRun.shift());
-        vc(ki);
+            Rv(d.preRun.shift());
+        yc(ki);
       }
-      function fu() {
-        Fr = true, !T && vc(er);
+      function hu() {
+        Fr = true, !T && yc(er);
+      }
+      function nh() {
+        T || ($e.terminateAllThreads(), th = true);
       }
       function sh() {
-        T || ($e.terminateAllThreads(), nh = true);
-      }
-      function rh() {
         if (!T) {
           if (d.postRun)
             for (typeof d.postRun == "function" && (d.postRun = [d.postRun]); d.postRun.length; )
-              mu(d.postRun.shift());
-          vc(hc);
+              fu(d.postRun.shift());
+          yc(pc);
         }
       }
-      function Dv(S) {
+      function Rv(S) {
         ki.unshift(S);
       }
-      function Fv(S) {
+      function Dv(S) {
         er.unshift(S);
       }
-      function mu(S) {
-        hc.unshift(S);
+      function fu(S) {
+        pc.unshift(S);
       }
-      var tr = 0, mc = null, ss = null;
-      function gu(S) {
+      var tr = 0, fc = null, ss = null;
+      function mu(S) {
         tr++, d.monitorRunDependencies && d.monitorRunDependencies(tr);
       }
-      function Ov(S) {
-        if (tr--, d.monitorRunDependencies && d.monitorRunDependencies(tr), tr == 0 && (mc !== null && (clearInterval(mc), mc = null), ss)) {
+      function Fv(S) {
+        if (tr--, d.monitorRunDependencies && d.monitorRunDependencies(tr), tr == 0 && (fc !== null && (clearInterval(fc), fc = null), ss)) {
           var D = ss;
           ss = null, D();
         }
@@ -1651,16 +1651,16 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var D = new WebAssembly.RuntimeError(S);
         throw f(D), D;
       }
-      var ah = "data:application/octet-stream;base64,";
-      function gc(S) {
-        return S.startsWith(ah);
+      var rh = "data:application/octet-stream;base64,";
+      function mc(S) {
+        return S.startsWith(rh);
       }
-      function bc(S) {
+      function gc(S) {
         return S.startsWith("file://");
       }
       var sn;
-      sn = "tfjs-backend-wasm-threaded-simd.wasm", gc(sn) || (sn = A(sn));
-      function yc(S) {
+      sn = "tfjs-backend-wasm-threaded-simd.wasm", mc(sn) || (sn = A(sn));
+      function bc(S) {
         try {
           if (S == sn && Ye)
             return new Uint8Array(Ye);
@@ -1673,13 +1673,13 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }
       function Si() {
         if (!Ye && (x || k)) {
-          if (typeof fetch == "function" && !bc(sn))
+          if (typeof fetch == "function" && !gc(sn))
             return fetch(sn, { credentials: "same-origin" }).then(function(S) {
               if (!S.ok)
                 throw "failed to load wasm binary file at '" + sn + "'";
               return S.arrayBuffer();
             }).catch(function() {
-              return yc(sn);
+              return bc(sn);
             });
           if (R)
             return new Promise(function(S, D) {
@@ -1689,23 +1689,23 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             });
         }
         return Promise.resolve().then(function() {
-          return yc(sn);
+          return bc(sn);
         });
       }
-      function ih() {
-        var S = { env: Ec, wasi_snapshot_preview1: Ec };
+      function ah() {
+        var S = { env: Ac, wasi_snapshot_preview1: Ac };
         function D(be, Te) {
           var bt = be.exports;
-          if (d.asm = bt, hh(d.asm.emscripten_tls_init), Dn = d.asm.__indirect_function_table, Fv(d.asm.__wasm_call_ctors), ut = Te, !T) {
+          if (d.asm = bt, ph(d.asm.emscripten_tls_init), Dn = d.asm.__indirect_function_table, Dv(d.asm.__wasm_call_ctors), ut = Te, !T) {
             var is = $e.unusedWorkers.length;
             $e.unusedWorkers.forEach(function(os) {
               $e.loadWasmModuleToWorker(os, function() {
-                --is || Ov("wasm-instantiate");
+                --is || Fv("wasm-instantiate");
               });
             });
           }
         }
-        T || gu("wasm-instantiate");
+        T || mu("wasm-instantiate");
         function B(be) {
           D(be.instance, be.module);
         }
@@ -1719,7 +1719,7 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           });
         }
         function ue() {
-          return !Ye && typeof WebAssembly.instantiateStreaming == "function" && !gc(sn) && !bc(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(be) {
+          return !Ye && typeof WebAssembly.instantiateStreaming == "function" && !mc(sn) && !gc(sn) && typeof fetch == "function" ? fetch(sn, { credentials: "same-origin" }).then(function(be) {
             var Te = WebAssembly.instantiateStreaming(be, S);
             return Te.then(B, function(bt) {
               return ee("wasm streaming compile failed: " + bt), ee("falling back to ArrayBuffer instantiation"), Q(B);
@@ -1735,8 +1735,8 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           }
         return ue().catch(f), {};
       }
-      var Pv, zv, oh = {};
-      function vc(S) {
+      var Ov, Pv, ih = {};
+      function yc(S) {
         for (; S.length > 0; ) {
           var D = S.shift();
           if (typeof D == "function") {
@@ -1748,29 +1748,29 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
       }
       function Ci(S) {
-        var D = Af(), B = S();
-        return Pc(D), B;
+        var D = _f(), B = S();
+        return Oc(D), B;
       }
       function zN(S) {
         return S;
       }
-      function Mv(S) {
+      function zv(S) {
         var D = /\b_Z[\w\d_]+/g;
         return S.replace(D, function(B) {
           var Q = B;
           return B === Q ? B : Q + " [" + B + "]";
         });
       }
-      function uh(S) {
+      function oh(S) {
         l()[S >> 2] = 0;
         var D = $e.pthreads[S];
-        delete $e.pthreads[S], D.worker.terminate(), _f(S), $e.runningWorkers.splice($e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0;
+        delete $e.pthreads[S], D.worker.terminate(), $f(S), $e.runningWorkers.splice($e.runningWorkers.indexOf(D.worker), 1), D.worker.pthread = void 0;
       }
-      function lh(S) {
+      function uh(S) {
         var D = $e.pthreads[S];
         D.worker.postMessage({ cmd: "cancel" });
       }
-      function xc(S) {
+      function vc(S) {
         var D = $e.pthreads[S];
         if (D) {
           l()[S >> 2] = 0;
@@ -1778,11 +1778,11 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           $e.returnWorkerToPool(B);
         }
       }
-      function wc(S) {
+      function xc(S) {
         NT(S);
       }
-      function ch(S) {
-        if (S instanceof ku || S == "unwind")
+      function lh(S) {
+        if (S instanceof wu || S == "unwind")
           return Zt;
         v(1, S);
       }
@@ -1807,14 +1807,14 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         $e.unusedWorkers = [];
       }, returnWorkerToPool: function(S) {
         $e.runWithoutMainThreadQueuedCalls(function() {
-          delete $e.pthreads[S.pthread.threadInfoStruct], $e.unusedWorkers.push(S), $e.runningWorkers.splice($e.runningWorkers.indexOf(S), 1), _f(S.pthread.threadInfoStruct), S.pthread = void 0;
+          delete $e.pthreads[S.pthread.threadInfoStruct], $e.unusedWorkers.push(S), $e.runningWorkers.splice($e.runningWorkers.indexOf(S), 1), $f(S.pthread.threadInfoStruct), S.pthread = void 0;
         });
       }, runWithoutMainThreadQueuedCalls: function(S) {
-        l()[Qv >> 2] = 0;
+        l()[Yv >> 2] = 0;
         try {
           S();
         } finally {
-          l()[Qv >> 2] = 1;
+          l()[Yv >> 2] = 1;
         }
       }, receiveObjectTransfer: function(S) {
       }, threadInit: function() {
@@ -1823,12 +1823,12 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, loadWasmModuleToWorker: function(S, D) {
         S.onmessage = (B) => {
           var Q = B.data, ue = Q.cmd;
-          if (S.pthread && ($e.currentProxiedOperationCallerThread = S.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Oc()) {
+          if (S.pthread && ($e.currentProxiedOperationCallerThread = S.pthread.threadInfoStruct), Q.targetThread && Q.targetThread != Fc()) {
             var pe = $e.pthreads[Q.targetThread];
             pe ? pe.worker.postMessage(Q, Q.transferList) : ee('Internal error! Worker sent a message "' + ue + '" to target pthread ' + Q.targetThread + ", but that thread no longer exists!"), $e.currentProxiedOperationCallerThread = void 0;
             return;
           }
-          ue === "processQueuedMainThreadWork" ? qv() : ue === "spawnThread" ? Ic(Q) : ue === "cleanupThread" ? xc(Q.thread) : ue === "killThread" ? uh(Q.thread) : ue === "cancelThread" ? lh(Q.thread) : ue === "loaded" ? (S.loaded = true, D && D(S), S.runPthread && (S.runPthread(), delete S.runPthread)) : ue === "print" ? te("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? S.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : ee("worker sent an unknown command " + ue), $e.currentProxiedOperationCallerThread = void 0;
+          ue === "processQueuedMainThreadWork" ? Hv() : ue === "spawnThread" ? kc(Q) : ue === "cleanupThread" ? vc(Q.thread) : ue === "killThread" ? oh(Q.thread) : ue === "cancelThread" ? uh(Q.thread) : ue === "loaded" ? (S.loaded = true, D && D(S), S.runPthread && (S.runPthread(), delete S.runPthread)) : ue === "print" ? te("Thread " + Q.threadId + ": " + Q.text) : ue === "printErr" ? ee("Thread " + Q.threadId + ": " + Q.text) : ue === "alert" ? alert("Thread " + Q.threadId + ": " + Q.text) : Q.target === "setimmediate" ? S.postMessage(Q) : ue === "onAbort" ? d.onAbort && d.onAbort(Q.arg) : ee("worker sent an unknown command " + ue), $e.currentProxiedOperationCallerThread = void 0;
         }, S.onerror = (B) => {
           var Q = "worker sent an error!";
           throw ee(Q + " " + B.filename + ":" + B.lineno + ": " + B.message), B;
@@ -1844,18 +1844,18 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, getNewWorker: function() {
         return $e.unusedWorkers.length == 0 && ($e.allocateUnusedWorker(), $e.loadWasmModuleToWorker($e.unusedWorkers[0])), $e.unusedWorkers.pop();
       } };
-      function dh() {
-        var S = Oc(), D = l()[S + 44 >> 2], B = l()[S + 48 >> 2], Q = D - B;
-        Yv(D, Q), Pc(D);
+      function ch() {
+        var S = Fc(), D = l()[S + 44 >> 2], B = l()[S + 48 >> 2], Q = D - B;
+        Xv(D, Q), Oc(D);
       }
-      d.establishStackSpace = dh;
-      function kc(S) {
+      d.establishStackSpace = ch;
+      function wc(S) {
         if (T)
           return Mr(1, 0, S);
         try {
-          wc(S);
+          xc(S);
         } catch (D) {
-          ch(D);
+          lh(D);
         }
       }
       var Pr = [];
@@ -1863,11 +1863,11 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var D = Pr[S];
         return D || (S >= Pr.length && (Pr.length = S + 1), Pr[S] = D = Dn.get(S)), D;
       }
-      function ph(S, D) {
+      function dh(S, D) {
         return Ni(S)(D);
       }
-      d.invokeEntryPoint = ph;
-      function Lv() {
+      d.invokeEntryPoint = dh;
+      function Mv() {
         var S = new Error();
         if (!S.stack) {
           try {
@@ -1880,10 +1880,10 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         return S.stack.toString();
       }
-      function hh(S, D, B) {
+      function ph(S, D, B) {
         $e.tlsInitFunctions.push(S);
       }
-      function Bv(S, D) {
+      function Lv(S, D) {
         Dn.set(S, D), Pr[S] = D;
       }
       var zr;
@@ -1891,30 +1891,30 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var S = process.hrtime();
         return S[0] * 1e3 + S[1] / 1e6;
       } : T ? zr = () => performance.now() - d.__performance_now_clock_drift : zr = () => performance.now();
-      var fh = true;
-      function mh(S) {
-        return l()[Hv() >> 2] = S, S;
+      var hh = true;
+      function fh(S) {
+        return l()[Gv() >> 2] = S, S;
       }
-      function gh(S, D) {
+      function mh(S, D) {
         var B;
         if (S === 0)
           B = Date.now();
-        else if ((S === 1 || S === 4) && fh)
+        else if ((S === 1 || S === 4) && hh)
           B = zr();
         else
-          return mh(28), -1;
+          return fh(28), -1;
         return l()[D >> 2] = B / 1e3 | 0, l()[D + 4 >> 2] = B % 1e3 * 1e3 * 1e3 | 0, 0;
       }
-      function bh(S, D) {
-        return gh(S, D);
+      function gh(S, D) {
+        return mh(S, D);
+      }
+      function bh(S) {
+        qv(S, !k, 1, !x), $e.threadInit();
       }
       function yh(S) {
-        jv(S, !k, 1, !x), $e.threadInit();
+        T ? postMessage({ cmd: "cleanupThread", thread: S }) : vc(S);
       }
-      function vh(S) {
-        T ? postMessage({ cmd: "cleanupThread", thread: S }) : xc(S);
-      }
-      function Ic(S) {
+      function kc(S) {
         var D = $e.getNewWorker();
         if (!D)
           return 6;
@@ -1926,21 +1926,21 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           Q.time = performance.now(), D.postMessage(Q, S.transferList);
         }, D.loaded && (D.runPthread(), delete D.runPthread), 0;
       }
-      function xh(S, D, B, Q) {
+      function vh(S, D, B, Q) {
         if (typeof SharedArrayBuffer == "undefined")
           return ee("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
         var ue = [], pe = 0;
         if (T && (ue.length === 0 || pe))
-          return Kv(687865856, S, D, B, Q);
+          return jv(687865856, S, D, B, Q);
         if (pe)
           return pe;
         var be = { startRoutine: B, pthread_ptr: S, arg: Q, transferList: ue };
-        return T ? (be.cmd = "spawnThread", postMessage(be, ue), 0) : Ic(be);
+        return T ? (be.cmd = "spawnThread", postMessage(be, ue), 0) : kc(be);
       }
-      function wh() {
+      function xh() {
         return 2097152;
       }
-      function kh(S, D) {
+      function wh(S, D) {
         if (S == D)
           postMessage({ cmd: "processQueuedMainThreadWork" });
         else if (T)
@@ -1953,19 +1953,19 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         return 1;
       }
-      function Ih() {
+      function kh() {
         Ii("");
       }
-      function Sh() {
+      function Ih() {
         C || k || ne("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
       }
-      function Sc() {
+      function Ic() {
         return 2147483648;
       }
-      function Ch(S, D, B) {
+      function Sh(S, D, B) {
         i().copyWithin(S, D, D + B);
       }
-      function Nh() {
+      function Ch() {
         return C ? XT().cpus().length : navigator.hardwareConcurrency;
       }
       function Mr(S, D) {
@@ -1975,34 +1975,34 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             var bt = Q[2 + Te];
             p()[be + Te] = bt;
           }
-          return Xv(S, ue, pe, D);
+          return Kv(S, ue, pe, D);
         });
       }
-      var bu = [];
-      function Th(S, D, B) {
-        bu.length = D;
+      var gu = [];
+      function Nh(S, D, B) {
+        gu.length = D;
         for (var Q = B >> 3, ue = 0; ue < D; ue++)
-          bu[ue] = p()[Q + ue];
-        var pe = S < 0, be = pe ? oh[-S - 1] : qh[S];
-        return be.apply(null, bu);
+          gu[ue] = p()[Q + ue];
+        var pe = S < 0, be = pe ? ih[-S - 1] : Hh[S];
+        return be.apply(null, gu);
       }
-      function $h(S) {
+      function Th(S) {
         try {
           return Ce.grow(S - tn.byteLength + 65535 >>> 16), ns(Ce.buffer), 1;
         } catch (D) {
         }
       }
-      function _h(S) {
+      function $h(S) {
         var D = i().length;
         if (S = S >>> 0, S <= D)
           return false;
-        var B = Sc();
+        var B = Ic();
         if (S > B)
           return false;
         for (var Q = 1; Q <= 4; Q *= 2) {
           var ue = D * (1 + 0.2 / Q);
           ue = Math.min(ue, S + 100663296);
-          var pe = Math.min(B, wi(Math.max(S, ue), 65536)), be = $h(pe);
+          var pe = Math.min(B, wi(Math.max(S, ue), 65536)), be = Th(pe);
           if (be)
             return true;
         }
@@ -2013,7 +2013,7 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           Le._removeHandler(S);
         Le.eventHandlers = [], Le.deferredCalls = [];
       }, registerRemoveEventListeners: function() {
-        Le.removeEventListenersRegistered || (th.push(Le.removeAllEventListeners), Le.removeEventListenersRegistered = true);
+        Le.removeEventListenersRegistered || (eh.push(Le.removeAllEventListeners), Le.removeEventListenersRegistered = true);
       }, deferredCalls: [], deferCall: function(S, D, B) {
         function Q(be, Te) {
           if (be.length != Te.length)
@@ -2060,7 +2060,7 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, queueEventHandlerOnThread_iiii: function(S, D, B, Q, ue) {
         Ci(function() {
           var pe = Ri(12);
-          l()[pe >> 2] = B, l()[pe + 4 >> 2] = Q, l()[pe + 8 >> 2] = ue, $f(S, 637534208, D, Q, pe);
+          l()[pe >> 2] = B, l()[pe + 4 >> 2] = Q, l()[pe + 8 >> 2] = ue, Tf(S, 637534208, D, Q, pe);
         });
       }, getTargetThreadForEventCallback: function(S) {
         switch (S) {
@@ -2076,33 +2076,33 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, fullscreenEnabled: function() {
         return document.fullscreenEnabled || document.webkitFullscreenEnabled;
       } };
-      function Ah(S) {
-        var D = xi(S) + 1, B = Tf(D);
+      function _h(S) {
+        var D = xi(S) + 1, B = Nf(D);
         return Ls(S, B, D), B;
       }
-      function Eh(S, D, B, Q) {
+      function Ah(S, D, B, Q) {
         Ci(function() {
           var ue = Ri(12), pe = 0;
-          D && (pe = Ah(D)), l()[ue >> 2] = pe, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, $f(S, 657457152, 0, pe, ue);
+          D && (pe = _h(D)), l()[ue >> 2] = pe, l()[ue + 4 >> 2] = B, l()[ue + 8 >> 2] = Q, Tf(S, 657457152, 0, pe, ue);
         });
       }
-      function Rh(S, D, B, Q) {
-        D = D ? en(D) : "", Eh(S, D, B, Q);
+      function Eh(S, D, B, Q) {
+        D = D ? en(D) : "", Ah(S, D, B, Q);
       }
-      function Dh(S) {
+      function Rh(S) {
         return S > 2 ? en(S) : S;
       }
-      var Fh = [0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0];
-      function Oh(S) {
-        S = Dh(S);
-        var D = Fh[S] || (typeof document != "undefined" ? document.querySelector(S) : void 0);
+      var Dh = [0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0];
+      function Fh(S) {
+        S = Rh(S);
+        var D = Dh[S] || (typeof document != "undefined" ? document.querySelector(S) : void 0);
         return D;
       }
-      function yu(S) {
-        return Oh(S);
+      function bu(S) {
+        return Fh(S);
       }
-      function Cc(S, D, B) {
-        var Q = yu(S);
+      function Sc(S, D, B) {
+        var Q = bu(S);
         if (!Q)
           return -4;
         if (Q.canvasSharedPtr && (l()[Q.canvasSharedPtr >> 2] = D, l()[Q.canvasSharedPtr + 4 >> 2] = B), Q.offscreenCanvas || !Q.controlTransferredOffscreen) {
@@ -2115,22 +2115,22 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           Q.width = D, Q.height = B, ue && Q.GLctxObject.GLctx.viewport(0, 0, D, B);
         } else if (Q.canvasSharedPtr) {
           var be = l()[Q.canvasSharedPtr + 8 >> 2];
-          return Rh(be, S, D, B), 1;
+          return Eh(be, S, D, B), 1;
         } else
           return -4;
         return 0;
       }
-      function Nc(S, D, B) {
-        return T ? Mr(2, 1, S, D, B) : Cc(S, D, B);
+      function Cc(S, D, B) {
+        return T ? Mr(2, 1, S, D, B) : Sc(S, D, B);
       }
-      function Ph(S, D, B) {
-        var Q = yu(S);
-        return Q ? Cc(S, D, B) : Nc(S, D, B);
+      function Oh(S, D, B) {
+        var Q = bu(S);
+        return Q ? Sc(S, D, B) : Cc(S, D, B);
       }
-      function zh() {
+      function Ph() {
         throw "unwind";
       }
-      function Mh(S) {
+      function zh(S) {
         var D = S.getExtension("ANGLE_instanced_arrays");
         if (D)
           return S.vertexAttribDivisor = function(B, Q) {
@@ -2141,7 +2141,7 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             D.drawElementsInstancedANGLE(B, Q, ue, pe, be);
           }, 1;
       }
-      function Lh(S) {
+      function Mh(S) {
         var D = S.getExtension("OES_vertex_array_object");
         if (D)
           return S.createVertexArray = function() {
@@ -2154,14 +2154,14 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
             return D.isVertexArrayOES(B);
           }, 1;
       }
-      function Bh(S) {
+      function Lh(S) {
         var D = S.getExtension("WEBGL_draw_buffers");
         if (D)
           return S.drawBuffers = function(B, Q) {
             D.drawBuffersWEBGL(B, Q);
           }, 1;
       }
-      function Vh(S) {
+      function Bh(S) {
         return !!(S.multiDrawWebgl = S.getExtension("WEBGL_multi_draw"));
       }
       var gt = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function(D) {
@@ -2187,36 +2187,36 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         var Q = gt.registerContext(B, D);
         return Q;
       }, registerContext: function(S, D) {
-        var B = Tf(8);
-        l()[B + 4 >> 2] = Oc();
+        var B = Nf(8);
+        l()[B + 4 >> 2] = Fc();
         var Q = { handle: B, attributes: D, version: D.majorVersion, GLctx: S };
         return S.canvas && (S.canvas.GLctxObject = Q), gt.contexts[B] = Q, (typeof D.enableExtensionsByDefault == "undefined" || D.enableExtensionsByDefault) && gt.initExtensions(Q), B;
       }, makeContextCurrent: function(S) {
-        return gt.currentContext = gt.contexts[S], d.ctx = Ac = gt.currentContext && gt.currentContext.GLctx, !(S && !Ac);
+        return gt.currentContext = gt.contexts[S], d.ctx = _c = gt.currentContext && gt.currentContext.GLctx, !(S && !_c);
       }, getContext: function(S) {
         return gt.contexts[S];
       }, deleteContext: function(S) {
-        gt.currentContext === gt.contexts[S] && (gt.currentContext = null), typeof Le == "object" && Le.removeAllHandlersOnTarget(gt.contexts[S].GLctx.canvas), gt.contexts[S] && gt.contexts[S].GLctx.canvas && (gt.contexts[S].GLctx.canvas.GLctxObject = void 0), Gv(gt.contexts[S].handle), gt.contexts[S] = null;
+        gt.currentContext === gt.contexts[S] && (gt.currentContext = null), typeof Le == "object" && Le.removeAllHandlersOnTarget(gt.contexts[S].GLctx.canvas), gt.contexts[S] && gt.contexts[S].GLctx.canvas && (gt.contexts[S].GLctx.canvas.GLctxObject = void 0), Uv(gt.contexts[S].handle), gt.contexts[S] = null;
       }, initExtensions: function(S) {
         if (S || (S = gt.currentContext), !S.initExtensionsDone) {
           S.initExtensionsDone = true;
           var D = S.GLctx;
-          Mh(D), Lh(D), Bh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Vh(D);
+          zh(D), Mh(D), Lh(D), D.disjointTimerQueryExt = D.getExtension("EXT_disjoint_timer_query"), Bh(D);
           var B = D.getSupportedExtensions() || [];
           B.forEach(function(Q) {
             !Q.includes("lose_context") && !Q.includes("debug") && D.getExtension(Q);
           });
         }
-      } }, Wh = ["default", "low-power", "high-performance"];
-      function Uh(S, D) {
-        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Wh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = yu(S);
+      } }, Vh = ["default", "low-power", "high-performance"];
+      function Wh(S, D) {
+        var B = D >> 2, Q = l()[B + 6], ue = { alpha: !!l()[B + 0], depth: !!l()[B + 1], stencil: !!l()[B + 2], antialias: !!l()[B + 3], premultipliedAlpha: !!l()[B + 4], preserveDrawingBuffer: !!l()[B + 5], powerPreference: Vh[Q], failIfMajorPerformanceCaveat: !!l()[B + 7], majorVersion: l()[B + 8], minorVersion: l()[B + 9], enableExtensionsByDefault: l()[B + 10], explicitSwapControl: l()[B + 11], proxyContextToMainThread: l()[B + 12], renderViaOffscreenBackBuffer: l()[B + 13] }, pe = bu(S);
         if (!pe || ue.explicitSwapControl)
           return 0;
         var be = gt.createContext(pe, ue);
         return be;
       }
-      function Gh(S, D) {
-        return Uh(S, D);
+      function Uh(S, D) {
+        return Wh(S, D);
       }
       var Ti = { mappings: {}, buffers: [null, [], []], printChar: function(S, D) {
         var B = Ti.buffers[S];
@@ -2231,14 +2231,14 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
       }, get64: function(S, D) {
         return S;
       } };
-      function Tc(S) {
+      function Nc(S) {
         return T ? Mr(3, 1, S) : 0;
       }
-      function $c(S, D, B, Q, ue) {
+      function Tc(S, D, B, Q, ue) {
         if (T)
           return Mr(4, 1, S, D, B, Q, ue);
       }
-      function _c(S, D, B, Q) {
+      function $c(S, D, B, Q) {
         if (T)
           return Mr(5, 1, S, D, B, Q);
         for (var ue = 0, pe = 0; pe < B; pe++) {
@@ -2250,92 +2250,92 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         }
         return l()[Q >> 2] = ue, 0;
       }
-      function Hh(S) {
+      function Gh(S) {
         Ee(S);
       }
       $e.init();
-      var Ac, qh = [null, kc, Nc, Tc, $c, _c], Vv = false, Ec = { __clock_gettime: bh, __emscripten_init_main_thread_js: yh, __emscripten_thread_cleanup: vh, __pthread_create_js: xh, _emscripten_default_pthread_stack_size: wh, _emscripten_notify_thread_queue: kh, abort: Ih, emscripten_check_blocking_allowed: Sh, emscripten_get_heap_max: Sc, emscripten_get_now: zr, emscripten_memcpy_big: Ch, emscripten_num_logical_cores: Nh, emscripten_receive_on_main_thread_js: Th, emscripten_resize_heap: _h, emscripten_set_canvas_element_size: Ph, emscripten_unwind_to_js_event_loop: zh, emscripten_webgl_create_context: Gh, exit: wc, fd_close: Tc, fd_seek: $c, fd_write: _c, memory: Ce || d.wasmMemory, setTempRet0: Hh }, Wv = ih(), jh = d.___wasm_call_ctors = function() {
-        return (jh = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments);
-      }, Kh = d._init = function() {
-        return (Kh = d._init = d.asm.init).apply(null, arguments);
-      }, Xh = d._init_with_threads_count = function() {
-        return (Xh = d._init_with_threads_count = d.asm.init_with_threads_count).apply(null, arguments);
-      }, Yh = d._get_threads_count = function() {
-        return (Yh = d._get_threads_count = d.asm.get_threads_count).apply(null, arguments);
-      }, Qh = d._register_tensor = function() {
-        return (Qh = d._register_tensor = d.asm.register_tensor).apply(null, arguments);
-      }, Zh = d._dispose_data = function() {
-        return (Zh = d._dispose_data = d.asm.dispose_data).apply(null, arguments);
-      }, Jh = d._dispose = function() {
-        return (Jh = d._dispose = d.asm.dispose).apply(null, arguments);
-      }, ef = d._Abs = function() {
-        return (ef = d._Abs = d.asm.Abs).apply(null, arguments);
-      }, tf = d._Add = function() {
-        return (tf = d._Add = d.asm.Add).apply(null, arguments);
-      }, nf = d._AddN = function() {
-        return (nf = d._AddN = d.asm.AddN).apply(null, arguments);
-      }, sf = d._All = function() {
-        return (sf = d._All = d.asm.All).apply(null, arguments);
-      }, rf = d._Any = function() {
-        return (rf = d._Any = d.asm.Any).apply(null, arguments);
-      }, af = d._ArgMax = function() {
-        return (af = d._ArgMax = d.asm.ArgMax).apply(null, arguments);
-      }, of = d._AvgPool = function() {
-        return (of = d._AvgPool = d.asm.AvgPool).apply(null, arguments);
-      }, uf = d._BatchMatMul = function() {
-        return (uf = d._BatchMatMul = d.asm.BatchMatMul).apply(null, arguments);
-      }, lf = d._Ceil = function() {
-        return (lf = d._Ceil = d.asm.Ceil).apply(null, arguments);
-      }, cf = d._ClipByValue = function() {
-        return (cf = d._ClipByValue = d.asm.ClipByValue).apply(null, arguments);
-      }, df = d._Conv2D = function() {
-        return (df = d._Conv2D = d.asm.Conv2D).apply(null, arguments);
-      }, pf = d._Conv2DBackpropInput = function() {
-        return (pf = d._Conv2DBackpropInput = d.asm.Conv2DBackpropInput).apply(null, arguments);
-      }, hf = d._Cos = function() {
-        return (hf = d._Cos = d.asm.Cos).apply(null, arguments);
-      }, ff = d._Cosh = function() {
-        return (ff = d._Cosh = d.asm.Cosh).apply(null, arguments);
-      }, mf = d._CropAndResize = function() {
-        return (mf = d._CropAndResize = d.asm.CropAndResize).apply(null, arguments);
-      }, gf = d._Cumsum = function() {
-        return (gf = d._Cumsum = d.asm.Cumsum).apply(null, arguments);
-      }, bf = d._DepthToSpace = function() {
-        return (bf = d._DepthToSpace = d.asm.DepthToSpace).apply(null, arguments);
-      }, yf = d._DepthwiseConv2dNative = function() {
-        return (yf = d._DepthwiseConv2dNative = d.asm.DepthwiseConv2dNative).apply(null, arguments);
-      }, vf = d._Elu = function() {
-        return (vf = d._Elu = d.asm.Elu).apply(null, arguments);
-      }, xf = d._Equal = function() {
-        return (xf = d._Equal = d.asm.Equal).apply(null, arguments);
-      }, wf = d._Exp = function() {
-        return (wf = d._Exp = d.asm.Exp).apply(null, arguments);
-      }, kf = d._FlipLeftRight = function() {
-        return (kf = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments);
-      }, Rc = d._Floor = function() {
-        return (Rc = d._Floor = d.asm.Floor).apply(null, arguments);
-      }, Dc = d._FloorDiv = function() {
-        return (Dc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
-      }, vu = d._FusedBatchNorm = function() {
-        return (vu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
-      }, If = d._FusedConv2D = function() {
-        return (If = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
-      }, Sf = d._FusedDepthwiseConv2D = function() {
-        return (Sf = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
+      var _c, Hh = [null, wc, Cc, Nc, Tc, $c], Bv = false, Ac = { __clock_gettime: gh, __emscripten_init_main_thread_js: bh, __emscripten_thread_cleanup: yh, __pthread_create_js: vh, _emscripten_default_pthread_stack_size: xh, _emscripten_notify_thread_queue: wh, abort: kh, emscripten_check_blocking_allowed: Ih, emscripten_get_heap_max: Ic, emscripten_get_now: zr, emscripten_memcpy_big: Sh, emscripten_num_logical_cores: Ch, emscripten_receive_on_main_thread_js: Nh, emscripten_resize_heap: $h, emscripten_set_canvas_element_size: Oh, emscripten_unwind_to_js_event_loop: Ph, emscripten_webgl_create_context: Uh, exit: xc, fd_close: Nc, fd_seek: Tc, fd_write: $c, memory: Ce || d.wasmMemory, setTempRet0: Gh }, Vv = ah(), qh = d.___wasm_call_ctors = function() {
+        return (qh = d.___wasm_call_ctors = d.asm.__wasm_call_ctors).apply(null, arguments);
+      }, jh = d._init = function() {
+        return (jh = d._init = d.asm.init).apply(null, arguments);
+      }, Kh = d._init_with_threads_count = function() {
+        return (Kh = d._init_with_threads_count = d.asm.init_with_threads_count).apply(null, arguments);
+      }, Xh = d._get_threads_count = function() {
+        return (Xh = d._get_threads_count = d.asm.get_threads_count).apply(null, arguments);
+      }, Yh = d._register_tensor = function() {
+        return (Yh = d._register_tensor = d.asm.register_tensor).apply(null, arguments);
+      }, Qh = d._dispose_data = function() {
+        return (Qh = d._dispose_data = d.asm.dispose_data).apply(null, arguments);
+      }, Zh = d._dispose = function() {
+        return (Zh = d._dispose = d.asm.dispose).apply(null, arguments);
+      }, Jh = d._Abs = function() {
+        return (Jh = d._Abs = d.asm.Abs).apply(null, arguments);
+      }, ef = d._Add = function() {
+        return (ef = d._Add = d.asm.Add).apply(null, arguments);
+      }, tf = d._AddN = function() {
+        return (tf = d._AddN = d.asm.AddN).apply(null, arguments);
+      }, nf = d._All = function() {
+        return (nf = d._All = d.asm.All).apply(null, arguments);
+      }, sf = d._Any = function() {
+        return (sf = d._Any = d.asm.Any).apply(null, arguments);
+      }, rf = d._ArgMax = function() {
+        return (rf = d._ArgMax = d.asm.ArgMax).apply(null, arguments);
+      }, af = d._AvgPool = function() {
+        return (af = d._AvgPool = d.asm.AvgPool).apply(null, arguments);
+      }, of = d._BatchMatMul = function() {
+        return (of = d._BatchMatMul = d.asm.BatchMatMul).apply(null, arguments);
+      }, uf = d._Ceil = function() {
+        return (uf = d._Ceil = d.asm.Ceil).apply(null, arguments);
+      }, lf = d._ClipByValue = function() {
+        return (lf = d._ClipByValue = d.asm.ClipByValue).apply(null, arguments);
+      }, cf = d._Conv2D = function() {
+        return (cf = d._Conv2D = d.asm.Conv2D).apply(null, arguments);
+      }, df = d._Conv2DBackpropInput = function() {
+        return (df = d._Conv2DBackpropInput = d.asm.Conv2DBackpropInput).apply(null, arguments);
+      }, pf = d._Cos = function() {
+        return (pf = d._Cos = d.asm.Cos).apply(null, arguments);
+      }, hf = d._Cosh = function() {
+        return (hf = d._Cosh = d.asm.Cosh).apply(null, arguments);
+      }, ff = d._CropAndResize = function() {
+        return (ff = d._CropAndResize = d.asm.CropAndResize).apply(null, arguments);
+      }, mf = d._Cumsum = function() {
+        return (mf = d._Cumsum = d.asm.Cumsum).apply(null, arguments);
+      }, gf = d._DepthToSpace = function() {
+        return (gf = d._DepthToSpace = d.asm.DepthToSpace).apply(null, arguments);
+      }, bf = d._DepthwiseConv2dNative = function() {
+        return (bf = d._DepthwiseConv2dNative = d.asm.DepthwiseConv2dNative).apply(null, arguments);
+      }, yf = d._Elu = function() {
+        return (yf = d._Elu = d.asm.Elu).apply(null, arguments);
+      }, vf = d._Equal = function() {
+        return (vf = d._Equal = d.asm.Equal).apply(null, arguments);
+      }, xf = d._Exp = function() {
+        return (xf = d._Exp = d.asm.Exp).apply(null, arguments);
+      }, wf = d._FlipLeftRight = function() {
+        return (wf = d._FlipLeftRight = d.asm.FlipLeftRight).apply(null, arguments);
+      }, Ec = d._Floor = function() {
+        return (Ec = d._Floor = d.asm.Floor).apply(null, arguments);
+      }, Rc = d._FloorDiv = function() {
+        return (Rc = d._FloorDiv = d.asm.FloorDiv).apply(null, arguments);
+      }, yu = d._FusedBatchNorm = function() {
+        return (yu = d._FusedBatchNorm = d.asm.FusedBatchNorm).apply(null, arguments);
+      }, kf = d._FusedConv2D = function() {
+        return (kf = d._FusedConv2D = d.asm.FusedConv2D).apply(null, arguments);
+      }, If = d._FusedDepthwiseConv2D = function() {
+        return (If = d._FusedDepthwiseConv2D = d.asm.FusedDepthwiseConv2D).apply(null, arguments);
       }, $i = d._Gather = function() {
         return ($i = d._Gather = d.asm.Gather).apply(null, arguments);
-      }, xu = d._GatherNd = function() {
-        return (xu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
-      }, wu = d._Greater = function() {
-        return (wu = d._Greater = d.asm.Greater).apply(null, arguments);
-      }, Uv = d._GreaterEqual = function() {
-        return (Uv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
+      }, vu = d._GatherNd = function() {
+        return (vu = d._GatherNd = d.asm.GatherNd).apply(null, arguments);
+      }, xu = d._Greater = function() {
+        return (xu = d._Greater = d.asm.Greater).apply(null, arguments);
+      }, Wv = d._GreaterEqual = function() {
+        return (Wv = d._GreaterEqual = d.asm.GreaterEqual).apply(null, arguments);
       }, _i = d._LeakyRelu = function() {
         return (_i = d._LeakyRelu = d.asm.LeakyRelu).apply(null, arguments);
       }, Ai = d._Less = function() {
         return (Ai = d._Less = d.asm.Less).apply(null, arguments);
-      }, Cf = d._LessEqual = function() {
-        return (Cf = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
+      }, Sf = d._LessEqual = function() {
+        return (Sf = d._LessEqual = d.asm.LessEqual).apply(null, arguments);
       }, H = d._Log = function() {
         return (H = d._Log = d.asm.Log).apply(null, arguments);
       }, J = d._LogicalAnd = function() {
@@ -2362,14 +2362,14 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return (Ei = d._NonMaxSuppressionV3 = d.asm.NonMaxSuppressionV3).apply(null, arguments);
       }, Lr = d._NonMaxSuppressionV4 = function() {
         return (Lr = d._NonMaxSuppressionV4 = d.asm.NonMaxSuppressionV4).apply(null, arguments);
-      }, Nf = d._NonMaxSuppressionV5 = function() {
-        return (Nf = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
+      }, Cf = d._NonMaxSuppressionV5 = function() {
+        return (Cf = d._NonMaxSuppressionV5 = d.asm.NonMaxSuppressionV5).apply(null, arguments);
       }, rn = d._NotEqual = function() {
         return (rn = d._NotEqual = d.asm.NotEqual).apply(null, arguments);
       }, nr = d._OneHot = function() {
         return (nr = d._OneHot = d.asm.OneHot).apply(null, arguments);
-      }, Fc = d._PadV2 = function() {
-        return (Fc = d._PadV2 = d.asm.PadV2).apply(null, arguments);
+      }, Dc = d._PadV2 = function() {
+        return (Dc = d._PadV2 = d.asm.PadV2).apply(null, arguments);
       }, MN = d._Pow = function() {
         return (MN = d._Pow = d.asm.Pow).apply(null, arguments);
       }, LN = d._Prelu = function() {
@@ -2436,72 +2436,72 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
         return (mT = d._Transpose = d.asm.Transpose).apply(null, arguments);
       }, gT = d.__FusedMatMul = function() {
         return (gT = d.__FusedMatMul = d.asm._FusedMatMul).apply(null, arguments);
-      }, Tf = d._malloc = function() {
-        return (Tf = d._malloc = d.asm.malloc).apply(null, arguments);
-      }, Gv = d._free = function() {
-        return (Gv = d._free = d.asm.free).apply(null, arguments);
+      }, Nf = d._malloc = function() {
+        return (Nf = d._malloc = d.asm.malloc).apply(null, arguments);
+      }, Uv = d._free = function() {
+        return (Uv = d._free = d.asm.free).apply(null, arguments);
       }, bT = d._emscripten_tls_init = function() {
         return (bT = d._emscripten_tls_init = d.asm.emscripten_tls_init).apply(null, arguments);
-      }, Hv = d.___errno_location = function() {
-        return (Hv = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
-      }, Oc = d._pthread_self = function() {
-        return (Oc = d._pthread_self = d.asm.pthread_self).apply(null, arguments);
-      }, qv = d._emscripten_main_thread_process_queued_calls = function() {
-        return (qv = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
+      }, Gv = d.___errno_location = function() {
+        return (Gv = d.___errno_location = d.asm.__errno_location).apply(null, arguments);
+      }, Fc = d._pthread_self = function() {
+        return (Fc = d._pthread_self = d.asm.pthread_self).apply(null, arguments);
+      }, Hv = d._emscripten_main_thread_process_queued_calls = function() {
+        return (Hv = d._emscripten_main_thread_process_queued_calls = d.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
       }, yT = d.__emscripten_thread_crashed = function() {
         return (yT = d.__emscripten_thread_crashed = d.asm._emscripten_thread_crashed).apply(null, arguments);
-      }, jv = d.__emscripten_thread_init = function() {
-        return (jv = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
+      }, qv = d.__emscripten_thread_init = function() {
+        return (qv = d.__emscripten_thread_init = d.asm._emscripten_thread_init).apply(null, arguments);
       }, vT = d._emscripten_current_thread_process_queued_calls = function() {
         return (vT = d._emscripten_current_thread_process_queued_calls = d.asm.emscripten_current_thread_process_queued_calls).apply(null, arguments);
       }, xT = d._emscripten_main_browser_thread_id = function() {
         return (xT = d._emscripten_main_browser_thread_id = d.asm.emscripten_main_browser_thread_id).apply(null, arguments);
       }, wT = d._emscripten_sync_run_in_main_thread_2 = function() {
         return (wT = d._emscripten_sync_run_in_main_thread_2 = d.asm.emscripten_sync_run_in_main_thread_2).apply(null, arguments);
-      }, Kv = d._emscripten_sync_run_in_main_thread_4 = function() {
-        return (Kv = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
-      }, Xv = d._emscripten_run_in_main_runtime_thread_js = function() {
-        return (Xv = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
-      }, $f = d._emscripten_dispatch_to_thread_ = function() {
-        return ($f = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
-      }, _f = d.__emscripten_thread_free_data = function() {
-        return (_f = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
+      }, jv = d._emscripten_sync_run_in_main_thread_4 = function() {
+        return (jv = d._emscripten_sync_run_in_main_thread_4 = d.asm.emscripten_sync_run_in_main_thread_4).apply(null, arguments);
+      }, Kv = d._emscripten_run_in_main_runtime_thread_js = function() {
+        return (Kv = d._emscripten_run_in_main_runtime_thread_js = d.asm.emscripten_run_in_main_runtime_thread_js).apply(null, arguments);
+      }, Tf = d._emscripten_dispatch_to_thread_ = function() {
+        return (Tf = d._emscripten_dispatch_to_thread_ = d.asm.emscripten_dispatch_to_thread_).apply(null, arguments);
+      }, $f = d.__emscripten_thread_free_data = function() {
+        return ($f = d.__emscripten_thread_free_data = d.asm._emscripten_thread_free_data).apply(null, arguments);
       }, kT = d.__emscripten_thread_exit = function() {
         return (kT = d.__emscripten_thread_exit = d.asm._emscripten_thread_exit).apply(null, arguments);
       }, IT = d._memalign = function() {
         return (IT = d._memalign = d.asm.memalign).apply(null, arguments);
-      }, Yv = d._emscripten_stack_set_limits = function() {
-        return (Yv = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
-      }, Af = d.stackSave = function() {
-        return (Af = d.stackSave = d.asm.stackSave).apply(null, arguments);
-      }, Pc = d.stackRestore = function() {
-        return (Pc = d.stackRestore = d.asm.stackRestore).apply(null, arguments);
+      }, Xv = d._emscripten_stack_set_limits = function() {
+        return (Xv = d._emscripten_stack_set_limits = d.asm.emscripten_stack_set_limits).apply(null, arguments);
+      }, _f = d.stackSave = function() {
+        return (_f = d.stackSave = d.asm.stackSave).apply(null, arguments);
+      }, Oc = d.stackRestore = function() {
+        return (Oc = d.stackRestore = d.asm.stackRestore).apply(null, arguments);
       }, Ri = d.stackAlloc = function() {
         return (Ri = d.stackAlloc = d.asm.stackAlloc).apply(null, arguments);
       }, ST = d.dynCall_iijjiiii = function() {
         return (ST = d.dynCall_iijjiiii = d.asm.dynCall_iijjiiii).apply(null, arguments);
       }, CT = d.dynCall_jiji = function() {
         return (CT = d.dynCall_jiji = d.asm.dynCall_jiji).apply(null, arguments);
-      }, Qv = d.__emscripten_allow_main_runtime_queued_calls = 21408;
-      d.cwrap = Jt, d.keepRuntimeAlive = Or, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = ku;
-      var zc;
-      function ku(S) {
+      }, Yv = d.__emscripten_allow_main_runtime_queued_calls = 21408;
+      d.cwrap = Jt, d.keepRuntimeAlive = Or, d.PThread = $e, d.PThread = $e, d.wasmMemory = Ce, d.ExitStatus = wu;
+      var Pc;
+      function wu(S) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + S + ")", this.status = S;
       }
       ss = function S() {
-        zc || Ef(), zc || (ss = S);
+        Pc || Af(), Pc || (ss = S);
       };
-      function Ef(S) {
+      function Af(S) {
         if (S = S || b, tr > 0)
           return;
         if (T) {
-          h(d), fu(), postMessage({ cmd: "loaded" });
+          h(d), hu(), postMessage({ cmd: "loaded" });
           return;
         }
         if (nn(), tr > 0)
           return;
         function D() {
-          zc || (zc = true, d.calledRun = true, !rt && (fu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), rh()));
+          Pc || (Pc = true, d.calledRun = true, !rt && (hu(), h(d), d.onRuntimeInitialized && d.onRuntimeInitialized(), sh()));
         }
         d.setStatus ? (d.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
@@ -2509,38 +2509,38 @@ var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js
           }, 1), D();
         }, 1)) : D();
       }
-      d.run = Ef;
+      d.run = Af;
       function NT(S, D) {
         if (Zt = S, !D && T)
-          throw kc(S), "unwind";
-        Or() || sh(), TT(S);
+          throw wc(S), "unwind";
+        Or() || nh(), TT(S);
       }
       function TT(S) {
-        Zt = S, Or() || ($e.terminateAllThreads(), d.onExit && d.onExit(S), rt = true), v(S, new ku(S));
+        Zt = S, Or() || ($e.terminateAllThreads(), d.onExit && d.onExit(S), rt = true), v(S, new wu(S));
       }
       if (d.preInit)
         for (typeof d.preInit == "function" && (d.preInit = [d.preInit]); d.preInit.length > 0; )
           d.preInit.pop()();
-      Ef();
-      var Mc;
-      m && (Mc = { uncaughtException: process.listeners("uncaughtException").filter(function(S) {
+      Af();
+      var zc;
+      m && (zc = { uncaughtException: process.listeners("uncaughtException").filter(function(S) {
         return !m.uncaughtException.indexOf(S) > -1;
       }), unhandledRejection: process.listeners("unhandledRejection").filter(function(S) {
         return !m.unhandledRejection.indexOf(S) > -1;
       }) });
-      var Lc;
+      var Mc;
       if (typeof WasmBackendModule != "undefined")
-        Lc = WasmBackendModule;
+        Mc = WasmBackendModule;
       else if (typeof r != "undefined")
-        Lc = r;
+        Mc = r;
       else
         throw new Error("Could not find wasm module in post.js");
-      if (Mc) {
-        var $T = Lc._dispose;
-        Lc._dispose = function() {
-          $T(), Mc.uncaughtException.forEach(function(S) {
+      if (zc) {
+        var $T = Mc._dispose;
+        Mc._dispose = function() {
+          $T(), zc.uncaughtException.forEach(function(S) {
             process.removeListener("uncaughtException", S);
-          }), Mc.unhandledRejection.forEach(function(S) {
+          }), zc.unhandledRejection.forEach(function(S) {
             process.removeListener("unhandledRejection", S);
           });
         };
@@ -2571,13 +2571,13 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       var y, v, x, k;
       function C(H) {
-        if (H instanceof xu)
+        if (H instanceof vu)
           return;
         R("exiting due to exception: " + H);
       }
       var T, E, A;
-      m ? (f ? g = dd().dirname(g) + "/" : g = __dirname + "/", A = () => {
-        E || (T = eg(), E = dd());
+      m ? (f ? g = cd().dirname(g) + "/" : g = __dirname + "/", A = () => {
+        E || (T = Jm(), E = cd());
       }, y = function(J, de) {
         return A(), J = E.normalize(J), T.readFileSync(J, de ? void 0 : "utf8");
       }, x = (H) => {
@@ -2588,12 +2588,12 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           ke ? de(ke) : J(Qe.buffer);
         });
       }, process.argv.length > 1 && (p = process.argv[1].replace(/\\/g, "/")), c = process.argv.slice(2), process.on("uncaughtException", function(H) {
-        if (!(H instanceof xu))
+        if (!(H instanceof vu))
           throw H;
       }), process.on("unhandledRejection", function(H) {
         throw H;
       }), d = (H, J) => {
-        if (hu())
+        if (pu())
           throw process.exitCode = H, J;
         C(J), process.exit(H);
       }, a.inspect = function() {
@@ -2647,7 +2647,7 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }
       function Y(H, J) {
         for (var de = H; de < H + J; de++) {
-          var ke = gu(de);
+          var ke = mu(de);
           ke && q.set(ke, de);
         }
       }
@@ -2669,12 +2669,12 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         var Ze = { string: function(rn) {
           var nr = 0;
           if (rn != null && rn !== 0) {
-            var Fc = (rn.length << 2) + 1;
-            nr = vu(Fc), tt(rn, nr, Fc);
+            var Dc = (rn.length << 2) + 1;
+            nr = yu(Dc), tt(rn, nr, Dc);
           }
           return nr;
         }, array: function(rn) {
-          var nr = vu(rn.length);
+          var nr = yu(rn.length);
           return rt(rn, nr), nr;
         } };
         function Ve(rn) {
@@ -2684,13 +2684,13 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         if (ke)
           for (var as = 0; as < ke.length; as++) {
             var Ei = Ze[de[as]];
-            Ei ? (rs === 0 && (rs = Rc()), Tt[as] = Ei(ke[as])) : Tt[as] = ke[as];
+            Ei ? (rs === 0 && (rs = Ec()), Tt[as] = Ei(ke[as])) : Tt[as] = ke[as];
           }
         var Lr = ze.apply(null, Tt);
-        function Nf(rn) {
-          return rs !== 0 && Dc(rs), Ve(rn);
+        function Cf(rn) {
+          return rs !== 0 && Rc(rs), Ve(rn);
         }
-        return Lr = Nf(Lr), Lr;
+        return Lr = Cf(Lr), Lr;
       }
       function Se(H, J, de, ke) {
         de = de || [];
@@ -2786,42 +2786,42 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       function Ls(H) {
         In = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Sn = new Int16Array(H), a.HEAP32 = Xt = new Int32Array(H), a.HEAPU8 = Jt = new Uint8Array(H), a.HEAPU16 = Cn = new Uint16Array(H), a.HEAPU32 = Rn = new Uint32Array(H), a.HEAPF32 = en = new Float32Array(H), a.HEAPF64 = Ms = new Float64Array(H);
       }
-      var xi = a.INITIAL_MEMORY || 16777216, Js, Bs = [], pu = [], wi = [], tn = false, ic = false, oc = 0;
-      function hu() {
-        return se || oc > 0;
+      var xi = a.INITIAL_MEMORY || 16777216, Js, Bs = [], du = [], wi = [], tn = false, ac = false, ic = 0;
+      function pu() {
+        return se || ic > 0;
       }
-      function uc() {
+      function oc() {
         if (a.preRun)
           for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
-            dc(a.preRun.shift());
-        mu(Bs);
+            cc(a.preRun.shift());
+        fu(Bs);
+      }
+      function uc() {
+        tn = true, fu(du);
+      }
+      function Ev() {
+        ac = true;
       }
       function lc() {
-        tn = true, mu(pu);
-      }
-      function Rv() {
-        ic = true;
-      }
-      function cc() {
         if (a.postRun)
           for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
-            pc(a.postRun.shift());
-        mu(wi);
+            dc(a.postRun.shift());
+        fu(wi);
       }
-      function dc(H) {
+      function cc(H) {
         Bs.unshift(H);
       }
       function ns(H) {
-        pu.unshift(H);
+        du.unshift(H);
       }
-      function pc(H) {
+      function dc(H) {
         wi.unshift(H);
       }
       var Dn = 0, ki = null, er = null;
-      function th(H) {
+      function eh(H) {
         Dn++, a.monitorRunDependencies && a.monitorRunDependencies(Dn);
       }
-      function hc(H) {
+      function pc(H) {
         if (Dn--, a.monitorRunDependencies && a.monitorRunDependencies(Dn), Dn == 0 && (ki !== null && (clearInterval(ki), ki = null), er)) {
           var J = er;
           er = null, J();
@@ -2833,16 +2833,16 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         var J = new WebAssembly.RuntimeError(H);
         throw o(J), J;
       }
-      var nh = "data:application/octet-stream;base64,";
-      function fc(H) {
-        return H.startsWith(nh);
+      var th = "data:application/octet-stream;base64,";
+      function hc(H) {
+        return H.startsWith(th);
       }
       function Or(H) {
         return H.startsWith("file://");
       }
       var nn;
-      nn = "tfjs-backend-wasm.wasm", fc(nn) || (nn = b(nn));
-      function fu(H) {
+      nn = "tfjs-backend-wasm.wasm", hc(nn) || (nn = b(nn));
+      function hu(H) {
         try {
           if (H == nn && ee)
             return new Uint8Array(ee);
@@ -2853,7 +2853,7 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           Fr(J);
         }
       }
-      function sh() {
+      function nh() {
         if (!ee && (h || f)) {
           if (typeof fetch == "function" && !Or(nn))
             return fetch(nn, { credentials: "same-origin" }).then(function(H) {
@@ -2861,7 +2861,7 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
                 throw "failed to load wasm binary file at '" + nn + "'";
               return H.arrayBuffer();
             }).catch(function() {
-              return fu(nn);
+              return hu(nn);
             });
           if (v)
             return new Promise(function(H, J) {
@@ -2871,21 +2871,21 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             });
         }
         return Promise.resolve().then(function() {
-          return fu(nn);
+          return hu(nn);
         });
       }
-      function rh() {
+      function sh() {
         var H = { env: Ci, wasi_snapshot_preview1: Ci };
         function J(Ve, ze) {
           var Tt = Ve.exports;
-          a.asm = Tt, ne = a.asm.memory, Ls(ne.buffer), Js = a.asm.__indirect_function_table, ns(a.asm.__wasm_call_ctors), hc("wasm-instantiate");
+          a.asm = Tt, ne = a.asm.memory, Ls(ne.buffer), Js = a.asm.__indirect_function_table, ns(a.asm.__wasm_call_ctors), pc("wasm-instantiate");
         }
-        th("wasm-instantiate");
+        eh("wasm-instantiate");
         function de(Ve) {
           J(Ve.instance);
         }
         function ke(Ve) {
-          return sh().then(function(ze) {
+          return nh().then(function(ze) {
             return WebAssembly.instantiate(ze, H);
           }).then(function(ze) {
             return ze;
@@ -2894,7 +2894,7 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           });
         }
         function Qe() {
-          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !fc(nn) && !Or(nn) && typeof fetch == "function" ? fetch(nn, { credentials: "same-origin" }).then(function(Ve) {
+          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !hc(nn) && !Or(nn) && typeof fetch == "function" ? fetch(nn, { credentials: "same-origin" }).then(function(Ve) {
             var ze = WebAssembly.instantiateStreaming(Ve, H);
             return ze.then(de, function(Tt) {
               return R("wasm streaming compile failed: " + Tt), R("falling back to ArrayBuffer instantiation"), ke(de);
@@ -2910,8 +2910,8 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           }
         return Qe().catch(o), {};
       }
-      var Dv, Fv;
-      function mu(H) {
+      var Rv, Dv;
+      function fu(H) {
         for (; H.length > 0; ) {
           var J = H.shift();
           if (typeof J == "function") {
@@ -2919,13 +2919,13 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
             continue;
           }
           var de = J.func;
-          typeof de == "number" ? J.arg === void 0 ? gu(de)() : gu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
+          typeof de == "number" ? J.arg === void 0 ? mu(de)() : mu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
         }
       }
       function tr(H) {
         return H;
       }
-      function mc(H) {
+      function fc(H) {
         var J = /\b_Z[\w\d_]+/g;
         return H.replace(J, function(de) {
           var ke = de;
@@ -2933,11 +2933,11 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         });
       }
       var ss = [];
-      function gu(H) {
+      function mu(H) {
         var J = ss[H];
         return J || (H >= ss.length && (ss.length = H + 1), ss[H] = J = Js.get(H)), J;
       }
-      function Ov() {
+      function Fv() {
         var H = new Error();
         if (!H.stack) {
           try {
@@ -2953,13 +2953,13 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       function Ii(H, J) {
         Js.set(H, J), ss[H] = J;
       }
-      function ah() {
+      function rh() {
         Fr("");
       }
-      function gc(H, J, de) {
+      function mc(H, J, de) {
         Jt.copyWithin(H, J, J + de);
       }
-      function bc() {
+      function gc() {
         return 2147483648;
       }
       function sn(H) {
@@ -2968,10 +2968,10 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         } catch (J) {
         }
       }
-      function yc(H) {
+      function bc(H) {
         var J = Jt.length;
         H = H >>> 0;
-        var de = bc();
+        var de = gc();
         if (H > de)
           return false;
         for (var ke = 1; ke <= 4; ke *= 2) {
@@ -2996,12 +2996,12 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       }, get64: function(H, J) {
         return H;
       } };
-      function ih(H) {
+      function ah(H) {
         return 0;
       }
-      function Pv(H, J, de, ke, Qe) {
+      function Ov(H, J, de, ke, Qe) {
       }
-      function zv(H, J, de, ke) {
+      function Pv(H, J, de, ke) {
         for (var Qe = 0, Ze = 0; Ze < de; Ze++) {
           var Ve = Xt[J >> 2], ze = Xt[J + 4 >> 2];
           J += 8;
@@ -3011,223 +3011,223 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
         }
         return Xt[ke >> 2] = Qe, 0;
       }
-      function oh(H) {
+      function ih(H) {
         te(H);
       }
-      var vc = false, Ci = { abort: ah, emscripten_memcpy_big: gc, emscripten_resize_heap: yc, fd_close: ih, fd_seek: Pv, fd_write: zv, setTempRet0: oh }, zN = rh(), Mv = a.___wasm_call_ctors = function() {
-        return (Mv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
-      }, uh = a._init = function() {
-        return (uh = a._init = a.asm.init).apply(null, arguments);
-      }, lh = a._init_with_threads_count = function() {
-        return (lh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
-      }, xc = a._get_threads_count = function() {
-        return (xc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
-      }, wc = a._register_tensor = function() {
-        return (wc = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
-      }, ch = a._dispose_data = function() {
-        return (ch = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
+      var yc = false, Ci = { abort: rh, emscripten_memcpy_big: mc, emscripten_resize_heap: bc, fd_close: ah, fd_seek: Ov, fd_write: Pv, setTempRet0: ih }, zN = sh(), zv = a.___wasm_call_ctors = function() {
+        return (zv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
+      }, oh = a._init = function() {
+        return (oh = a._init = a.asm.init).apply(null, arguments);
+      }, uh = a._init_with_threads_count = function() {
+        return (uh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
+      }, vc = a._get_threads_count = function() {
+        return (vc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
+      }, xc = a._register_tensor = function() {
+        return (xc = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
+      }, lh = a._dispose_data = function() {
+        return (lh = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
       }, $e = a._dispose = function() {
         return ($e = a._dispose = a.asm.dispose).apply(null, arguments);
-      }, dh = a._Abs = function() {
-        return (dh = a._Abs = a.asm.Abs).apply(null, arguments);
-      }, kc = a._Add = function() {
-        return (kc = a._Add = a.asm.Add).apply(null, arguments);
+      }, ch = a._Abs = function() {
+        return (ch = a._Abs = a.asm.Abs).apply(null, arguments);
+      }, wc = a._Add = function() {
+        return (wc = a._Add = a.asm.Add).apply(null, arguments);
       }, Pr = a._AddN = function() {
         return (Pr = a._AddN = a.asm.AddN).apply(null, arguments);
       }, Ni = a._All = function() {
         return (Ni = a._All = a.asm.All).apply(null, arguments);
-      }, ph = a._Any = function() {
-        return (ph = a._Any = a.asm.Any).apply(null, arguments);
-      }, Lv = a._ArgMax = function() {
-        return (Lv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
-      }, hh = a._AvgPool = function() {
-        return (hh = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
-      }, Bv = a._BatchMatMul = function() {
-        return (Bv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
+      }, dh = a._Any = function() {
+        return (dh = a._Any = a.asm.Any).apply(null, arguments);
+      }, Mv = a._ArgMax = function() {
+        return (Mv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
+      }, ph = a._AvgPool = function() {
+        return (ph = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
+      }, Lv = a._BatchMatMul = function() {
+        return (Lv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
       }, zr = a._Ceil = function() {
         return (zr = a._Ceil = a.asm.Ceil).apply(null, arguments);
-      }, fh = a._ClipByValue = function() {
-        return (fh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
-      }, mh = a._Conv2D = function() {
-        return (mh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
-      }, gh = a._Conv2DBackpropInput = function() {
-        return (gh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
-      }, bh = a._Cos = function() {
-        return (bh = a._Cos = a.asm.Cos).apply(null, arguments);
-      }, yh = a._Cosh = function() {
-        return (yh = a._Cosh = a.asm.Cosh).apply(null, arguments);
-      }, vh = a._CropAndResize = function() {
-        return (vh = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
-      }, Ic = a._Cumsum = function() {
-        return (Ic = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
-      }, xh = a._DepthToSpace = function() {
-        return (xh = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
-      }, wh = a._DepthwiseConv2dNative = function() {
-        return (wh = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
-      }, kh = a._Elu = function() {
-        return (kh = a._Elu = a.asm.Elu).apply(null, arguments);
-      }, Ih = a._Equal = function() {
-        return (Ih = a._Equal = a.asm.Equal).apply(null, arguments);
-      }, Sh = a._Exp = function() {
-        return (Sh = a._Exp = a.asm.Exp).apply(null, arguments);
-      }, Sc = a._FlipLeftRight = function() {
-        return (Sc = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
-      }, Ch = a._Floor = function() {
-        return (Ch = a._Floor = a.asm.Floor).apply(null, arguments);
-      }, Nh = a._FloorDiv = function() {
-        return (Nh = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
+      }, hh = a._ClipByValue = function() {
+        return (hh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
+      }, fh = a._Conv2D = function() {
+        return (fh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
+      }, mh = a._Conv2DBackpropInput = function() {
+        return (mh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
+      }, gh = a._Cos = function() {
+        return (gh = a._Cos = a.asm.Cos).apply(null, arguments);
+      }, bh = a._Cosh = function() {
+        return (bh = a._Cosh = a.asm.Cosh).apply(null, arguments);
+      }, yh = a._CropAndResize = function() {
+        return (yh = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
+      }, kc = a._Cumsum = function() {
+        return (kc = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
+      }, vh = a._DepthToSpace = function() {
+        return (vh = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
+      }, xh = a._DepthwiseConv2dNative = function() {
+        return (xh = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
+      }, wh = a._Elu = function() {
+        return (wh = a._Elu = a.asm.Elu).apply(null, arguments);
+      }, kh = a._Equal = function() {
+        return (kh = a._Equal = a.asm.Equal).apply(null, arguments);
+      }, Ih = a._Exp = function() {
+        return (Ih = a._Exp = a.asm.Exp).apply(null, arguments);
+      }, Ic = a._FlipLeftRight = function() {
+        return (Ic = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
+      }, Sh = a._Floor = function() {
+        return (Sh = a._Floor = a.asm.Floor).apply(null, arguments);
+      }, Ch = a._FloorDiv = function() {
+        return (Ch = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
       }, Mr = a._FusedBatchNorm = function() {
         return (Mr = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
-      }, bu = a._FusedConv2D = function() {
-        return (bu = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
-      }, Th = a._FusedDepthwiseConv2D = function() {
-        return (Th = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
-      }, $h = a._Gather = function() {
-        return ($h = a._Gather = a.asm.Gather).apply(null, arguments);
-      }, _h = a._GatherNd = function() {
-        return (_h = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
+      }, gu = a._FusedConv2D = function() {
+        return (gu = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
+      }, Nh = a._FusedDepthwiseConv2D = function() {
+        return (Nh = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
+      }, Th = a._Gather = function() {
+        return (Th = a._Gather = a.asm.Gather).apply(null, arguments);
+      }, $h = a._GatherNd = function() {
+        return ($h = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
       }, Le = a._Greater = function() {
         return (Le = a._Greater = a.asm.Greater).apply(null, arguments);
-      }, Ah = a._GreaterEqual = function() {
-        return (Ah = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
-      }, Eh = a._LeakyRelu = function() {
-        return (Eh = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
-      }, Rh = a._Less = function() {
-        return (Rh = a._Less = a.asm.Less).apply(null, arguments);
-      }, Dh = a._LessEqual = function() {
-        return (Dh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
-      }, Fh = a._Log = function() {
-        return (Fh = a._Log = a.asm.Log).apply(null, arguments);
-      }, Oh = a._LogicalAnd = function() {
-        return (Oh = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
-      }, yu = a._Max = function() {
-        return (yu = a._Max = a.asm.Max).apply(null, arguments);
-      }, Cc = a._MaxPool = function() {
-        return (Cc = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
-      }, Nc = a._Maximum = function() {
-        return (Nc = a._Maximum = a.asm.Maximum).apply(null, arguments);
-      }, Ph = a._Mean = function() {
-        return (Ph = a._Mean = a.asm.Mean).apply(null, arguments);
-      }, zh = a._Min = function() {
-        return (zh = a._Min = a.asm.Min).apply(null, arguments);
-      }, Mh = a._Minimum = function() {
-        return (Mh = a._Minimum = a.asm.Minimum).apply(null, arguments);
-      }, Lh = a._MirrorPad = function() {
-        return (Lh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
-      }, Bh = a._Multiply = function() {
-        return (Bh = a._Multiply = a.asm.Multiply).apply(null, arguments);
-      }, Vh = a._Neg = function() {
-        return (Vh = a._Neg = a.asm.Neg).apply(null, arguments);
+      }, _h = a._GreaterEqual = function() {
+        return (_h = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
+      }, Ah = a._LeakyRelu = function() {
+        return (Ah = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
+      }, Eh = a._Less = function() {
+        return (Eh = a._Less = a.asm.Less).apply(null, arguments);
+      }, Rh = a._LessEqual = function() {
+        return (Rh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
+      }, Dh = a._Log = function() {
+        return (Dh = a._Log = a.asm.Log).apply(null, arguments);
+      }, Fh = a._LogicalAnd = function() {
+        return (Fh = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
+      }, bu = a._Max = function() {
+        return (bu = a._Max = a.asm.Max).apply(null, arguments);
+      }, Sc = a._MaxPool = function() {
+        return (Sc = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
+      }, Cc = a._Maximum = function() {
+        return (Cc = a._Maximum = a.asm.Maximum).apply(null, arguments);
+      }, Oh = a._Mean = function() {
+        return (Oh = a._Mean = a.asm.Mean).apply(null, arguments);
+      }, Ph = a._Min = function() {
+        return (Ph = a._Min = a.asm.Min).apply(null, arguments);
+      }, zh = a._Minimum = function() {
+        return (zh = a._Minimum = a.asm.Minimum).apply(null, arguments);
+      }, Mh = a._MirrorPad = function() {
+        return (Mh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
+      }, Lh = a._Multiply = function() {
+        return (Lh = a._Multiply = a.asm.Multiply).apply(null, arguments);
+      }, Bh = a._Neg = function() {
+        return (Bh = a._Neg = a.asm.Neg).apply(null, arguments);
       }, gt = a._NonMaxSuppressionV3 = function() {
         return (gt = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
-      }, Wh = a._NonMaxSuppressionV4 = function() {
-        return (Wh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
-      }, Uh = a._NonMaxSuppressionV5 = function() {
-        return (Uh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
-      }, Gh = a._NotEqual = function() {
-        return (Gh = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
+      }, Vh = a._NonMaxSuppressionV4 = function() {
+        return (Vh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
+      }, Wh = a._NonMaxSuppressionV5 = function() {
+        return (Wh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
+      }, Uh = a._NotEqual = function() {
+        return (Uh = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
       }, Ti = a._OneHot = function() {
         return (Ti = a._OneHot = a.asm.OneHot).apply(null, arguments);
-      }, Tc = a._PadV2 = function() {
-        return (Tc = a._PadV2 = a.asm.PadV2).apply(null, arguments);
-      }, $c = a._Pow = function() {
-        return ($c = a._Pow = a.asm.Pow).apply(null, arguments);
-      }, _c = a._Prelu = function() {
-        return (_c = a._Prelu = a.asm.Prelu).apply(null, arguments);
-      }, Hh = a._Prod = function() {
-        return (Hh = a._Prod = a.asm.Prod).apply(null, arguments);
-      }, Ac = a._RealDiv = function() {
-        return (Ac = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
-      }, qh = a._Relu = function() {
-        return (qh = a._Relu = a.asm.Relu).apply(null, arguments);
-      }, Vv = a._Relu6 = function() {
-        return (Vv = a._Relu6 = a.asm.Relu6).apply(null, arguments);
-      }, Ec = a._ResizeBilinear = function() {
-        return (Ec = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
-      }, Wv = a._Reverse = function() {
-        return (Wv = a._Reverse = a.asm.Reverse).apply(null, arguments);
-      }, jh = a._RotateWithOffset = function() {
-        return (jh = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
-      }, Kh = a._Round = function() {
-        return (Kh = a._Round = a.asm.Round).apply(null, arguments);
-      }, Xh = a._Rsqrt = function() {
-        return (Xh = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
-      }, Yh = a._ScatterNd = function() {
-        return (Yh = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
-      }, Qh = a._SelectV2 = function() {
-        return (Qh = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
-      }, Zh = a._Sigmoid = function() {
-        return (Zh = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
-      }, Jh = a._Sin = function() {
-        return (Jh = a._Sin = a.asm.Sin).apply(null, arguments);
-      }, ef = a._Softmax = function() {
-        return (ef = a._Softmax = a.asm.Softmax).apply(null, arguments);
-      }, tf = a._SparseFillEmptyRows = function() {
-        return (tf = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
-      }, nf = a._SparseReshape = function() {
-        return (nf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
-      }, sf = a._SparseSegmentReduction = function() {
-        return (sf = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
-      }, rf = a._Sqrt = function() {
-        return (rf = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
-      }, af = a._Square = function() {
-        return (af = a._Square = a.asm.Square).apply(null, arguments);
-      }, of = a._SquaredDifference = function() {
-        return (of = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
-      }, uf = a._Step = function() {
-        return (uf = a._Step = a.asm.Step).apply(null, arguments);
-      }, lf = a._StridedSlice = function() {
-        return (lf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
-      }, cf = a._Sub = function() {
-        return (cf = a._Sub = a.asm.Sub).apply(null, arguments);
-      }, df = a._Sum = function() {
-        return (df = a._Sum = a.asm.Sum).apply(null, arguments);
-      }, pf = a._Tan = function() {
-        return (pf = a._Tan = a.asm.Tan).apply(null, arguments);
-      }, hf = a._Tanh = function() {
-        return (hf = a._Tanh = a.asm.Tanh).apply(null, arguments);
-      }, ff = a._Tile = function() {
-        return (ff = a._Tile = a.asm.Tile).apply(null, arguments);
-      }, mf = a._TopK = function() {
-        return (mf = a._TopK = a.asm.TopK).apply(null, arguments);
-      }, gf = a._Transform = function() {
-        return (gf = a._Transform = a.asm.Transform).apply(null, arguments);
-      }, bf = a._Transpose = function() {
-        return (bf = a._Transpose = a.asm.Transpose).apply(null, arguments);
-      }, yf = a.__FusedMatMul = function() {
-        return (yf = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
-      }, vf = a._malloc = function() {
-        return (vf = a._malloc = a.asm.malloc).apply(null, arguments);
-      }, xf = a._free = function() {
-        return (xf = a._free = a.asm.free).apply(null, arguments);
-      }, wf = a.___errno_location = function() {
-        return (wf = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
-      }, kf = a._emscripten_main_thread_process_queued_calls = function() {
-        return (kf = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
-      }, Rc = a.stackSave = function() {
-        return (Rc = a.stackSave = a.asm.stackSave).apply(null, arguments);
-      }, Dc = a.stackRestore = function() {
-        return (Dc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
-      }, vu = a.stackAlloc = function() {
-        return (vu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
-      }, If = a.dynCall_iijjiiii = function() {
-        return (If = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
-      }, Sf = a.dynCall_jiji = function() {
-        return (Sf = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
+      }, Nc = a._PadV2 = function() {
+        return (Nc = a._PadV2 = a.asm.PadV2).apply(null, arguments);
+      }, Tc = a._Pow = function() {
+        return (Tc = a._Pow = a.asm.Pow).apply(null, arguments);
+      }, $c = a._Prelu = function() {
+        return ($c = a._Prelu = a.asm.Prelu).apply(null, arguments);
+      }, Gh = a._Prod = function() {
+        return (Gh = a._Prod = a.asm.Prod).apply(null, arguments);
+      }, _c = a._RealDiv = function() {
+        return (_c = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
+      }, Hh = a._Relu = function() {
+        return (Hh = a._Relu = a.asm.Relu).apply(null, arguments);
+      }, Bv = a._Relu6 = function() {
+        return (Bv = a._Relu6 = a.asm.Relu6).apply(null, arguments);
+      }, Ac = a._ResizeBilinear = function() {
+        return (Ac = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
+      }, Vv = a._Reverse = function() {
+        return (Vv = a._Reverse = a.asm.Reverse).apply(null, arguments);
+      }, qh = a._RotateWithOffset = function() {
+        return (qh = a._RotateWithOffset = a.asm.RotateWithOffset).apply(null, arguments);
+      }, jh = a._Round = function() {
+        return (jh = a._Round = a.asm.Round).apply(null, arguments);
+      }, Kh = a._Rsqrt = function() {
+        return (Kh = a._Rsqrt = a.asm.Rsqrt).apply(null, arguments);
+      }, Xh = a._ScatterNd = function() {
+        return (Xh = a._ScatterNd = a.asm.ScatterNd).apply(null, arguments);
+      }, Yh = a._SelectV2 = function() {
+        return (Yh = a._SelectV2 = a.asm.SelectV2).apply(null, arguments);
+      }, Qh = a._Sigmoid = function() {
+        return (Qh = a._Sigmoid = a.asm.Sigmoid).apply(null, arguments);
+      }, Zh = a._Sin = function() {
+        return (Zh = a._Sin = a.asm.Sin).apply(null, arguments);
+      }, Jh = a._Softmax = function() {
+        return (Jh = a._Softmax = a.asm.Softmax).apply(null, arguments);
+      }, ef = a._SparseFillEmptyRows = function() {
+        return (ef = a._SparseFillEmptyRows = a.asm.SparseFillEmptyRows).apply(null, arguments);
+      }, tf = a._SparseReshape = function() {
+        return (tf = a._SparseReshape = a.asm.SparseReshape).apply(null, arguments);
+      }, nf = a._SparseSegmentReduction = function() {
+        return (nf = a._SparseSegmentReduction = a.asm.SparseSegmentReduction).apply(null, arguments);
+      }, sf = a._Sqrt = function() {
+        return (sf = a._Sqrt = a.asm.Sqrt).apply(null, arguments);
+      }, rf = a._Square = function() {
+        return (rf = a._Square = a.asm.Square).apply(null, arguments);
+      }, af = a._SquaredDifference = function() {
+        return (af = a._SquaredDifference = a.asm.SquaredDifference).apply(null, arguments);
+      }, of = a._Step = function() {
+        return (of = a._Step = a.asm.Step).apply(null, arguments);
+      }, uf = a._StridedSlice = function() {
+        return (uf = a._StridedSlice = a.asm.StridedSlice).apply(null, arguments);
+      }, lf = a._Sub = function() {
+        return (lf = a._Sub = a.asm.Sub).apply(null, arguments);
+      }, cf = a._Sum = function() {
+        return (cf = a._Sum = a.asm.Sum).apply(null, arguments);
+      }, df = a._Tan = function() {
+        return (df = a._Tan = a.asm.Tan).apply(null, arguments);
+      }, pf = a._Tanh = function() {
+        return (pf = a._Tanh = a.asm.Tanh).apply(null, arguments);
+      }, hf = a._Tile = function() {
+        return (hf = a._Tile = a.asm.Tile).apply(null, arguments);
+      }, ff = a._TopK = function() {
+        return (ff = a._TopK = a.asm.TopK).apply(null, arguments);
+      }, mf = a._Transform = function() {
+        return (mf = a._Transform = a.asm.Transform).apply(null, arguments);
+      }, gf = a._Transpose = function() {
+        return (gf = a._Transpose = a.asm.Transpose).apply(null, arguments);
+      }, bf = a.__FusedMatMul = function() {
+        return (bf = a.__FusedMatMul = a.asm._FusedMatMul).apply(null, arguments);
+      }, yf = a._malloc = function() {
+        return (yf = a._malloc = a.asm.malloc).apply(null, arguments);
+      }, vf = a._free = function() {
+        return (vf = a._free = a.asm.free).apply(null, arguments);
+      }, xf = a.___errno_location = function() {
+        return (xf = a.___errno_location = a.asm.__errno_location).apply(null, arguments);
+      }, wf = a._emscripten_main_thread_process_queued_calls = function() {
+        return (wf = a._emscripten_main_thread_process_queued_calls = a.asm.emscripten_main_thread_process_queued_calls).apply(null, arguments);
+      }, Ec = a.stackSave = function() {
+        return (Ec = a.stackSave = a.asm.stackSave).apply(null, arguments);
+      }, Rc = a.stackRestore = function() {
+        return (Rc = a.stackRestore = a.asm.stackRestore).apply(null, arguments);
+      }, yu = a.stackAlloc = function() {
+        return (yu = a.stackAlloc = a.asm.stackAlloc).apply(null, arguments);
+      }, kf = a.dynCall_iijjiiii = function() {
+        return (kf = a.dynCall_iijjiiii = a.asm.dynCall_iijjiiii).apply(null, arguments);
+      }, If = a.dynCall_jiji = function() {
+        return (If = a.dynCall_jiji = a.asm.dynCall_jiji).apply(null, arguments);
       };
       a.cwrap = Se;
       var $i;
-      function xu(H) {
+      function vu(H) {
         this.name = "ExitStatus", this.message = "Program terminated with exit(" + H + ")", this.status = H;
       }
       er = function H() {
-        $i || wu(), $i || (er = H);
+        $i || xu(), $i || (er = H);
       };
-      function wu(H) {
-        if (H = H || c, Dn > 0 || (uc(), Dn > 0))
+      function xu(H) {
+        if (H = H || c, Dn > 0 || (oc(), Dn > 0))
           return;
         function J() {
-          $i || ($i = true, a.calledRun = true, !oe && (lc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), cc()));
+          $i || ($i = true, a.calledRun = true, !oe && (uc(), i(a), a.onRuntimeInitialized && a.onRuntimeInitialized(), lc()));
         }
         a.setStatus ? (a.setStatus("Running..."), setTimeout(function() {
           setTimeout(function() {
@@ -3235,14 +3235,14 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
           }, 1), J();
         }, 1)) : J();
       }
-      a.run = wu;
-      function Uv(H) {
-        re = H, hu() || (a.onExit && a.onExit(H), oe = true), d(H, new xu(H));
+      a.run = xu;
+      function Wv(H) {
+        re = H, pu() || (a.onExit && a.onExit(H), oe = true), d(H, new vu(H));
       }
       if (a.preInit)
         for (typeof a.preInit == "function" && (a.preInit = [a.preInit]); a.preInit.length > 0; )
           a.preInit.pop()();
-      wu();
+      xu();
       var _i;
       u && (_i = { uncaughtException: process.listeners("uncaughtException").filter(function(H) {
         return !u.uncaughtException.indexOf(H) > -1;
@@ -3257,9 +3257,9 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
       else
         throw new Error("Could not find wasm module in post.js");
       if (_i) {
-        var Cf = Ai._dispose;
+        var Sf = Ai._dispose;
         Ai._dispose = function() {
-          Cf(), _i.uncaughtException.forEach(function(H) {
+          Sf(), _i.uncaughtException.forEach(function(H) {
             process.removeListener("uncaughtException", H);
           }), _i.unhandledRejection.forEach(function(H) {
             process.removeListener("unhandledRejection", H);
@@ -3275,7 +3275,7 @@ var QT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js"(e, t) {
 } });
 var ZT = 1e-7;
 var JT = 1e-4;
-var Ud = class {
+var Wd = class {
   constructor(e, t) {
     this.backend = e, this.dataMover = t, this.data = /* @__PURE__ */ new WeakMap(), this.dataIdsCount = 0;
   }
@@ -3295,7 +3295,7 @@ var Ud = class {
     return this.dataIdsCount;
   }
 };
-var nl = class {
+var tl = class {
   refCount(e) {
     return Fn("refCount");
   }
@@ -3348,22 +3348,22 @@ function Fn(e) {
 function zw(e) {
   let t = e.length, n = 0;
   for (; t > 0; )
-    n = Math.random() * t | 0, t--, pd(e, t, n);
+    n = Math.random() * t | 0, t--, dd(e, t, n);
 }
 function e$(e, t) {
   if (e.length !== t.length)
     throw new Error(`Array sizes must match to be shuffled together First array length was ${e.length}Second array length was ${t.length}`);
   let n = e.length, s = 0;
   for (; n > 0; )
-    s = Math.random() * n | 0, n--, pd(e, n, s), pd(t, n, s);
+    s = Math.random() * n | 0, n--, dd(e, n, s), dd(t, n, s);
 }
-function Vu(e, t, n) {
+function Bu(e, t, n) {
   return Math.max(e, Math.min(t, n));
 }
 function t$(e) {
   return e % 2 === 0 ? e : e + 1;
 }
-function pd(e, t, n) {
+function dd(e, t, n) {
   let s = e[t];
   e[t] = e[n], e[n] = s;
 }
@@ -3424,7 +3424,7 @@ function wr(e, t) {
       return false;
   return true;
 }
-function Yi(e) {
+function Xi(e) {
   return e % 1 === 0;
 }
 function i$(e) {
@@ -3449,7 +3449,7 @@ function u$(e) {
     t[n] = n;
   return zw(t), t;
 }
-function zu(e, t) {
+function Pu(e, t) {
   return t <= e.length ? e : e + " ".repeat(t - e.length);
 }
 function l$(e, t = (s) => 0, n) {
@@ -3495,7 +3495,7 @@ function c$(e, t) {
 }
 function Jn(e, t) {
   let n = t.length;
-  return e = e == null ? t.map((s, r) => r) : [].concat(e), O(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), O(e.every((s) => Yi(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
+  return e = e == null ? t.map((s, r) => r) : [].concat(e), O(e.every((s) => s >= -n && s < n), () => `All values in axis param must be in range [-${n}, ${n}) but got axis ${e}`), O(e.every((s) => Xi(s)), () => `All values in axis param must be integers but got axis ${e}`), e.map((s) => s < 0 ? n + s : s);
 }
 function Mw(e, t) {
   let n = [], s = [], r = t != null && Array.isArray(t) && t.length === 0, a = t == null || r ? null : Jn(t, e).sort(), i = 0;
@@ -3551,7 +3551,7 @@ function d$(e, t) {
 function Yt(e) {
   return e instanceof Float32Array || e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray;
 }
-function Kf(e) {
+function jf(e) {
   if (e === "float32" || e === "int32")
     return 4;
   if (e === "complex64")
@@ -3575,19 +3575,19 @@ function Gw(e) {
 function Hw(e) {
   return typeof e == "number";
 }
-function Gd(e) {
-  return Array.isArray(e) ? Gd(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : Hw(e) ? "float32" : ir(e) ? "string" : Gw(e) ? "bool" : "float32";
+function Ud(e) {
+  return Array.isArray(e) ? Ud(e[0]) : e instanceof Float32Array ? "float32" : e instanceof Int32Array || e instanceof Uint8Array || e instanceof Uint8ClampedArray ? "int32" : Hw(e) ? "float32" : ir(e) ? "string" : Gw(e) ? "bool" : "float32";
 }
 function hr(e) {
   return !!(e && e.constructor && e.call && e.apply);
 }
-function hd(e, t) {
+function pd(e, t) {
   for (let n = t; n < e; ++n)
     if (e % n === 0)
       return n;
   return e;
 }
-function ao(e) {
+function ro(e) {
   let t = e.length;
   if (t < 2)
     return [];
@@ -3610,7 +3610,7 @@ function qw(e, t, n, s = false) {
   }
   return r;
 }
-function Hi(e, t, n = false) {
+function Gi(e, t, n = false) {
   if (e.length === 0)
     return t[0];
   let s = e.reduce((r, a) => r * a) * (n ? 2 : 1);
@@ -3620,13 +3620,13 @@ function Hi(e, t, n = false) {
     throw new Error(`[${e}] does not match the input size ${t.length}${n ? " for a complex tensor" : ""}.`);
   return qw(0, e, t, n);
 }
-function tg(e, t) {
-  let n = Hd(e, t);
+function eg(e, t) {
+  let n = Gd(e, t);
   for (let s = 0; s < n.length; s++)
     n[s] = 1;
   return n;
 }
-function Hd(e, t) {
+function Gd(e, t) {
   if (t == null || t === "float32" || t === "complex64")
     return new Float32Array(e);
   if (t === "int32")
@@ -3638,14 +3638,14 @@ function Hd(e, t) {
 function p$(e, t) {
   let n = e.reduce((s, r) => s * r, 1);
   if (t == null || t === "float32")
-    return Hi(e, new Float32Array(n));
+    return Gi(e, new Float32Array(n));
   if (t === "int32")
-    return Hi(e, new Int32Array(n));
+    return Gi(e, new Int32Array(n));
   if (t === "bool")
-    return Hi(e, new Uint8Array(n));
+    return Gi(e, new Uint8Array(n));
   throw new Error(`Unknown data type ${t}`);
 }
-function ng(e) {
+function tg(e) {
   e.forEach((t) => {
     O(Number.isInteger(t) && t >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${e}].`);
   });
@@ -3670,10 +3670,10 @@ function f$(e, t, n) {
     s[r] = Math.floor(e / n[r]), e -= s[r] * n[r];
   return s[s.length - 1] = e, s;
 }
-function sg(e) {
+function ng(e) {
   return e && e.then && typeof e.then == "function";
 }
-var Jv = "tfjsflags";
+var Zv = "tfjsflags";
 var m$ = class {
   constructor(e) {
     this.global = e, this.flags = {}, this.flagRegistry = {}, this.urlFlags = {}, this.getQueryParams = g$, this.populateURLFlags();
@@ -3694,7 +3694,7 @@ var m$ = class {
     if (e in this.flags)
       return this.flags[e];
     let t = this.evaluateFlag(e);
-    if (sg(t))
+    if (ng(t))
       throw new Error(`Flag ${e} cannot be synchronously evaluated. Please use getAsync() instead.`);
     return this.flags[e] = t, this.flags[e];
   }
@@ -3730,7 +3730,7 @@ var m$ = class {
     if (typeof this.global == "undefined" || typeof this.global.location == "undefined" || typeof this.global.location.search == "undefined")
       return;
     let e = this.getQueryParams(this.global.location.search);
-    Jv in e && e[Jv].split(",").forEach((n) => {
+    Zv in e && e[Zv].split(",").forEach((n) => {
       let [s, r] = n.split(":");
       this.urlFlags[s] = y$(s, r);
     });
@@ -3757,9 +3757,9 @@ var jw = null;
 function v$(e) {
   jw = e;
 }
-var Rf;
+var Ef;
 function Kw() {
-  if (Rf == null) {
+  if (Ef == null) {
     let e;
     if (typeof window != "undefined")
       e = window;
@@ -3771,15 +3771,15 @@ function Kw() {
       e = self;
     else
       throw new Error("Could not find a global object");
-    Rf = e;
+    Ef = e;
   }
-  return Rf;
+  return Ef;
 }
 function x$() {
   let e = Kw();
   return e._tfGlobals == null && (e._tfGlobals = /* @__PURE__ */ new Map()), e._tfGlobals;
 }
-function rg(e, t) {
+function sg(e, t) {
   let n = x$();
   if (n.has(e))
     return n.get(e);
@@ -3788,170 +3788,170 @@ function rg(e, t) {
     return n.set(e, s), n.get(e);
   }
 }
-var io = "Abs";
-var sl = "Acos";
-var rl = "Acosh";
+var ao = "Abs";
+var nl = "Acos";
+var sl = "Acosh";
 var kr = "Add";
 var xa = "AddN";
-var al = "All";
-var il = "Any";
+var rl = "All";
+var al = "Any";
 var wa = "ArgMax";
-var ol = "ArgMin";
-var ul = "Asin";
-var ll = "Asinh";
-var cl = "Atan";
-var dl = "Atanh";
-var pl = "Atan2";
+var il = "ArgMin";
+var ol = "Asin";
+var ul = "Asinh";
+var ll = "Atan";
+var cl = "Atanh";
+var dl = "Atan2";
 var ka = "AvgPool";
-var ag = "AvgPoolGrad";
-var qd = "AvgPool3D";
-var ig = "AvgPool3DGrad";
+var rg = "AvgPoolGrad";
+var Hd = "AvgPool3D";
+var ag = "AvgPool3DGrad";
 var Ia = "BatchMatMul";
-var oo = "BatchToSpaceND";
-var og = "Bincount";
+var io = "BatchToSpaceND";
+var ig = "Bincount";
 var w$ = "BroadcastTo";
-var ug = "BroadcastArgs";
+var og = "BroadcastArgs";
 var Sa = "Cast";
 var Ca = "Ceil";
 var Ir = "ClipByValue";
-var jd = "Complex";
-var Kd = "ComplexAbs";
-var uo = "Concat";
+var qd = "Complex";
+var jd = "ComplexAbs";
+var oo = "Concat";
 var Na = "Conv2D";
-var lg = "Conv2DBackpropFilter";
+var ug = "Conv2DBackpropFilter";
 var Ta = "Conv2DBackpropInput";
-var Xd = "Conv3D";
-var cg = "Conv3DBackpropFilterV2";
-var dg = "Conv3DBackpropInputV2";
+var Kd = "Conv3D";
+var lg = "Conv3DBackpropFilterV2";
+var cg = "Conv3DBackpropInputV2";
 var $a = "Cos";
 var _a = "Cosh";
-var lo = "Cumsum";
-var co = "CropAndResize";
-var pg = "DenseBincount";
-var po = "DepthToSpace";
+var uo = "Cumsum";
+var lo = "CropAndResize";
+var dg = "DenseBincount";
+var co = "DepthToSpace";
 var Aa = "DepthwiseConv2dNative";
-var hg = "DepthwiseConv2dNativeBackpropFilter";
-var fg = "DepthwiseConv2dNativeBackpropInput";
-var mg = "Diag";
-var Yd = "Dilation2D";
-var Xf = "Dilation2DBackpropInput";
-var Yf = "Dilation2DBackpropFilter";
+var pg = "DepthwiseConv2dNativeBackpropFilter";
+var hg = "DepthwiseConv2dNativeBackpropInput";
+var fg = "Diag";
+var Xd = "Dilation2D";
+var Kf = "Dilation2DBackpropInput";
+var Xf = "Dilation2DBackpropFilter";
 var Ea = "RealDiv";
-var Qd = "Einsum";
+var Yd = "Einsum";
 var Ra = "Elu";
-var gg = "EluGrad";
-var hl = "Erf";
-var ho = "Equal";
+var mg = "EluGrad";
+var pl = "Erf";
+var po = "Equal";
 var Da = "Exp";
-var fo = "ExpandDims";
-var mo = "Expm1";
-var bg = "FFT";
-var fl = "Fill";
-var go = "FlipLeftRight";
+var ho = "ExpandDims";
+var fo = "Expm1";
+var gg = "FFT";
+var hl = "Fill";
+var mo = "FlipLeftRight";
 var Fa = "Floor";
 var Oa = "FloorDiv";
 var Pa = "FusedBatchNorm";
-var bo = "GatherV2";
-var yo = "GatherNd";
-var vo = "Greater";
+var go = "GatherV2";
+var bo = "GatherNd";
+var yo = "Greater";
 var za = "GreaterEqual";
 var Ma = "Identity";
-var yg = "IFFT";
-var Zd = "Imag";
-var ml = "IsFinite";
-var gl = "IsInf";
-var bl = "IsNan";
+var bg = "IFFT";
+var Qd = "Imag";
+var fl = "IsFinite";
+var ml = "IsInf";
+var gl = "IsNan";
 var La = "LeakyRelu";
-var xo = "Less";
-var wo = "LessEqual";
-var vg = "LinSpace";
+var vo = "Less";
+var xo = "LessEqual";
+var yg = "LinSpace";
 var Ba = "Log";
-var yl = "Log1p";
-var ko = "LogicalAnd";
-var vl = "LogicalNot";
-var Jd = "LogicalOr";
+var bl = "Log1p";
+var wo = "LogicalAnd";
+var yl = "LogicalNot";
+var Zd = "LogicalOr";
 var k$ = "LogSoftmax";
-var ep = "LRN";
-var xg = "LRNGrad";
+var Jd = "LRN";
+var vg = "LRNGrad";
 var Va = "Max";
 var Wa = "Maximum";
 var Ua = "MaxPool";
-var wg = "MaxPoolGrad";
-var tp = "MaxPool3D";
-var kg = "MaxPool3DGrad";
-var Ig = "MaxPoolWithArgmax";
+var xg = "MaxPoolGrad";
+var ep = "MaxPool3D";
+var wg = "MaxPool3DGrad";
+var kg = "MaxPoolWithArgmax";
 var Ga = "Mean";
 var Ha = "Min";
 var qa = "Minimum";
 var ja = "MirrorPad";
-var xl = "Mod";
-var Sg = "Multinomial";
+var vl = "Mod";
+var Ig = "Multinomial";
 var Ka = "Multiply";
-var Io = "Neg";
-var So = "NotEqual";
-var Co = "NonMaxSuppressionV3";
-var wl = "NonMaxSuppressionV4";
-var No = "NonMaxSuppressionV5";
-var To = "OnesLike";
-var $o = "OneHot";
-var _o = "Pack";
+var ko = "Neg";
+var Io = "NotEqual";
+var So = "NonMaxSuppressionV3";
+var xl = "NonMaxSuppressionV4";
+var Co = "NonMaxSuppressionV5";
+var No = "OnesLike";
+var To = "OneHot";
+var $o = "Pack";
 var Xa = "PadV2";
 var pde = "Pool";
 var Ya = "Pow";
 var Qa = "Prelu";
-var Ao = "Prod";
-var kl = "Range";
-var np = "Real";
-var Il = "Reciprocal";
+var _o = "Prod";
+var wl = "Range";
+var tp = "Real";
+var kl = "Reciprocal";
 var Za = "Relu";
-var Eo = "Reshape";
-var Sl = "ResizeNearestNeighbor";
-var Cg = "ResizeNearestNeighborGrad";
+var Ao = "Reshape";
+var Il = "ResizeNearestNeighbor";
+var Sg = "ResizeNearestNeighborGrad";
 var Ja = "ResizeBilinear";
-var Ng = "ResizeBilinearGrad";
+var Cg = "ResizeBilinearGrad";
 var ei = "Relu6";
-var Ro = "Reverse";
-var Do = "Round";
+var Eo = "Reverse";
+var Ro = "Round";
 var ti = "Rsqrt";
-var Fo = "ScatterNd";
-var Oo = "Select";
-var Cl = "Selu";
-var Po = "Slice";
+var Do = "ScatterNd";
+var Fo = "Select";
+var Sl = "Selu";
+var Oo = "Slice";
 var ni = "Sin";
-var zo = "Sinh";
-var Nl = "Sign";
+var Po = "Sinh";
+var Cl = "Sign";
 var si = "Sigmoid";
-var Tl = "Softplus";
+var Nl = "Softplus";
 var ri = "Sqrt";
 var ai = "Sum";
-var Mo = "SpaceToBatchND";
-var Lo = "SplitV";
+var zo = "SpaceToBatchND";
+var Mo = "SplitV";
 var ii = "Softmax";
-var sp = "SparseFillEmptyRows";
-var $l = "SparseReshape";
-var rp = "SparseSegmentMean";
-var ap = "SparseSegmentSum";
-var ip = "SparseToDense";
+var np = "SparseFillEmptyRows";
+var Tl = "SparseReshape";
+var sp = "SparseSegmentMean";
+var rp = "SparseSegmentSum";
+var ap = "SparseToDense";
 var oi = "SquaredDifference";
-var _l = "Square";
-var Bo = "StridedSlice";
-var op = "StringNGrams";
-var Tg = "StringSplit";
-var $g = "StringToHashBucketFast";
+var $l = "Square";
+var Lo = "StridedSlice";
+var ip = "StringNGrams";
+var Ng = "StringSplit";
+var Tg = "StringToHashBucketFast";
 var ui = "Sub";
-var Vo = "Tan";
+var Bo = "Tan";
 var li = "Tanh";
 var Sr = "Tile";
-var Wo = "TopK";
-var Uo = "Transform";
+var Vo = "TopK";
+var Wo = "Transform";
 var ci = "Transpose";
-var _g = "Unique";
-var Go = "Unpack";
-var up = "UnsortedSegmentSum";
-var Ho = "ZerosLike";
+var $g = "Unique";
+var Uo = "Unpack";
+var op = "UnsortedSegmentSum";
+var Go = "ZerosLike";
 var di = "Step";
-var fd = "FromPixels";
-var qo = "RotateWithOffset";
+var hd = "FromPixels";
+var Ho = "RotateWithOffset";
 var na = "_FusedMatMul";
 var sa = "FusedConv2D";
 var ra = "FusedDepthwiseConv2D";
@@ -3961,17 +3961,17 @@ function ar(...e) {
 function I$(...e) {
   X().getBool("IS_TEST") || X().getBool("PROD") || console.log(...e);
 }
-var Qi = rg("kernelRegistry", () => /* @__PURE__ */ new Map());
-var Wu = rg("gradRegistry", () => /* @__PURE__ */ new Map());
-function Qf(e, t) {
-  let n = Ag(e, t);
-  return Qi.get(n);
+var Yi = sg("kernelRegistry", () => /* @__PURE__ */ new Map());
+var Vu = sg("gradRegistry", () => /* @__PURE__ */ new Map());
+function Yf(e, t) {
+  let n = _g(e, t);
+  return Yi.get(n);
 }
-function ex(e) {
-  return Wu.get(e);
+function Jv(e) {
+  return Vu.get(e);
 }
-function Zf(e) {
-  let t = Qi.entries(), n = [];
+function Qf(e) {
+  let t = Yi.entries(), n = [];
   for (; ; ) {
     let { done: s, value: r } = t.next();
     if (s)
@@ -3981,45 +3981,45 @@ function Zf(e) {
   }
   return n;
 }
-function Al(e) {
-  let { kernelName: t, backendName: n } = e, s = Ag(t, n);
-  Qi.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), Qi.set(s, e);
+function _l(e) {
+  let { kernelName: t, backendName: n } = e, s = _g(t, n);
+  Yi.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), Yi.set(s, e);
 }
 function S$(e) {
   let { kernelName: t } = e;
-  Wu.has(t) && X().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), Wu.set(t, e);
+  Vu.has(t) && X().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), Vu.set(t, e);
 }
 function hde(e, t) {
-  let n = Ag(e, t);
-  if (!Qi.has(n))
+  let n = _g(e, t);
+  if (!Yi.has(n))
     throw new Error(`The kernel '${e}' for backend '${t}' is not registered`);
-  Qi.delete(n);
+  Yi.delete(n);
 }
 function fde(e) {
-  if (!Wu.has(e))
+  if (!Vu.has(e))
     throw new Error(`The gradient '${e}' for backend is not registered`);
-  Wu.delete(e);
+  Vu.delete(e);
 }
 function mde(e, t) {
-  Zf(e).forEach((s) => {
+  Qf(e).forEach((s) => {
     let r = Object.assign({}, s, { backendName: t });
-    Al(r);
+    _l(r);
   });
 }
-function Ag(e, t) {
+function _g(e, t) {
   return `${t}_${e}`;
 }
 var w = {};
-Ae(w, { arraysEqual: () => wr, assert: () => O, assertNonNegativeIntegerDimensions: () => ng, assertNonNull: () => va, assertShapesMatch: () => dn, bytesFromStringArray: () => Uw, bytesPerElement: () => Kf, checkConversionForErrors: () => Vw, clamp: () => Vu, computeStrides: () => ao, createScalarValue: () => A$, createShuffledIndices: () => u$, decodeString: () => md, distSquared: () => r$, encodeString: () => Rl, fetch: () => R$, fingerPrint64: () => _$, flatten: () => ta, getArrayFromDType: () => Bw, getTypedArrayFromDType: () => Lw, hasEncodingLoss: () => d$, hexToLong: () => El, indexToLoc: () => f$, inferDtype: () => Gd, inferFromImplicitShape: () => c$, isBoolean: () => Gw, isFunction: () => hr, isInt: () => Yi, isNumber: () => Hw, isPromise: () => sg, isScalarShape: () => a$, isString: () => ir, isTypedArray: () => Yt, isValidDtype: () => Ww, locToIndex: () => h$, makeOnesTypedArray: () => tg, makeZerosNestedTypedArray: () => p$, makeZerosTypedArray: () => Hd, nearestDivisor: () => hd, nearestLargerEven: () => t$, now: () => Uu, parseAxisParam: () => Jn, randUniform: () => s$, repeatedTry: () => l$, rightPad: () => zu, shuffle: () => zw, shuffleCombo: () => e$, sizeFromShape: () => pt, sizeToSquarishShape: () => o$, squeezeShape: () => Mw, sum: () => n$, swap: () => pd, tanh: () => i$, toNestedArray: () => Hi, toTypedArray: () => lp });
-var tx = ya(PT());
-var Gr = tx.default || tx;
-function El(e) {
+Ae(w, { arraysEqual: () => wr, assert: () => O, assertNonNegativeIntegerDimensions: () => tg, assertNonNull: () => va, assertShapesMatch: () => dn, bytesFromStringArray: () => Uw, bytesPerElement: () => jf, checkConversionForErrors: () => Vw, clamp: () => Bu, computeStrides: () => ro, createScalarValue: () => A$, createShuffledIndices: () => u$, decodeString: () => fd, distSquared: () => r$, encodeString: () => El, fetch: () => R$, fingerPrint64: () => _$, flatten: () => ta, getArrayFromDType: () => Bw, getTypedArrayFromDType: () => Lw, hasEncodingLoss: () => d$, hexToLong: () => Al, indexToLoc: () => f$, inferDtype: () => Ud, inferFromImplicitShape: () => c$, isBoolean: () => Gw, isFunction: () => hr, isInt: () => Xi, isNumber: () => Hw, isPromise: () => ng, isScalarShape: () => a$, isString: () => ir, isTypedArray: () => Yt, isValidDtype: () => Ww, locToIndex: () => h$, makeOnesTypedArray: () => eg, makeZerosNestedTypedArray: () => p$, makeZerosTypedArray: () => Gd, nearestDivisor: () => pd, nearestLargerEven: () => t$, now: () => Wu, parseAxisParam: () => Jn, randUniform: () => s$, repeatedTry: () => l$, rightPad: () => Pu, shuffle: () => zw, shuffleCombo: () => e$, sizeFromShape: () => pt, sizeToSquarishShape: () => o$, squeezeShape: () => Mw, sum: () => n$, swap: () => dd, tanh: () => i$, toNestedArray: () => Gi, toTypedArray: () => up });
+var ex = ya(PT());
+var Gr = ex.default || ex;
+function Al(e) {
   return Gr.fromString(e, true, 16);
 }
-var Xw = El("c3a5c85c97cb3127");
-var Ur = El("b492b66fbe98f273");
-var an = El("9ae16a3b2f90404f");
-function Jf(e) {
+var Xw = Al("c3a5c85c97cb3127");
+var Ur = Al("b492b66fbe98f273");
+var an = Al("9ae16a3b2f90404f");
+function Zf(e) {
   return e.xor(e.shru(47));
 }
 function Yw(e, t, n) {
@@ -4029,13 +4029,13 @@ function Yw(e, t, n) {
 function lt(e, t) {
   return Yw(e, t, 8);
 }
-function nx(e, t) {
+function tx(e, t) {
   return Yw(e, t, 4);
 }
 function Lt(e, t) {
   return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
 }
-function ur(e, t, n = El("9ddfea08eb382d69")) {
+function ur(e, t, n = Al("9ddfea08eb382d69")) {
   let s = e.xor(t).mul(n);
   s = s.xor(s.shru(47));
   let r = t.xor(s).mul(n);
@@ -4046,7 +4046,7 @@ function C$(e, t, n, s, r, a) {
   let i = r;
   return r = r.add(t), r = r.add(n), a = a.add(Lt(r, 44)), [r.add(s), a.add(i)];
 }
-function Vc(e, t, n, s) {
+function Bc(e, t, n, s) {
   return C$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
 }
 function N$(e, t = e.length) {
@@ -4055,12 +4055,12 @@ function N$(e, t = e.length) {
     return ur(a, i, n);
   }
   if (t >= 4) {
-    let n = an.add(t * 2), s = nx(e, 0);
-    return ur(s.shl(3).add(t), nx(e, t - 4), n);
+    let n = an.add(t * 2), s = tx(e, 0);
+    return ur(s.shl(3).add(t), tx(e, t - 4), n);
   }
   if (t > 0) {
     let n = e[0], s = e[t >> 1], r = e[t - 1], a = n + (s << 8), i = t + (r << 2);
-    return Jf(an.mul(a).xor(Xw.mul(i))).mul(an);
+    return Zf(an.mul(a).xor(Xw.mul(i))).mul(an);
   }
   return an;
 }
@@ -4078,22 +4078,22 @@ function _$(e, t = e.length) {
     return t <= 16 ? N$(e, t) : T$(e, t);
   if (t <= 64)
     return $$(e, t);
-  let s = n, r = n.mul(Ur).add(113), a = Jf(r.mul(an).add(113)).mul(an), i = [Gr.UZERO, Gr.UZERO], o = [Gr.UZERO, Gr.UZERO];
+  let s = n, r = n.mul(Ur).add(113), a = Zf(r.mul(an).add(113)).mul(an), i = [Gr.UZERO, Gr.UZERO], o = [Gr.UZERO, Gr.UZERO];
   s = s.mul(an).add(lt(e, 0));
   let u = 0, l = (t - 1 >> 6) * 64, c = l + (t - 1 & 63) - 63;
   do
-    s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(Ur), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(Ur), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(Ur), i = Vc(e, u, i[1].mul(Ur), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
+    s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(Ur), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(Ur), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(Ur), i = Bc(e, u, i[1].mul(Ur), s.add(o[0])), o = Bc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
   while (u !== l);
   let p = Ur.add(a.and(255).shl(1));
-  return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Vc(e, u, i[1].mul(p), s.add(o[0])), o = Vc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], ur(ur(i[0], o[0], p).add(Jf(r).mul(Xw)).add(a), ur(i[1], o[1], p).add(s), p);
+  return u = c, o[0] = o[0].add(t - 1 & 63), i[0] = i[0].add(o[0]), o[0] = o[0].add(i[0]), s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(p), s = s.xor(o[1].mul(9)), r = r.add(i[0].mul(9).add(lt(e, u + 40))), a = Lt(a.add(o[0]), 33).mul(p), i = Bc(e, u, i[1].mul(p), s.add(o[0])), o = Bc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], ur(ur(i[0], o[0], p).add(Zf(r).mul(Xw)).add(a), ur(i[1], o[1], p).add(s), p);
 }
 function A$(e, t) {
-  return t === "string" ? Rl(e) : lp([e], t);
+  return t === "string" ? El(e) : up([e], t);
 }
 function E$(e, t) {
   return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
 }
-function lp(e, t) {
+function up(e, t) {
   if (t === "string")
     throw new Error("Cannot convert a string[] to a TypedArray");
   if (Array.isArray(e) && (e = ta(e)), X().getBool("DEBUG") && Vw(e, t), E$(e, t))
@@ -4110,16 +4110,16 @@ function lp(e, t) {
   } else
     throw new Error(`Unknown data type ${t}`);
 }
-function Uu() {
+function Wu() {
   return X().platform.now();
 }
 function R$(e, t) {
   return X().platform.fetch(e, t);
 }
-function Rl(e, t = "utf-8") {
+function El(e, t = "utf-8") {
   return t = t || "utf-8", X().platform.encode(e, t);
 }
-function md(e, t = "utf-8") {
+function fd(e, t = "utf-8") {
   return t = t || "utf-8", X().platform.decode(e, t);
 }
 var D$ = class {
@@ -4129,14 +4129,14 @@ var D$ = class {
   profileKernel(e, t, n) {
     let s, r = () => {
       s = n();
-    }, a, i = Uu();
+    }, a, i = Wu();
     if (this.backendTimer.timerAvailable())
       a = this.backendTimer.time(r);
     else {
       r();
       for (let u of s)
         u.dataSync();
-      a = Promise.resolve({ kernelMs: Uu() - i });
+      a = Promise.resolve({ kernelMs: Wu() - i });
     }
     if (X().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
       for (let u = 0; u < s.length; u++) {
@@ -4168,7 +4168,7 @@ function F$(e, t, n) {
 }
 var O$ = class {
   logKernelProfile(e, t, n, s, r, a) {
-    let i = typeof s == "number" ? zu(`${s}ms`, 9) : s.error, o = zu(e, 25), u = t.rank, l = t.size, c = zu(t.shape.toString(), 14), p = "";
+    let i = typeof s == "number" ? Pu(`${s}ms`, 9) : s.error, o = Pu(e, 25), u = t.rank, l = t.size, c = Pu(t.shape.toString(), 14), p = "";
     for (let d in r) {
       let h = r[d];
       if (h != null) {
@@ -4250,64 +4250,64 @@ function z$(e, t, n, s) {
     }
   }
 }
-var sx = 20;
-var Su = 3;
-var Df = 7;
+var nx = 20;
+var Iu = 3;
+var Rf = 7;
 function M$(e, t, n, s) {
-  let r = ao(t), a = L$(e, t, n, r), i = t.length, o = ed(e, t, n, r, a), u = ["Tensor"];
+  let r = ro(t), a = L$(e, t, n, r), i = t.length, o = Jc(e, t, n, r, a), u = ["Tensor"];
   return s && (u.push(`  dtype: ${n}`), u.push(`  rank: ${i}`), u.push(`  shape: [${t}]`), u.push("  values:")), u.push(o.map((l) => "    " + l).join(`
 `)), u.join(`
 `);
 }
 function L$(e, t, n, s) {
-  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? _u(e) : e;
+  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? $u(e) : e;
   if (o > 1)
     for (let l = 0; l < r / a; l++) {
       let c = l * a;
       for (let p = 0; p < a; p++)
-        i[p] = Math.max(i[p], $u(u[c + p], 0, n).length);
+        i[p] = Math.max(i[p], Tu(u[c + p], 0, n).length);
     }
   return i;
 }
-function $u(e, t, n) {
+function Tu(e, t, n) {
   let s;
-  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Df))} + ${parseFloat(e[1].toFixed(Df))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = Qw(e) : s = parseFloat(e.toFixed(Df)).toString(), zu(s, t);
+  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Rf))} + ${parseFloat(e[1].toFixed(Rf))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = Qw(e) : s = parseFloat(e.toFixed(Rf)).toString(), Pu(s, t);
 }
 function Qw(e) {
   return e === 0 ? "false" : "true";
 }
-function ed(e, t, n, s, r, a = true) {
+function Jc(e, t, n, s, r, a = true) {
   let i = n === "complex64" ? 2 : 1, o = t[0], u = t.length;
   if (u === 0) {
     if (n === "complex64") {
-      let m = _u(e);
-      return [$u(m[0], 0, n)];
+      let m = $u(e);
+      return [Tu(m[0], 0, n)];
     }
     return n === "bool" ? [Qw(e[0])] : [e[0].toString()];
   }
   if (u === 1) {
-    if (o > sx) {
-      let g = Su * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Su) * i, o * i));
-      return n === "complex64" && (b = _u(b), y = _u(y)), ["[" + b.map((v, x) => $u(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => $u(v, r[o - Su + x], n)).join(", ") + "]"];
+    if (o > nx) {
+      let g = Iu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Iu) * i, o * i));
+      return n === "complex64" && (b = $u(b), y = $u(y)), ["[" + b.map((v, x) => Tu(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Tu(v, r[o - Iu + x], n)).join(", ") + "]"];
     }
-    let m = n === "complex64" ? _u(e) : Array.from(e);
-    return ["[" + m.map((g, b) => $u(g, r[b], n)).join(", ") + "]"];
+    let m = n === "complex64" ? $u(e) : Array.from(e);
+    return ["[" + m.map((g, b) => Tu(g, r[b], n)).join(", ") + "]"];
   }
   let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
-  if (o > sx) {
-    for (let m = 0; m < Su; m++) {
+  if (o > nx) {
+    for (let m = 0; m < Iu; m++) {
       let g = m * p, b = g + p;
-      d.push(...ed(e.slice(g, b), l, n, c, r, false));
+      d.push(...Jc(e.slice(g, b), l, n, c, r, false));
     }
     d.push("...");
-    for (let m = o - Su; m < o; m++) {
+    for (let m = o - Iu; m < o; m++) {
       let g = m * p, b = g + p;
-      d.push(...ed(e.slice(g, b), l, n, c, r, m === o - 1));
+      d.push(...Jc(e.slice(g, b), l, n, c, r, m === o - 1));
     }
   } else
     for (let m = 0; m < o; m++) {
       let g = m * p, b = g + p;
-      d.push(...ed(e.slice(g, b), l, n, c, r, m === o - 1));
+      d.push(...Jc(e.slice(g, b), l, n, c, r, m === o - 1));
     }
   let h = u === 2 ? "," : "";
   d[0] = "[" + d[0] + h;
@@ -4320,7 +4320,7 @@ function ed(e, t, n, s, r, a = true) {
 `;
   return d[d.length - 1] = " " + d[d.length - 1] + "]" + (a ? "" : f), d;
 }
-function _u(e) {
+function $u(e) {
   let t = [];
   for (let n = 0; n < e.length; n += 2)
     t.push([e[n], e[n + 1]]);
@@ -4334,7 +4334,7 @@ var Vt = class {
     }
     if (t === "complex64")
       throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
-    this.values = n || Bw(t, this.size), this.strides = ao(e);
+    this.values = n || Bw(t, this.size), this.strides = ro(e);
   }
   set(e, ...t) {
     t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
@@ -4384,37 +4384,37 @@ var Vt = class {
   }
 };
 var us = null;
-var Vi = null;
+var Bi = null;
 var B$ = null;
 function V$(e) {
   us = e;
 }
 function W$(e) {
-  Vi = e;
+  Bi = e;
 }
 function U$(e) {
   B$ = e;
 }
 var et = class {
   constructor(e, t, n, s) {
-    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = ao(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
+    this.kept = false, this.isDisposedInternal = false, this.shape = e.slice(), this.dtype = t || "float32", this.size = pt(e), this.strides = ro(e), this.dataId = n, this.id = s, this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
   }
   get rank() {
     return this.shape.length;
   }
   async buffer() {
     let e = await this.data();
-    return Vi.buffer(this.shape, this.dtype, e);
+    return Bi.buffer(this.shape, this.dtype, e);
   }
   bufferSync() {
-    return Vi.buffer(this.shape, this.dtype, this.dataSync());
+    return Bi.buffer(this.shape, this.dtype, this.dataSync());
   }
   async array() {
     let e = await this.data();
-    return Hi(this.shape, e, this.dtype === "complex64");
+    return Gi(this.shape, e, this.dtype === "complex64");
   }
   arraySync() {
-    return Hi(this.shape, this.dataSync(), this.dtype === "complex64");
+    return Gi(this.shape, this.dataSync(), this.dtype === "complex64");
   }
   async data() {
     this.throwIfDisposed();
@@ -4422,7 +4422,7 @@ var et = class {
     if (this.dtype === "string") {
       let t = await e;
       try {
-        return t.map((n) => md(n));
+        return t.map((n) => fd(n));
       } catch (n) {
         throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
       }
@@ -4437,7 +4437,7 @@ var et = class {
     let e = us().readSync(this.dataId);
     if (this.dtype === "string")
       try {
-        return e.map((t) => md(t));
+        return e.map((t) => fd(t));
       } catch (t) {
         throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
       }
@@ -4459,17 +4459,17 @@ var et = class {
       throw new Error("Tensor is disposed.");
   }
   print(e = false) {
-    return Vi.print(this, e);
+    return Bi.print(this, e);
   }
   clone() {
-    return this.throwIfDisposed(), Vi.clone(this);
+    return this.throwIfDisposed(), Bi.clone(this);
   }
   toString(e = false) {
     let t = this.dataSync();
     return M$(t, this.shape, this.dtype, e);
   }
   cast(e) {
-    return this.throwIfDisposed(), Vi.cast(this, e);
+    return this.throwIfDisposed(), Bi.cast(this, e);
   }
   variable(e = true, t, n) {
     return this.throwIfDisposed(), us().makeVariable(this, e, t, n);
@@ -4477,10 +4477,10 @@ var et = class {
 };
 Object.defineProperty(et, Symbol.hasInstance, { value: (e) => !!e && e.data != null && e.dataSync != null && e.throwIfDisposed != null });
 function G$() {
-  return rg("Tensor", () => et);
+  return sg("Tensor", () => et);
 }
 G$();
-var gd = class extends et {
+var md = class extends et {
   constructor(e, t, n, s) {
     super(e.shape, e.dtype, e.dataId, s);
     this.trainable = t, this.name = n;
@@ -4496,9 +4496,9 @@ var gd = class extends et {
     us().disposeVariable(this), this.isDisposedInternal = true;
   }
 };
-Object.defineProperty(gd, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
+Object.defineProperty(md, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
 var _s = {};
-Ae(_s, { assertTypesMatch: () => nk, getTensorsInContainer: () => Eg, isTensorInList: () => j$, makeTypesMatch: () => vt });
+Ae(_s, { assertTypesMatch: () => nk, getTensorsInContainer: () => Ag, isTensorInList: () => j$, makeTypesMatch: () => vt });
 var H$ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(H$ || {});
 var Zw = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(Zw || {});
 var Jw = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(Jw || {});
@@ -4513,7 +4513,7 @@ function yn(e, t) {
   }
   return q$[e][t];
 }
-function cp(e) {
+function lp(e) {
   return yn(e, "int32");
 }
 function vt(e, t) {
@@ -4528,7 +4528,7 @@ function nk(e, t) {
 function j$(e, t) {
   return t.some((n) => n.id === e.id);
 }
-function Eg(e) {
+function Ag(e) {
   let t = [];
   return sk(e, t, /* @__PURE__ */ new Set()), t;
 }
@@ -4550,10 +4550,10 @@ function sk(e, t, n) {
 function K$(e) {
   return Array.isArray(e) || typeof e == "object";
 }
-function Ff(e) {
+function Df(e) {
   return e.kernelName != null;
 }
-var rx = class {
+var sx = class {
   constructor() {
     this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
       return Array.from(new Set(this.kernels.map((e) => e.name)));
@@ -4564,9 +4564,9 @@ var rx = class {
       this.registeredVariables[e].dispose();
   }
 };
-var em = class {
+var Jf = class {
   constructor(e) {
-    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new rx();
+    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new sx();
   }
   async ready() {
     if (this.pendingBackendInit != null)
@@ -4626,12 +4626,12 @@ var em = class {
     return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new D$(this.backendInstance), true;
   }
   setupRegisteredKernels() {
-    Zf(this.backendName).forEach((t) => {
+    Qf(this.backendName).forEach((t) => {
       t.setupFunc != null && t.setupFunc(this.backendInstance);
     });
   }
   disposeRegisteredKernels(e) {
-    Zf(e).forEach((n) => {
+    Qf(e).forEach((n) => {
       n.disposeFunc != null && n.disposeFunc(this.registry[e]);
     });
   }
@@ -4641,7 +4641,7 @@ var em = class {
       throw new Error(`Cannot initialize backend ${e}, no registration found.`);
     try {
       let n = t.factory();
-      if (n && !(n instanceof nl) && typeof n.then == "function") {
+      if (n && !(n instanceof tl) && typeof n.then == "function") {
         let s = ++this.pendingBackendInitId, r = n.then((a) => s < this.pendingBackendInitId ? false : (this.registry[e] = a, this.pendingBackendInit = null, true)).catch((a) => (s < this.pendingBackendInitId || (this.pendingBackendInit = null, ar(`Initialization of backend ${e} failed`), ar(a.stack || a.message)), false));
         return this.pendingBackendInit = r, { success: r, asyncInit: true };
       } else
@@ -4699,10 +4699,10 @@ var em = class {
     }
   }
   nextTensorId() {
-    return em.nextTensorId++;
+    return Jf.nextTensorId++;
   }
   nextVariableId() {
-    return em.nextVariableId++;
+    return Jf.nextVariableId++;
   }
   clone(e) {
     let t = M.runKernel(Ma, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
@@ -4712,7 +4712,7 @@ var em = class {
     return this.addTapeNode(this.state.activeScope.name, n, [t], s, r, {}), t;
   }
   runKernel(e, t, n) {
-    if (this.backendName == null && this.backend, !(Qf(e, this.backendName) != null))
+    if (this.backendName == null && this.backend, !(Yf(e, this.backendName) != null))
       throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);
     return this.runKernelFunc({ kernelName: e, inputs: t, attrs: n });
   }
@@ -4733,11 +4733,11 @@ var em = class {
     this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
     let i;
     this.backendName == null && this.backend;
-    let o, u = Ff(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
-    if (Ff(e)) {
+    let o, u = Df(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
+    if (Df(e)) {
       let { kernelName: h, inputs: f, attrs: m } = e;
       this.backendName == null && this.backend;
-      let g = Qf(h, this.backendName);
+      let g = Yf(h, this.backendName);
       O(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), i = () => {
         let b = this.backend.numDataIds();
         o = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend });
@@ -4766,7 +4766,7 @@ var em = class {
         return this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(u, m, g), g;
       };
     }
-    let { inputs: l, attrs: c } = e, p = Ff(e) ? null : e.backwardsFunc, d;
+    let { inputs: l, attrs: c } = e, p = Df(e) ? null : e.backwardsFunc, d;
     return this.scopedRun(() => this.state.kernelDepth++, () => this.state.kernelDepth--, () => {
       !this.ENV.getBool("DEBUG") && !this.state.profiling ? t = i() : (d = this.profiler.profileKernel(u, l, () => i()), this.ENV.getBool("DEBUG") && this.profiler.logKernelProfile(d), t = d.outputs);
     }), s && this.addTapeNode(u, l, t, p, n, c), this.state.profiling && this.state.activeProfile.kernels.push({ name: u, bytesAdded: this.state.numBytes - r, totalBytesSnapshot: this.state.numBytes, tensorsAdded: this.state.numTensors - a, totalTensorsSnapshot: this.state.numTensors, inputShapes: Object.keys(l).map((h) => l[h] != null ? l[h].shape : null), outputShapes: t.map((h) => h.shape), kernelTimeMs: d.timeMs, extraInfo: d.extraInfo }), Array.isArray(o) ? t : t[0];
@@ -4775,7 +4775,7 @@ var em = class {
     return e.map((n) => this.keep(this.clone(n)));
   }
   getTensorsForGradient(e, t, n) {
-    let s = ex(e);
+    let s = Jv(e);
     if (s != null) {
       let r = s.inputsToSave || [], a = s.outputsToSave || [], i;
       s.saveAllInputs ? (O(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t).map((u) => t[u])) : i = r.map((u) => t[u]);
@@ -4789,7 +4789,7 @@ var em = class {
       throw new Error("Values passed to engine.makeTensor() are null");
     n = n || "float32", s = s || this.backend;
     let r = e;
-    n === "string" && ir(e[0]) && (r = e.map((o) => Rl(o)));
+    n === "string" && ir(e[0]) && (r = e.map((o) => El(o)));
     let a = s.write(r, t, n), i = new et(t, n, a, this.nextTensorId());
     if (this.trackTensor(i, s), n === "string") {
       let o = this.state.tensorInfo.get(a), u = Uw(r);
@@ -4804,7 +4804,7 @@ var em = class {
   }
   makeVariable(e, t = true, n, s) {
     n = n || this.nextVariableId().toString(), s != null && s !== e.dtype && (e = e.cast(s));
-    let r = new gd(e, t, n, this.nextTensorId());
+    let r = new md(e, t, n, this.nextTensorId());
     if (this.state.registeredVariables[r.name] != null)
       throw new Error(`Variable with name ${r.name} was already registered`);
     return this.state.registeredVariables[r.name] = r, this.incRef(r, this.backend), r;
@@ -4812,7 +4812,7 @@ var em = class {
   trackTensor(e, t) {
     this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
     let n = 0;
-    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * Kf(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 gd || this.track(e);
+    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * jf(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 md || this.track(e);
   }
   incRef(e, t) {
     this.trackTensor(e, t), this.backend.incRef(e.dataId);
@@ -4825,7 +4825,7 @@ var em = class {
       return;
     let t = this.state.tensorInfo.get(e.dataId);
     if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
-      let n = e.size * Kf(e.dtype);
+      let n = e.size * jf(e.dtype);
       this.state.numBytes -= n;
     }
     t.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t.backend);
@@ -4855,10 +4855,10 @@ var em = class {
     return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
   }
   addTapeNode(e, t, n, s, r, a) {
-    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = ex(e);
+    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = Jv(e);
     o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => {
       if (l == null) {
-        let p = n[c], d = Hd(p.size, p.dtype);
+        let p = n[c], d = Gd(p.size, p.dtype);
         return this.makeTensor(d, p.shape, p.dtype);
       }
       return l;
@@ -4878,7 +4878,7 @@ var em = class {
     e && (t.name = e), this.state.scopeStack.push(t), this.state.activeScope = t;
   }
   endScope(e) {
-    let t = Eg(e), n = new Set(t.map((r) => r.id));
+    let t = Ag(e), n = new Set(t.map((r) => r.id));
     for (let r = 0; r < this.state.activeScope.track.length; r++) {
       let a = this.state.activeScope.track[r];
       !a.kept && !n.has(a.id) && a.dispose();
@@ -4934,8 +4934,8 @@ var em = class {
     return this.state.tensorInfo.get(e).backend.readToGPU(e, t);
   }
   async time(e) {
-    let t = Uu(), n = await this.backend.time(e);
-    return n.wallMs = Uu() - t, n;
+    let t = Wu(), n = await this.backend.time(e);
+    return n.wallMs = Wu() - t, n;
   }
   track(e) {
     return this.state.activeScope != null && (e.scopeId = this.state.activeScope.id, this.state.activeScope.track.push(e)), e;
@@ -4944,24 +4944,24 @@ var em = class {
     return this.state.registeredVariables;
   }
   reset() {
-    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new rx();
+    this.pendingBackendInitId++, this.state.dispose(), this.ENV.reset(), this.state = new sx();
     for (let e in this.registry)
       this.disposeRegisteredKernels(e), this.registry[e].dispose(), delete this.registry[e];
     this.backendName = null, this.backendInstance = null, this.pendingBackendInit = null;
   }
 };
-var Rg = em;
-Rg.nextTensorId = 0;
-Rg.nextVariableId = 0;
+var Eg = Jf;
+Eg.nextTensorId = 0;
+Eg.nextVariableId = 0;
 function X$(e) {
-  let t = tg(pt(e), "float32");
+  let t = eg(pt(e), "float32");
   return M.makeTensor(t, e, "float32");
 }
 function rk() {
   let e = Kw();
   if (e._tfengine == null) {
     let t = new m$(e);
-    e._tfengine = new Rg(t);
+    e._tfengine = new Eg(t);
   }
   return v$(e._tfengine.ENV), V$(() => e._tfengine), e._tfengine;
 }
@@ -4970,18 +4970,18 @@ function Y$(e, t) {
   let n = { a: e, b: t };
   return M.runKernel(kr, n);
 }
-var dp = {};
-Ae(dp, { isBrowser: () => ak, isMobile: () => J$, mockIsMobile: () => Z$ });
+var cp = {};
+Ae(cp, { isBrowser: () => ak, isMobile: () => J$, mockIsMobile: () => Z$ });
 function Q$() {
   return typeof navigator != "undefined" && navigator != null;
 }
-var tm;
+var em;
 function Z$(e) {
-  tm = e;
+  em = e;
 }
 function J$(e) {
-  if (tm !== void 0)
-    return tm;
+  if (em !== void 0)
+    return em;
   if (e || Q$()) {
     if (e || (e = navigator), e.product === "ReactNative")
       return true;
@@ -5031,7 +5031,7 @@ function ik(e, t, n) {
   for (let r = 0; r < e.length; ++r)
     ik(e[r], s, n.concat(r));
 }
-function ax(e, t, n, s) {
+function rx(e, t, n, s) {
   if (e !== "string_or_numeric") {
     if (e == null)
       throw new Error("Expected dtype cannot be null.");
@@ -5041,18 +5041,18 @@ function ax(e, t, n, s) {
 }
 function _(e, t, n, s = "numeric") {
   if (e instanceof et)
-    return ax(s, e.dtype, t, n), e;
-  let r = Gd(e);
-  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), ax(s, r, t, n), e == null || !Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
+    return rx(s, e.dtype, t, n), e;
+  let r = Ud(e);
+  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), rx(s, r, t, n), e == null || !Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
     let u = e == null ? "null" : e.constructor.name;
     throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`);
   }
   let a = Rs(e, r);
   !Yt(e) && !Array.isArray(e) && (e = [e]);
-  let o = r !== "string" ? lp(e, r) : ta(e, [], true);
+  let o = r !== "string" ? up(e, r) : ta(e, [], true);
   return M.makeTensor(o, a, r);
 }
-function Gu(e, t, n, s = "numeric") {
+function Uu(e, t, n, s = "numeric") {
   if (!Array.isArray(e))
     throw new Error(`Argument ${t} passed to ${n} must be a \`Tensor[]\` or \`TensorLike[]\``);
   return e.map((a, i) => _(a, `${t}[${i}]`, n, s));
@@ -5068,7 +5068,7 @@ function L(e) {
     M.startScope(n);
     try {
       let i = s(...a);
-      return sg(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i;
+      return ng(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i;
     } catch (i) {
       throw M.endScope(null), i;
     }
@@ -5079,16 +5079,16 @@ function t_(e, t) {
   let n = _(e, "real", "complex"), s = _(t, "imag", "complex");
   dn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);
   let r = { real: n, imag: s };
-  return M.runKernel(jd, r);
+  return M.runKernel(qd, r);
 }
 var aa = L({ complex_: t_ });
 function Cr(e, t, n, s) {
-  if (s == null && (s = Gd(e)), s === "complex64")
+  if (s == null && (s = Ud(e)), s === "complex64")
     throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
   if (!Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
     throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
   if (t != null) {
-    ng(t);
+    tg(t);
     let r = pt(t), a = pt(n);
     O(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
     for (let i = 0; i < n.length; ++i) {
@@ -5096,14 +5096,14 @@ function Cr(e, t, n, s) {
       O(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
     }
   }
-  return !Yt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? lp(e, s) : ta(e, [], true), M.makeTensor(e, t, s);
+  return !Yt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? up(e, s) : ta(e, [], true), M.makeTensor(e, t, s);
 }
 function hs(e, t, n) {
   let s = Rs(e, n);
   return Cr(e, t, s, n);
 }
-var nm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
-var bd = 4;
+var tm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
+var gd = 4;
 async function n_(e, t) {
   let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e);
   for (let i = 0; i < r.length; ++i) {
@@ -5113,10 +5113,10 @@ async function n_(e, t) {
     let l = { name: o, shape: u.shape, dtype: u.dtype };
     if (u.dtype === "string") {
       let c = new Promise(async (p) => {
-        let d = await u.bytes(), h = d.reduce((g, b) => g + b.length, 0) + bd * d.length, f = new Uint8Array(h), m = 0;
+        let d = await u.bytes(), h = d.reduce((g, b) => g + b.length, 0) + gd * d.length, f = new Uint8Array(h), m = 0;
         for (let g = 0; g < d.length; g++) {
           let b = d[g], y = new Uint8Array(new Uint32Array([b.length]).buffer);
-          f.set(y, m), m += bd, f.set(b, m), m += b.length;
+          f.set(y, m), m += gd, f.set(b, m), m += b.length;
         }
         p(f);
       });
@@ -5142,7 +5142,7 @@ function ok(e, t) {
           throw new Error(`Weight ${a.name} is quantized with ${p.dtype} which only supports weights of type float32 not ${o}.`);
       } else
         throw new Error(`Weight ${a.name} has unknown quantization dtype ${p.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);
-      let d = nm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
+      let d = tm[p.dtype], h = e.slice(r, r + l * d), f = p.dtype === "uint8" ? new Uint8Array(h) : new Uint16Array(h);
       if (o === "float32")
         if (p.dtype === "uint8" || p.dtype === "uint16") {
           c = new Float32Array(f.length);
@@ -5169,13 +5169,13 @@ function ok(e, t) {
       let p = pt(a.shape);
       c = [];
       for (let d = 0; d < p; d++) {
-        let h = new Uint32Array(e.slice(r, r + bd))[0];
-        r += bd;
+        let h = new Uint32Array(e.slice(r, r + gd))[0];
+        r += gd;
         let f = new Uint8Array(e.slice(r, r + h));
         c.push(f), r += h;
       }
     } else {
-      let p = nm[o], d = e.slice(r, r + l * p);
+      let p = tm[o], d = e.slice(r, r + l * p);
       if (o === "float32")
         c = new Float32Array(d);
       else if (o === "int32")
@@ -5210,12 +5210,12 @@ function s_(e) {
     s.set(new Uint8Array(a.buffer), r), r += a.byteLength;
   }), s.buffer;
 }
-var Dg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
-function ix(e) {
-  return Dg ? Buffer.byteLength(e) : new Blob([e]).size;
+var Rg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
+function ax(e) {
+  return Rg ? Buffer.byteLength(e) : new Blob([e]).size;
 }
 function r_(e) {
-  if (Dg)
+  if (Rg)
     return Buffer.from(e).toString("base64");
   let t = new Uint8Array(e), n = "";
   for (let s = 0, r = t.length; s < r; s++)
@@ -5223,7 +5223,7 @@ function r_(e) {
   return btoa(n);
 }
 function a_(e) {
-  if (Dg) {
+  if (Rg) {
     let s = Buffer.from(e, "base64");
     return s.buffer.slice(s.byteOffset, s.byteOffset + s.byteLength);
   }
@@ -5232,7 +5232,7 @@ function a_(e) {
     n.set([t.charCodeAt(s)], s);
   return n.buffer;
 }
-function Fg(e) {
+function Dg(e) {
   if (e.length === 1)
     return e[0];
   let t = 0;
@@ -5244,7 +5244,7 @@ function Fg(e) {
     n.set(new Uint8Array(r), s), s += r.byteLength;
   }), n.buffer;
 }
-function ox(e) {
+function ix(e) {
   let t = "/";
   for (e = e.trim(); e.endsWith(t); )
     e = e.slice(0, e.length - 1);
@@ -5255,7 +5255,7 @@ function uk(e, t) {
   let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, weightsManifest: t };
   return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), n;
 }
-async function Og(e, t) {
+async function Fg(e, t) {
   let n = { modelTopology: e.modelTopology, format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy };
   if (e.trainingConfig != null && (n.trainingConfig = e.trainingConfig), e.weightsManifest != null) {
     let [s, r] = await t(e.weightsManifest);
@@ -5263,10 +5263,10 @@ async function Og(e, t) {
   }
   return e.signature != null && (n.signature = e.signature), e.userDefinedMetadata != null && (n.userDefinedMetadata = e.userDefinedMetadata), e.modelInitializer != null && (n.modelInitializer = e.modelInitializer), n;
 }
-function Dl(e) {
+function Rl(e) {
   if (e.modelTopology instanceof ArrayBuffer)
     throw new Error("Expected JSON model topology, received ArrayBuffer.");
-  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : ix(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : ix(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
+  return { dateSaved: new Date(), modelTopologyType: "JSON", modelTopologyBytes: e.modelTopology == null ? 0 : ax(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : ax(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
 }
 function i_() {
   let e = (n) => {
@@ -5339,8 +5339,8 @@ var c_ = (e) => xt.registerSaveRouter(e);
 var d_ = (e) => xt.registerLoadRouter(e);
 var p_ = (e) => xt.getSaveHandlers(e);
 var h_ = (e, t) => xt.getLoadHandlers(e, t);
-var sm = "tensorflowjs";
-var rm = 1;
+var nm = "tensorflowjs";
+var sm = 1;
 var Kr = "models_store";
 var or = "model_info_store";
 function lk() {
@@ -5351,7 +5351,7 @@ function lk() {
     throw new Error("The current browser does not appear to support IndexedDB.");
   return t;
 }
-function am(e) {
+function rm(e) {
   let t = e.result;
   t.createObjectStore(Kr, { keyPath: "modelPath" }), t.createObjectStore(or, { keyPath: "modelPath" });
 }
@@ -5371,8 +5371,8 @@ var ia = class {
   }
   databaseAction(e, t) {
     return new Promise((n, s) => {
-      let r = this.indexedDB.open(sm, rm);
-      r.onupgradeneeded = () => am(r), r.onsuccess = () => {
+      let r = this.indexedDB.open(nm, sm);
+      r.onupgradeneeded = () => rm(r), r.onsuccess = () => {
         let a = r.result;
         if (t == null) {
           let i = a.transaction(Kr, "readonly"), u = i.objectStore(Kr).get(this.modelPath);
@@ -5382,7 +5382,7 @@ var ia = class {
             n(u.result.modelArtifacts);
           }, u.onerror = (l) => (a.close(), s(u.error)), i.oncomplete = () => a.close();
         } else {
-          let i = Dl(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
+          let i = Rl(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
           l.onsuccess = () => {
             c = a.transaction(Kr, "readwrite");
             let d = c.objectStore(Kr).put({ modelPath: this.modelPath, modelArtifacts: t, modelArtifactsInfo: i });
@@ -5415,8 +5415,8 @@ var g_ = class {
   }
   async listModels() {
     return new Promise((e, t) => {
-      let n = this.indexedDB.open(sm, rm);
-      n.onupgradeneeded = () => am(n), n.onsuccess = () => {
+      let n = this.indexedDB.open(nm, sm);
+      n.onupgradeneeded = () => rm(n), n.onsuccess = () => {
         let s = n.result, r = s.transaction(or, "readonly"), i = r.objectStore(or).getAll();
         i.onsuccess = () => {
           let o = {};
@@ -5429,8 +5429,8 @@ var g_ = class {
   }
   async removeModel(e) {
     return e = m_(e), new Promise((t, n) => {
-      let s = this.indexedDB.open(sm, rm);
-      s.onupgradeneeded = () => am(s), s.onsuccess = () => {
+      let s = this.indexedDB.open(nm, sm);
+      s.onupgradeneeded = () => rm(s), s.onsuccess = () => {
         let r = s.result, a = r.transaction(or, "readwrite"), i = a.objectStore(or), o = i.get(e), u;
         o.onsuccess = () => {
           if (o.result == null)
@@ -5451,14 +5451,14 @@ var g_ = class {
   }
 };
 var Gs = "/";
-var Wi = "tensorflowjs_models";
+var Vi = "tensorflowjs_models";
 var dk = "info";
 var b_ = "model_topology";
 var y_ = "weight_specs";
 var v_ = "weight_data";
 var x_ = "model_metadata";
 function pk(e) {
-  return { info: [Wi, e, dk].join(Gs), topology: [Wi, e, b_].join(Gs), weightSpecs: [Wi, e, y_].join(Gs), weightData: [Wi, e, v_].join(Gs), modelMetadata: [Wi, e, x_].join(Gs) };
+  return { info: [Vi, e, dk].join(Gs), topology: [Vi, e, b_].join(Gs), weightSpecs: [Vi, e, y_].join(Gs), weightData: [Vi, e, v_].join(Gs), modelMetadata: [Vi, e, x_].join(Gs) };
 }
 function hk(e) {
   for (let t of Object.values(e))
@@ -5485,7 +5485,7 @@ var oa = class {
     if (e.modelTopology instanceof ArrayBuffer)
       throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
     {
-      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Dl(e);
+      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Rl(e);
       try {
         this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, r_(e.weightData));
         let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
@@ -5532,7 +5532,7 @@ var S_ = class {
     O(X().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), O(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
   }
   async listModels() {
-    let e = {}, t = Wi + Gs, n = Gs + dk;
+    let e = {}, t = Vi + Gs, n = Gs + dk;
     for (let s = 0; s < this.LS.length; ++s) {
       let r = this.LS.key(s);
       if (r.startsWith(t) && r.endsWith(n)) {
@@ -5551,7 +5551,7 @@ var S_ = class {
     return hk(t), n;
   }
 };
-var qi = "://";
+var Hi = "://";
 var Pn = class {
   constructor() {
     this.managers = {};
@@ -5560,7 +5560,7 @@ var Pn = class {
     return Pn.instance == null && (Pn.instance = new Pn()), Pn.instance;
   }
   static registerManager(e, t) {
-    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(qi) && (e = e.slice(0, e.indexOf(qi))), O(e.length > 0, () => "scheme must not be an empty string.");
+    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(Hi) && (e = e.slice(0, e.indexOf(Hi))), O(e.length > 0, () => "scheme must not be an empty string.");
     let n = Pn.getInstance();
     O(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
   }
@@ -5574,10 +5574,10 @@ var Pn = class {
     return Object.keys(this.getInstance().managers);
   }
 };
-function td(e) {
-  if (e.indexOf(qi) === -1)
+function ed(e) {
+  if (e.indexOf(Hi) === -1)
     throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Pn.getSchemes().join(",")}`);
-  return { scheme: e.split(qi)[0], path: e.split(qi)[1] };
+  return { scheme: e.split(Hi)[0], path: e.split(Hi)[1] };
 }
 async function mk(e, t, n = false) {
   O(e !== t, () => `Old path and new path are the same: '${e}'`);
@@ -5585,7 +5585,7 @@ async function mk(e, t, n = false) {
   O(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), O(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
   let r = s[0], a = xt.getSaveHandlers(t);
   O(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), O(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);
-  let i = a[0], o = td(e).scheme, u = td(e).path, l = o === td(e).scheme, c = await r.load();
+  let i = a[0], o = ed(e).scheme, u = ed(e).path, l = o === ed(e).scheme, c = await r.load();
   n && l && await Pn.getManager(o).removeModel(u);
   let p = await i.save(c);
   return n && !l && await Pn.getManager(o).removeModel(u), p.modelArtifactsInfo;
@@ -5595,14 +5595,14 @@ async function C_() {
   for (let n of e) {
     let s = await Pn.getManager(n).listModels();
     for (let r in s) {
-      let a = n + qi + r;
+      let a = n + Hi + r;
       t[a] = s[r];
     }
   }
   return t;
 }
 async function N_(e) {
-  let t = td(e);
+  let t = ed(e);
   return Pn.getManager(t.scheme).removeModel(t.path);
 }
 async function T_(e, t) {
@@ -5639,13 +5639,13 @@ if (X().get("IS_BROWSER")) {
   }
 }
 var A_ = { importFetch: () => zT() };
-var Of;
+var Ff;
 var E_ = class {
   constructor() {
     this.util = MT(), this.textEncoder = new this.util.TextEncoder();
   }
   fetch(e, t) {
-    return X().global.fetch != null ? X().global.fetch(e, t) : (Of == null && (Of = A_.importFetch()), Of(e, t));
+    return X().global.fetch != null ? X().global.fetch(e, t) : (Ff == null && (Ff = A_.importFetch()), Ff(e, t));
   }
   now() {
     let e = process.hrtime();
@@ -5662,7 +5662,7 @@ var E_ = class {
 };
 X().get("IS_NODE") && !X().get("IS_BROWSER") && X().setPlatform("node", new E_());
 function De(e, t = "float32", n) {
-  return t = t || "float32", ng(e), new Vt(e, t, n);
+  return t = t || "float32", tg(e), new Vt(e, t, n);
 }
 function R_(e, t) {
   let n = _(e, "x", "cast");
@@ -5686,18 +5686,18 @@ rk();
 var O_ = { buffer: De, cast: ce, clone: lr, print: F_ };
 W$(O_);
 var _n = {};
-Ae(_n, { browserFiles: () => W_, browserHTTPRequest: () => j_, concatenateArrayBuffers: () => Fg, copyModel: () => T_, decodeWeights: () => ok, encodeWeights: () => n_, fromMemory: () => X_, getLoadHandlers: () => h_, getModelArtifactsForJSON: () => Og, getModelArtifactsInfoForJSON: () => Dl, getSaveHandlers: () => p_, http: () => zg, isHTTPScheme: () => om, listModels: () => C_, loadWeights: () => U_, moveModel: () => $_, registerLoadRouter: () => d_, registerSaveRouter: () => c_, removeModel: () => N_, weightsLoaderFactory: () => bk, withSaveHandler: () => Y_ });
+Ae(_n, { browserFiles: () => W_, browserHTTPRequest: () => j_, concatenateArrayBuffers: () => Dg, copyModel: () => T_, decodeWeights: () => ok, encodeWeights: () => n_, fromMemory: () => X_, getLoadHandlers: () => h_, getModelArtifactsForJSON: () => Fg, getModelArtifactsInfoForJSON: () => Rl, getSaveHandlers: () => p_, http: () => Pg, isHTTPScheme: () => im, listModels: () => C_, loadWeights: () => U_, moveModel: () => $_, registerLoadRouter: () => d_, registerSaveRouter: () => c_, removeModel: () => N_, weightsLoaderFactory: () => bk, withSaveHandler: () => Y_ });
 var P_ = "model";
 var z_ = ".json";
 var M_ = ".weights.bin";
-function ux(e) {
+function ox(e) {
   return new Promise((t) => setTimeout(t)).then(e);
 }
-var im = class {
+var am = class {
   constructor(e) {
     if (!X().getBool("IS_BROWSER"))
       throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
-    e.startsWith(im.URL_SCHEME) && (e = e.slice(im.URL_SCHEME.length)), (e == null || e.length === 0) && (e = P_), this.modelJsonFileName = e + z_, this.weightDataFileName = e + M_;
+    e.startsWith(am.URL_SCHEME) && (e = e.slice(am.URL_SCHEME.length)), (e == null || e.length === 0) && (e = P_), this.modelJsonFileName = e + z_, this.weightDataFileName = e + M_;
   }
   async save(e) {
     if (typeof document == "undefined")
@@ -5707,16 +5707,16 @@ var im = class {
       throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
     {
       let n = [{ paths: ["./" + this.weightDataFileName], weights: e.weightSpecs }], s = uk(e, n), r = window.URL.createObjectURL(new Blob([JSON.stringify(s)], { type: "application/json" })), a = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
-      if (a.download = this.modelJsonFileName, a.href = r, await ux(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
+      if (a.download = this.modelJsonFileName, a.href = r, await ox(() => a.dispatchEvent(new MouseEvent("click"))), e.weightData != null) {
         let i = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
-        i.download = this.weightDataFileName, i.href = t, await ux(() => i.dispatchEvent(new MouseEvent("click")));
+        i.download = this.weightDataFileName, i.href = t, await ox(() => i.dispatchEvent(new MouseEvent("click")));
       }
-      return { modelArtifactsInfo: Dl(e) };
+      return { modelArtifactsInfo: Rl(e) };
     }
   }
 };
-var yd = im;
-yd.URL_SCHEME = "downloads://";
+var bd = am;
+bd.URL_SCHEME = "downloads://";
 var L_ = class {
   constructor(e) {
     if (e == null || e.length < 1)
@@ -5740,7 +5740,7 @@ var L_ = class {
           e({ modelTopology: a });
           return;
         }
-        let o = Og(r, (u) => this.loadWeights(u));
+        let o = Fg(r, (u) => this.loadWeights(u));
         e(o);
       }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`), n.readAsText(this.jsonFile);
     });
@@ -5750,7 +5750,7 @@ var L_ = class {
     for (let a of e)
       t.push(...a.weights), n.push(...a.paths);
     let s = this.checkManifestAndWeightFiles(e), r = n.map((a) => this.loadWeightsFile(a, s[a]));
-    return Promise.all(r).then((a) => [t, Fg(a)]);
+    return Promise.all(r).then((a) => [t, Dg(a)]);
   }
   loadWeightsFile(e, t) {
     return new Promise((n, s) => {
@@ -5762,10 +5762,10 @@ var L_ = class {
     });
   }
   checkManifestAndWeightFiles(e) {
-    let t = [], n = this.weightsFiles.map((r) => ox(r.name)), s = {};
+    let t = [], n = this.weightsFiles.map((r) => ix(r.name)), s = {};
     for (let r of e)
       r.paths.forEach((a) => {
-        let i = ox(a);
+        let i = ix(a);
         if (t.indexOf(i) !== -1)
           throw new Error(`Duplicate file basename found in weights manifest: '${i}'`);
         if (t.push(i), n.indexOf(i) === -1)
@@ -5777,15 +5777,15 @@ var L_ = class {
     return s;
   }
 };
-var B_ = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(yd.URL_SCHEME) ? V_(e.slice(yd.URL_SCHEME.length)) : null;
+var B_ = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(bd.URL_SCHEME) ? V_(e.slice(bd.URL_SCHEME.length)) : null;
 xt.registerSaveRouter(B_);
 function V_(e = "model") {
-  return new yd(e);
+  return new bd(e);
 }
 function W_(e) {
   return new L_(e);
 }
-function lx(e, t, n, s) {
+function ux(e, t, n, s) {
   i(e), n = n == null ? 0 : n, s = s == null ? 1 : s, o(n, s);
   let r = 0, a = (u) => (u.then((l) => {
     let c = n + ++r / e.length * (s - n);
@@ -5801,8 +5801,8 @@ function lx(e, t, n, s) {
 }
 async function gk(e, t) {
   t == null && (t = {});
-  let n = t.fetchFunc == null ? X().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await lx(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
-  return t.onProgress == null ? await Promise.all(o) : await lx(o, t.onProgress, u, l);
+  let n = t.fetchFunc == null ? X().platform.fetch : t.fetchFunc, s = e.map((p) => n(p, t.requestInit, { isBinary: true })), r = 0, a = 0.5, o = (t.onProgress == null ? await Promise.all(s) : await ux(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
+  return t.onProgress == null ? await Promise.all(o) : await ux(o, t.onProgress, u, l);
 }
 async function U_(e, t = "", n, s) {
   return bk((i) => gk(i, { requestInit: s }))(e, t, n);
@@ -5813,7 +5813,7 @@ function bk(e) {
     if (t.forEach((h, f) => {
       let m = 0;
       h.weights.forEach((g) => {
-        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = nm[b] * pt(g.shape), v = () => {
+        let b = "quantization" in g ? g.quantization.dtype : g.dtype, y = tm[b] * pt(g.shape), v = () => {
           r[f] = true, a[f] == null && (a[f] = []), a[f].push({ manifestEntry: g, groupOffset: m, sizeBytes: y });
         };
         s != null ? s.forEach((x, k) => {
@@ -5852,7 +5852,7 @@ Manifest JSON has weights with names: ${o.join(", ")}.`);
 }
 var G_ = "application/octet-stream";
 var H_ = "application/json";
-var Pg = class {
+var Og = class {
   constructor(e, t) {
     if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (O(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = X().platform.fetch, O(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && O(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
       throw new Error("requestInit is expected to have no pre-existing body, but has one.");
@@ -5867,7 +5867,7 @@ var Pg = class {
     t.body.append("model.json", new Blob([JSON.stringify(s)], { type: H_ }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: G_ }), "model.weights.bin");
     let r = await this.fetch(this.path, t);
     if (r.ok)
-      return { modelArtifactsInfo: Dl(e), responses: [r] };
+      return { modelArtifactsInfo: Rl(e), responses: [r] };
     throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${r.status}.`);
   }
   async load() {
@@ -5884,7 +5884,7 @@ var Pg = class {
     let n = t.modelTopology, s = t.weightsManifest;
     if (n == null && s == null)
       throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
-    return Og(t, (r) => this.loadWeights(r));
+    return Fg(t, (r) => this.loadWeights(r));
   }
   async loadWeights(e) {
     let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = q_(t), r = this.weightPathPrefix || n, a = [];
@@ -5896,36 +5896,36 @@ var Pg = class {
         this.weightUrlConverter != null ? o.push(this.weightUrlConverter(c)) : i.push(r + c + s);
     this.weightUrlConverter && i.push(...await Promise.all(o));
     let u = await gk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
-    return [a, Fg(u)];
+    return [a, Dg(u)];
   }
 };
-Pg.URL_SCHEME_REGEX = /^https?:\/\//;
+Og.URL_SCHEME_REGEX = /^https?:\/\//;
 function q_(e) {
   let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : "";
   return [s + "/", r];
 }
-function om(e) {
-  return e.match(Pg.URL_SCHEME_REGEX) != null;
+function im(e) {
+  return e.match(Og.URL_SCHEME_REGEX) != null;
 }
 var yk = (e, t) => {
   if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null))
     return null;
   {
     let n = true;
-    if (Array.isArray(e) ? n = e.every((s) => om(s)) : n = om(e), n)
-      return zg(e, t);
+    if (Array.isArray(e) ? n = e.every((s) => im(s)) : n = im(e), n)
+      return Pg(e, t);
   }
   return null;
 };
 xt.registerSaveRouter(yk);
 xt.registerLoadRouter(yk);
-function zg(e, t) {
-  return new Pg(e, t);
+function Pg(e, t) {
+  return new Og(e, t);
 }
 function j_(e, t) {
-  return zg(e, t);
+  return Pg(e, t);
 }
-var Pf = class {
+var Of = class {
   constructor(e) {
     this.modelArtifacts = e;
   }
@@ -5942,7 +5942,7 @@ var K_ = class {
   }
 };
 function X_(e, t, n, s) {
-  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Pf(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Pf({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Pf({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
+  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Of(e) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Of({ modelTopology: e })) : (console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release."), new Of({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
 }
 function Y_(e) {
   return new K_(e);
@@ -5960,9 +5960,9 @@ function J_(e, t, n = 1, s = 0) {
   if (t < 2)
     throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
   let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
-  return M.runKernel($o, a, i);
+  return M.runKernel(To, a, i);
 }
-var vd = L({ oneHot_: J_ });
+var yd = L({ oneHot_: J_ });
 function eA(e, t) {
   let n = _(e, "x", "transpose");
   if (t == null && (t = n.shape.map((a, i) => i).reverse()), O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`), t.forEach((a) => {
@@ -5976,12 +5976,12 @@ var qe = L({ transpose_: eA });
 function tA(e, t, n) {
   let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix");
   O(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), O(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), O(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), O(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), O(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
-  let a = vd(ce(s, "int32"), n), i = vd(ce(r, "int32"), n), o = qe(a), u = We(o, i);
+  let a = yd(ce(s, "int32"), n), i = yd(ce(r, "int32"), n), o = qe(a), u = We(o, i);
   return ce(u, "int32");
 }
 var nA = L({ confusionMatrix_: tA });
-var jo = {};
-Ae(jo, { assertAndGetBroadcastShape: () => it, getBroadcastDims: () => vk, getReductionAxes: () => _t });
+var qo = {};
+Ae(qo, { assertAndGetBroadcastShape: () => it, getBroadcastDims: () => vk, getReductionAxes: () => _t });
 function vk(e, t) {
   let n = e.length, s = [];
   for (let r = 0; r < n; r++) {
@@ -6051,9 +6051,9 @@ function wk(e, t = 3) {
     throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${e.constructor.name}`);
   if (r && r && e.readyState < 2)
     throw new Error("The video element has not loaded data yet. Please wait for `loadeddata` event on the <video> element.");
-  if (Qf(fd, M.backendName) != null) {
+  if (Yf(hd, M.backendName) != null) {
     let f = { pixels: e }, m = { numChannels: t };
-    return M.runKernel(fd, f, m);
+    return M.runKernel(hd, f, m);
   }
   let [l, c] = r ? [e.videoWidth, e.videoHeight] : [e.width, e.height], p;
   if (i)
@@ -6167,12 +6167,12 @@ function Ik(e, t) {
   let l = 1;
   for (let p = a; p < n; ++p)
     l *= o[p], u.push(o[p]);
-  let c = [...ao(e.shape).map((p) => p / l), 1].slice(0, a);
+  let c = [...ro(e.shape).map((p) => p / l), 1].slice(0, a);
   return [u, i, l, c];
 }
 var Sk = {};
-Ae(Sk, { calculateShapes: () => Ck, validateInput: () => Lg, validateUpdateShape: () => Mg });
-function Mg(e, t, n) {
+Ae(Sk, { calculateShapes: () => Ck, validateInput: () => Mg, validateUpdateShape: () => zg });
+function zg(e, t, n) {
   let s = t.rank > 1 ? t.shape[t.rank - 1] : 1, r = t.rank > 1 ? t.rank - 1 : 1, a = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: ${s}, and batchDim: ${r}.`;
   if (n.rank < r)
     throw new Error(a + ` update.rank < ${r}. `);
@@ -6187,7 +6187,7 @@ function Mg(e, t, n) {
     if (n.shape[i + r] !== e[i + s])
       throw new Error(a + ` updates.shape[${i + r}] (${n.shape[i + r]}) != shape[${i + r}] (${e[i + r]})`);
 }
-function Lg(e, t, n) {
+function Mg(e, t, n) {
   if (t.rank < 1)
     throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${t.rank}.`);
   if (e.rank < 1)
@@ -6202,18 +6202,18 @@ function Lg(e, t, n) {
     if (e.size === 0)
       throw new Error(`Updates specified for empty output. updates shape: ${e.shape}`);
   }
-  Mg(n, t, e);
+  zg(n, t, e);
 }
 function Ck(e, t, n) {
   let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
   for (let p = r; p < a; ++p)
     i *= n[p];
-  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...ao(n.slice(0, r)), 1], c = pt(n);
+  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...ro(n.slice(0, r)), 1], c = pt(n);
   return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
 }
 var wt = {};
 Ae(wt, { assertParamsValid: () => pA, computeFlatOffset: () => bA, computeOutShape: () => fA, getNormalizedAxes: () => mA, isSliceContinous: () => gA, maskToAxes: () => hA, parseSliceParams: () => Fk, sliceInfo: () => yA, startForAxis: () => Rk, startIndicesWithElidedDims: () => _k, stopForAxis: () => Dk, stopIndicesWithElidedDims: () => Ak, stridesForAxis: () => Ek, stridesWithElidedDims: () => Nk });
-var um = -2;
+var om = -2;
 var dA = -1;
 function pA(e, t, n) {
   let s = e.shape.length;
@@ -6282,7 +6282,7 @@ function Ak(e, t, n, s, r) {
     }
   for (let o = 0; o < a.length; o++) {
     let u = r[o];
-    a[o] < 0 && (a[o] += u), a[o] = Vu(0, a[o], r[o]);
+    a[o] < 0 && (a[o] += u), a[o] = Bu(0, a[o], r[o]);
   }
   return a;
 }
@@ -6294,13 +6294,13 @@ function Rk(e, t, n, s, r, a) {
   let i = t[r], o = n[r] || 1;
   (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MIN_SAFE_INTEGER : i = Number.MAX_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), i = Vu(0, i, u - 1), i;
+  return i < 0 && (i += u), i = Bu(0, i, u - 1), i;
 }
 function Dk(e, t, n, s, r, a) {
   let i = t[r], o = n[r] || 1;
   (e & 1 << r || a & 1 << r || i == null) && (o > 0 ? i = Number.MAX_SAFE_INTEGER : i = Number.MIN_SAFE_INTEGER);
   let u = s[r];
-  return i < 0 && (i += u), o > 0 ? i = Vu(0, i, u) : i = Vu(-1, i, u - 1), i;
+  return i < 0 && (i += u), o > 0 ? i = Bu(0, i, u) : i = Bu(-1, i, u - 1), i;
 }
 function gA(e, t, n) {
   let s = n.length;
@@ -6358,7 +6358,7 @@ function yA(e, t, n, s, r, a, i, o, u) {
         if (d.begin[v] = F, d.end[v] = d.begin[v] + 1, F < 0 || F >= k)
           throw Error(`slice index ${d.begin[v]} of dimension ${v} out of bounds.`);
       } else
-        d.begin[v] = cx(d.begin[v], 0, d.strides[v], k, C, T), d.end[v] = cx(d.end[v], 1, d.strides[v], k, C, T);
+        d.begin[v] = lx(d.begin[v], 0, d.strides[v], k, C, T), d.end[v] = lx(d.end[v], 1, d.strides[v], k, C, T);
       let R = d.strides[v] === 1 && d.begin[v] === 0 && d.end[v] === k;
       h = h && R, f = f && (v === 0 && d.strides[v] === 1 || R);
     } else
@@ -6372,9 +6372,9 @@ function yA(e, t, n, s, r, a, i, o, u) {
   }
   for (let v = 0; v < d.finalShapeGatherIndices.length; ++v) {
     let x = d.finalShapeGatherIndices[v];
-    x >= 0 ? b.push(g[x]) : x === um && b.push(1);
+    x >= 0 ? b.push(g[x]) : x === om && b.push(1);
   }
-  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== um), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
+  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== om), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
 }
 function vA(e, t) {
   t.beginMask = 0, t.endMask = 0, t.shrinkAxisMask = 0;
@@ -6386,14 +6386,14 @@ function vA(e, t) {
       for (; n < r; n++)
         t.begin[n] = 0, t.end[n] = 0, t.strides[n] = 1, t.beginMask |= 1 << n, t.endMask |= 1 << n, t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(-1), t.inputShapeGatherIndicesSparse[n] = s;
     } else if (1 << s & e.newAxisMask)
-      t.finalShapeGatherIndices.push(um), t.finalShapeGatherIndicesSparse.push(-1);
+      t.finalShapeGatherIndices.push(om), t.finalShapeGatherIndicesSparse.push(-1);
     else {
       if (n === t.begin.length)
         throw Error(`Index out of range using input dim ${n}; input has only ${t.dims} dims, ${t.begin.length}.`);
       e.begin != null && (t.begin[n] = e.begin[s]), e.end != null && (t.end[n] = e.end[s]), t.strides[n] = e.strides[s], e.beginMask & 1 << s && (t.beginMask |= 1 << n), e.endMask & 1 << s && (t.endMask |= 1 << n), e.shrinkAxisMask & 1 << s ? (t.finalShapeGatherIndices.push(dA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
     }
 }
-function cx(e, t, n, s, r, a) {
+function lx(e, t, n, s, r, a) {
   if (r[t])
     return n > 0 ? a[t] : a[t + 1 & 1];
   {
@@ -6426,16 +6426,16 @@ function Nr(e) {
   O(e.className != null, () => "Class being registered does not have the static className property defined."), O(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), O(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Hr.register(e);
 }
 var xA = {};
-Ae(xA, { TEST_EPSILON_FLOAT16: () => Pk, encodeStrings: () => zk, expectArrayBuffersEqual: () => TA, expectArraysClose: () => kA, expectArraysEqual: () => SA, expectNumbersClose: () => CA, expectPromiseToFail: () => IA, expectValuesInRange: () => NA, testEpsilon: () => Bg });
+Ae(xA, { TEST_EPSILON_FLOAT16: () => Pk, encodeStrings: () => zk, expectArrayBuffersEqual: () => TA, expectArraysClose: () => kA, expectArraysEqual: () => SA, expectNumbersClose: () => CA, expectPromiseToFail: () => IA, expectValuesInRange: () => NA, testEpsilon: () => Lg });
 var wA = 1e-3;
 var Pk = 0.1;
 function kA(e, t, n) {
-  return n == null && (n = Bg()), lm(e, t, (s, r) => Vg(s, r, n));
+  return n == null && (n = Lg()), um(e, t, (s, r) => Bg(s, r, n));
 }
-function Bg() {
+function Lg() {
   return M.backend.floatPrecision() === 32 ? wA : Pk;
 }
-function lm(e, t, n) {
+function um(e, t, n) {
   let s = true;
   if ((Yt(e) || Yt(t)) && (s = false), Yt(e) && Yt(t) && (s = true), s) {
     let i = e.constructor.name, o = t.constructor.name;
@@ -6465,13 +6465,13 @@ function IA(e, t) {
 }
 function SA(e, t) {
   let n = typeof t == "string" || typeof t == "number" || typeof t == "boolean" ? [t] : t;
-  return ir(e) || ir(e[0]) || ir(t) || ir(t[0]) ? lm(e, n, (s, r) => s == r) : lm(e, t, (s, r) => Vg(s, r, 0));
+  return ir(e) || ir(e[0]) || ir(t) || ir(t[0]) ? um(e, n, (s, r) => s == r) : um(e, t, (s, r) => Bg(s, r, 0));
 }
 function CA(e, t, n) {
-  if (n == null && (n = Bg()), !Vg(e, t, n))
+  if (n == null && (n = Lg()), !Bg(e, t, n))
     throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`);
 }
-function Vg(e, t, n) {
+function Bg(e, t, n) {
   return !isFinite(e) && !isFinite(t) ? true : !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);
 }
 function NA(e, t, n) {
@@ -6490,7 +6490,7 @@ function TA(e, t) {
 function zk(e) {
   for (let t = 0; t < e.length; t++) {
     let n = e[t];
-    Array.isArray(n) ? zk(n) : e[t] = Rl(n);
+    Array.isArray(n) ? zk(n) : e[t] = El(n);
   }
   return e;
 }
@@ -6514,7 +6514,7 @@ function xde() {
 function Ss() {
   return M;
 }
-function cm() {
+function lm() {
   return M.memory();
 }
 function wde(e) {
@@ -6524,7 +6524,7 @@ function j(e, t) {
   return M.tidy(e, t);
 }
 function Re(e) {
-  Eg(e).forEach((n) => n.dispose());
+  Ag(e).forEach((n) => n.dispose());
 }
 function Ht(e) {
   return M.keep(e);
@@ -6550,7 +6550,7 @@ function Tde(e) {
 function $de(e) {
   return M.findBackendFactory(e);
 }
-function pp(e, t, n = 1) {
+function dp(e, t, n = 1) {
   return M.registerBackend(e, t, n);
 }
 function $A() {
@@ -6592,21 +6592,21 @@ function DA(e) {
   let t = _(e, "x", "abs");
   if (t.dtype === "complex64") {
     let n = { x: t };
-    return M.runKernel(Kd, n);
+    return M.runKernel(jd, n);
   } else {
     let n = { x: t };
-    return M.runKernel(io, n);
+    return M.runKernel(ao, n);
   }
 }
 var Mt = L({ abs_: DA });
 function FA(e) {
   let n = { x: _(e, "x", "acos") };
-  return M.runKernel(sl, n);
+  return M.runKernel(nl, n);
 }
 var OA = L({ acos_: FA });
 function PA(e) {
   let n = { x: _(e, "x", "acosh") };
-  return M.runKernel(rl, n);
+  return M.runKernel(sl, n);
 }
 var zA = L({ acosh_: PA });
 function MA(e) {
@@ -6625,67 +6625,67 @@ function MA(e) {
 var LA = L({ addN_: MA });
 function BA(e, t = null, n = false) {
   let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
-  return M.runKernel(al, r, a);
+  return M.runKernel(rl, r, a);
 }
 var Bk = L({ all_: BA });
 function VA(e, t = null, n = false) {
   let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
-  return M.runKernel(il, r, a);
+  return M.runKernel(al, r, a);
 }
-var dm = L({ any_: VA });
+var cm = L({ any_: VA });
 function WA(e, t = 0) {
   let s = { x: _(e, "x", "argMax") }, r = { axis: t };
   return M.runKernel(wa, s, r);
 }
-var Hu = L({ argMax_: WA });
+var Gu = L({ argMax_: WA });
 function UA(e, t = 0) {
   let s = { x: _(e, "x", "argMin") }, r = { axis: t };
-  return M.runKernel(ol, s, r);
+  return M.runKernel(il, s, r);
 }
 var GA = L({ argMin_: UA });
 function HA(e) {
   let n = { x: _(e, "x", "asin") };
-  return M.runKernel(ul, n);
+  return M.runKernel(ol, n);
 }
 var qA = L({ asin_: HA });
 function jA(e) {
   let n = { x: _(e, "x", "asinh") };
-  return M.runKernel(ll, n);
+  return M.runKernel(ul, n);
 }
 var KA = L({ asinh_: jA });
 function XA(e) {
   let n = { x: _(e, "x", "atan") };
-  return M.runKernel(cl, n);
+  return M.runKernel(ll, n);
 }
 var YA = L({ atan_: XA });
 function QA(e, t) {
   let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(pl, r);
+  return M.runKernel(dl, r);
 }
 var ZA = L({ atan2_: QA });
 function JA(e) {
   let n = { x: _(e, "x", "atanh") };
-  return M.runKernel(dl, n);
+  return M.runKernel(cl, n);
 }
 var eE = L({ atanh_: JA });
 function tE(e, t, n, s, r = "NHWC", a) {
   let i = e[3], o = [...t, i], u = Uk(r);
-  return Fl(e, o, n, a, s, null, null, u);
+  return Dl(e, o, n, a, s, null, null, u);
 }
 function Vk(e, t, n, s, r, a, i = "channelsLast") {
-  let [o, u] = xd(t), l;
+  let [o, u] = vd(t), l;
   if (i === "channelsLast")
     l = [o, u, e[3], e[3]];
   else if (i === "channelsFirst")
     l = [o, u, e[1], e[1]];
   else
     throw new Error(`Unknown dataFormat ${i}`);
-  return Fl(e, l, n, s, r, a, false, i);
+  return Dl(e, l, n, s, r, a, false, i);
 }
 function nE(e, t, n, s, r, a, i = "NDHWC") {
-  let [o, u, l] = pm(t), c, p;
+  let [o, u, l] = dm(t), c, p;
   if (i === "NDHWC")
     p = "channelsLast", c = [o, u, l, e[4], e[4]];
   else if (i === "NCDHW")
@@ -6694,7 +6694,7 @@ function nE(e, t, n, s, r, a, i = "NDHWC") {
     throw new Error(`Unknown dataFormat ${i}`);
   return Wk(e, c, n, s, r, false, p, a);
 }
-function Fl(e, t, n, s, r, a, i = false, o = "channelsLast") {
+function Dl(e, t, n, s, r, a, i = false, o = "channelsLast") {
   let [u, l, c, p] = [-1, -1, -1, -1];
   if (o === "channelsLast")
     [u, l, c, p] = e;
@@ -6702,7 +6702,7 @@ function Fl(e, t, n, s, r, a, i = false, o = "channelsLast") {
     [u, p, l, c] = e;
   else
     throw new Error(`Unknown dataFormat ${o}`);
-  let [d, h, , f] = t, [m, g] = xd(n), [b, y] = xd(s), v = ji(d, b), x = ji(h, y), { padInfo: k, outHeight: C, outWidth: T } = aE(r, l, c, m, g, v, x, a, o), E = i ? f * p : f, A;
+  let [d, h, , f] = t, [m, g] = vd(n), [b, y] = vd(s), v = qi(d, b), x = qi(h, y), { padInfo: k, outHeight: C, outWidth: T } = aE(r, l, c, m, g, v, x, a, o), E = i ? f * p : f, A;
   return o === "channelsFirst" ? A = [u, E, C, T] : o === "channelsLast" && (A = [u, C, T, E]), { batchSize: u, dataFormat: o, inHeight: l, inWidth: c, inChannels: p, outHeight: C, outWidth: T, outChannels: E, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: A, filterShape: t };
 }
 function Wk(e, t, n, s, r, a = false, i = "channelsLast", o) {
@@ -6713,30 +6713,30 @@ function Wk(e, t, n, s, r, a = false, i = "channelsLast", o) {
     [u, d, l, c, p] = e;
   else
     throw new Error(`Unknown dataFormat ${i}`);
-  let [h, f, m, , g] = t, [b, y, v] = pm(n), [x, k, C] = pm(s), T = ji(h, x), E = ji(f, k), A = ji(m, C), { padInfo: P, outDepth: R, outHeight: F, outWidth: $ } = iE(r, l, c, p, b, y, v, T, E, A, o), z = a ? g * d : g, W;
+  let [h, f, m, , g] = t, [b, y, v] = dm(n), [x, k, C] = dm(s), T = qi(h, x), E = qi(f, k), A = qi(m, C), { padInfo: P, outDepth: R, outHeight: F, outWidth: $ } = iE(r, l, c, p, b, y, v, T, E, A, o), z = a ? g * d : g, W;
   return i === "channelsFirst" ? W = [u, z, R, F, $] : i === "channelsLast" && (W = [u, R, F, $, z]), { batchSize: u, dataFormat: i, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: R, outHeight: F, outWidth: $, outChannels: z, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: T, effectiveFilterHeight: E, effectiveFilterWidth: A, dilationDepth: x, dilationHeight: k, dilationWidth: C, inShape: e, outShape: W, filterShape: t };
 }
 function sE(e, t, n, s, r) {
-  s == null && (s = Wg(e, t, n));
+  s == null && (s = Vg(e, t, n));
   let a = e[0], i = e[1], o = Qr((a - t + 2 * s) / n + 1, r), u = Qr((i - t + 2 * s) / n + 1, r);
   return [o, u];
 }
 function rE(e, t, n, s, r, a) {
-  r == null && (r = Wg(e, t, s));
+  r == null && (r = Vg(e, t, s));
   let i = e[0], o = e[1], u = e[2], l = Qr((i - t + 2 * r) / s + 1, a), c = Qr((o - t + 2 * r) / s + 1, a), p = Qr((u - t + 2 * r) / s + 1, a);
   return [l, c, p, n];
 }
-function Wg(e, t, n, s = 1) {
-  let r = ji(t, s);
+function Vg(e, t, n, s = 1) {
+  let r = qi(t, s);
   return Math.floor((e[0] * (n - 1) - n + r) / 2);
 }
-function xd(e) {
+function vd(e) {
   return typeof e == "number" ? [e, e, e] : e.length === 2 ? [e[0], e[1], 1] : e;
 }
-function pm(e) {
+function dm(e) {
   return typeof e == "number" ? [e, e, e] : e;
 }
-function ji(e, t) {
+function qi(e, t) {
   return t <= 1 ? e : e + (e - 1) * (t - 1);
 }
 function aE(e, t, n, s, r, a, i, o, u) {
@@ -6789,7 +6789,7 @@ function Qr(e, t) {
   }
 }
 function fr(e) {
-  let [t, n, s] = xd(e);
+  let [t, n, s] = vd(e);
   return t === 1 && n === 1 && s === 1;
 }
 function Ps(e, t) {
@@ -6807,11 +6807,11 @@ function pn(e, t, n) {
     if (typeof t == "string")
       throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
     if (typeof t == "number")
-      O(Yi(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
+      O(Xi(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
     else if (typeof t == "object")
       t.forEach((s) => {
         s.forEach((r) => {
-          O(Yi(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
+          O(Xi(r), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${r}.`);
         });
       });
     else
@@ -6820,7 +6820,7 @@ function pn(e, t, n) {
 }
 function oE(e, t) {
   let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
-  return M.runKernel(Eo, s, r);
+  return M.runKernel(Ao, s, r);
 }
 var G = L({ reshape_: oE });
 function uE(e, t, n, s, r) {
@@ -6831,17 +6831,17 @@ function uE(e, t, n, s, r) {
   let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(ka, l, c);
   return p = ce(p, a.dtype), u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var Ug = L({ avgPool_: uE });
+var Wg = L({ avgPool_: uE });
 function lE(e, t, n, s, r, a = "NDHWC") {
   let i = _(e, "x", "avgPool3d", "float32"), o = i, u = false;
   i.rank === 4 && (u = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), pn("avgPool3d", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(qd, l, c);
+  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(Hd, l, c);
   return p = ce(p, o.dtype), u ? G(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
 }
 var Gk = L({ avgPool3d_: lE });
 function cE(e, t = 0) {
   O(e.length >= 1, () => "Pass at least one tensor to concat");
-  let n = Gu(e, "tensors", "concat", "string_or_numeric");
+  let n = Uu(e, "tensors", "concat", "string_or_numeric");
   if (n[0].dtype === "complex64" && n.forEach((a) => {
     if (a.dtype !== "complex64")
       throw new Error(`Cannot concatenate complex64 tensors with a tensor
@@ -6849,7 +6849,7 @@ function cE(e, t = 0) {
   }), n.length === 1)
     return lr(n[0]);
   let s = n, r = { axis: t };
-  return M.runKernel(uo, s, r);
+  return M.runKernel(oo, s, r);
 }
 var Ft = L({ concat_: cE });
 function dE(e) {
@@ -6862,16 +6862,16 @@ function pE(e, t, n) {
   if (s.rank === 0)
     throw new Error("Slicing scalar is not possible");
   let r = { x: s }, a = { begin: t, size: n };
-  return M.runKernel(Po, r, a);
+  return M.runKernel(Oo, r, a);
 }
 var He = L({ slice_: pE });
 function hE(e) {
   let n = { x: _(e, "x", "tanh", "float32") };
   return M.runKernel(li, n);
 }
-var qu = L({ tanh_: hE });
+var Hu = L({ tanh_: hE });
 function fE(e, t, n, s, r, a) {
-  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = He(f, [0, 0], b), v = He(f, [0, g], b), x = He(f, [0, g * 2], b), k = He(f, [0, g * 3], b), C = ie(V(qs(y), qu(v)), V(c, qs(ie(i, x)))), T = V(qu(C), qs(k));
+  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = He(f, [0, 0], b), v = He(f, [0, g], b), x = He(f, [0, g * 2], b), k = He(f, [0, g * 3], b), C = ie(V(qs(y), Hu(v)), V(c, qs(ie(i, x)))), T = V(Hu(C), qs(k));
   return [C, T];
 }
 var Ade = L({ basicLSTMCell_: fE });
@@ -6879,9 +6879,9 @@ function mE(e, t, n) {
   let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
   O(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), O(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), O(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
   let a = { x: s }, i = { blockShape: t, crops: n };
-  return M.runKernel(oo, a, i);
+  return M.runKernel(io, a, i);
 }
-var Gg = L({ batchToSpaceND_: mE });
+var Ug = L({ batchToSpaceND_: mE });
 function gE(e) {
   let t;
   return e.rank === 0 || e.rank === 1 ? t = G(e, [1, 1, 1, e.size]) : e.rank === 2 ? t = G(e, [1, 1, e.shape[0], e.shape[1]]) : e.rank === 3 ? t = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]) : t = e, t;
@@ -6895,33 +6895,33 @@ function bE(e, t, n, s, r, a) {
   let d = { x: gE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = M.runKernel(Pa, d, h);
   return G(f, i.shape);
 }
-var ju = L({ batchNorm_: bE });
+var qu = L({ batchNorm_: bE });
 function yE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
 }
 var vE = L({ batchNorm2d_: yE });
 function xE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
 }
 var wE = L({ batchNorm3d_: xE });
 function kE(e, t, n, s, r, a) {
   let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
   r != null && (l = _(r, "scale", "batchNorm"));
   let c;
-  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), ju(i, o, u, c, l, a);
+  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
 }
 var IE = L({ batchNorm4d_: kE });
 function SE(e, t, n) {
   let s = _(e, "x", "bincount"), r = _(t, "weights", "bincount");
   O(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(r.size === s.size || r.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);
   let a = { x: s, weights: r }, i = { size: n };
-  return M.runKernel(og, a, i);
+  return M.runKernel(ig, a, i);
 }
 var Hk = L({ bincount_: SE });
 function CE(e, t) {
@@ -6931,7 +6931,7 @@ function CE(e, t) {
   if (s.rank !== 1)
     throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);
   let r = { s0: n, s1: s };
-  return M.runKernel(ug, r);
+  return M.runKernel(og, r);
 }
 var NE = L({ broadcastArgs_: CE });
 function TE(e, t) {
@@ -6957,7 +6957,7 @@ function TE(e, t) {
   let o = { x: n }, u = { reps: a };
   return M.runKernel(Sr, o, u);
 }
-var nd = L({ broadcastTo_: TE });
+var td = L({ broadcastTo_: TE });
 function $E(e) {
   let n = { x: _(e, "x", "ceil", "float32") };
   return M.runKernel(Ca, n);
@@ -7011,16 +7011,16 @@ function VE(e, t, n, s, r, a = "NHWC", i) {
   let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, inputShape: o }, f = M.runKernel(Ta, d, h);
   return l ? G(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
 }
-var Hg = L({ conv2DBackpropInput_: VE });
+var Gg = L({ conv2DBackpropInput_: VE });
 function WE(e, t, n, s, r, a) {
   let i = _(e, "x", "conv2dTranspose"), o = _(t, "filter", "conv2dTranspose");
-  return Hg(n, i, o, s, r, "NHWC", a);
+  return Gg(n, i, o, s, r, "NHWC", a);
 }
 var jk = L({ conv2dTranspose_: WE });
 function UE(e, t, n, s, r = "NDHWC", a = [1, 1, 1]) {
   let i = _(e, "x", "conv3d"), o = _(t, "filter", "conv3d"), u = i, l = false;
   i.rank === 4 && (l = true, u = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), O(o.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`), O(u.shape[4] === o.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`), O(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
-  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = M.runKernel(Xd, c, p);
+  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = M.runKernel(Kd, c, p);
   return l ? G(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
 }
 var Kk = L({ conv3d_: UE });
@@ -7030,7 +7030,7 @@ function GE(e, t, n, s, r) {
   t.rank === 4 && (o = true, i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]]), a = [1, e[0], e[1], e[2], e[3]]);
   let u = a[4], l = i.shape[4];
   O(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), O(i.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`), O(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), O(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), O(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
-  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = M.runKernel(dg, c, p);
+  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = M.runKernel(cg, c, p);
   return o ? G(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
 }
 var Xk = L({ conv3DBackpropInput_: GE });
@@ -7043,7 +7043,7 @@ function jE(e) {
   let n = { x: _(e, "x", "cos", "float32") };
   return M.runKernel($a, n);
 }
-var qg = L({ cos_: jE });
+var Hg = L({ cos_: jE });
 function KE(e) {
   let n = { x: _(e, "x", "cosh", "float32") };
   return M.runKernel(_a, n);
@@ -7051,14 +7051,14 @@ function KE(e) {
 var Yk = L({ cosh_: KE });
 function XE(e, t = 0, n = false, s = false) {
   let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
-  return M.runKernel(lo, a, i);
+  return M.runKernel(uo, a, i);
 }
 var Qk = L({ cumsum_: XE });
 function YE(e, t, n, s = false) {
   let r = _(e, "x", "denseBincount"), a = _(t, "weights", "denseBincount");
   O(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), O(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(a.size === r.size || a.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);
   let i = { x: r, weights: a }, o = { size: n, binaryOutput: s };
-  return M.runKernel(pg, i, o);
+  return M.runKernel(dg, i, o);
 }
 var QE = L({ denseBincount_: YE });
 function ZE(e, t, n = "NHWC") {
@@ -7069,7 +7069,7 @@ function ZE(e, t, n = "NHWC") {
     ${a} and ${t} for depthToSpace with input shape
         ${s.shape}`), O(i % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${i} for depthToSpace with input shape ${s.shape}`);
   let o = { x: s }, u = { blockSize: t, dataFormat: n };
-  return M.runKernel(po, o, u);
+  return M.runKernel(co, o, u);
 }
 var JE = L({ depthToSpace_: ZE });
 function eR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
@@ -7078,10 +7078,10 @@ function eR(e, t, n, s, r = "NHWC", a = [1, 1], i) {
   let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, h = M.runKernel(Aa, p, d);
   return c ? G(h, [h.shape[1], h.shape[2], h.shape[3]]) : h;
 }
-var hp = L({ depthwiseConv2d_: eR });
+var pp = L({ depthwiseConv2d_: eR });
 function tR(e) {
   let n = { x: _(e, "x", "diag") };
-  return M.runKernel(mg, n);
+  return M.runKernel(fg, n);
 }
 var Ede = L({ diag_: tR });
 function nR(e, t, n, s, r = [1, 1], a = "NHWC") {
@@ -7089,7 +7089,7 @@ function nR(e, t, n, s, r = [1, 1], a = "NHWC") {
   O(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), O(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), O(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
   let u = i, l = false;
   i.rank === 3 && (u = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), l = true);
-  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = M.runKernel(Yd, c, p);
+  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = M.runKernel(Xd, c, p);
   return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
 }
 var sR = L({ dilation2d_: nR });
@@ -7097,17 +7097,17 @@ function rR(e, t) {
   let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
   [n, s] = vt(n, s), it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(ho, r);
+  return M.runKernel(po, r);
 }
 var qn = L({ equal_: rR });
 function aR(e, t, n) {
-  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = it(it(a.shape, s.shape), r.shape), o = nd(a, i), u = nd(s, i), l = nd(r, i), c = { condition: o, t: u, e: l };
-  return M.runKernel(Oo, c);
+  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = it(it(a.shape, s.shape), r.shape), o = td(a, i), u = td(s, i), l = td(r, i), c = { condition: o, t: u, e: l };
+  return M.runKernel(Fo, c);
 }
 var vn = L({ where_: aR });
 function iR(e) {
   let n = { x: _(e, "x", "zerosLike") };
-  return M.runKernel(Ho, n);
+  return M.runKernel(Go, n);
 }
 var je = L({ zerosLike_: iR });
 function oR(e, t) {
@@ -7138,19 +7138,19 @@ function lR(e, t) {
 var Rde = L({ dot_: lR });
 function cR(e, ...t) {
   let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
-  return M.runKernel(Qd, n, s);
+  return M.runKernel(Yd, n, s);
 }
 var dR = L({ einsum_: cR });
 function pR(e) {
   let n = { x: _(e, "x", "elu", "float32") };
   return M.runKernel(Ra, n);
 }
-var fp = L({ elu_: pR });
+var hp = L({ elu_: pR });
 function hR(e) {
   let t = _(e, "x", "erf");
   O(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = ce(t, "float32"));
   let n = { x: t };
-  return M.runKernel(hl, n);
+  return M.runKernel(pl, n);
 }
 var fR = L({ erf_: hR });
 function mR(e) {
@@ -7162,12 +7162,12 @@ function gR(e, t = 0) {
   let n = _(e, "x", "expandDims", "string_or_numeric");
   O(t <= n.rank, () => "Axis must be <= rank of the tensor");
   let s = { input: n }, r = { dim: t };
-  return M.runKernel(fo, s, r);
+  return M.runKernel(ho, s, r);
 }
 var On = L({ expandDims_: gR });
 function bR(e) {
   let n = { x: _(e, "x", "expm1") };
-  return M.runKernel(mo, n);
+  return M.runKernel(fo, n);
 }
 var yR = L({ expm1_: bR });
 function vR(e, t) {
@@ -7194,25 +7194,25 @@ function xR(e, t, n, s = "float32") {
   throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`);
 }
 var Zk = L({ eye_: xR });
-function Ol(e, t, n) {
+function Fl(e, t, n) {
   let s = { shape: e, value: t, dtype: n };
-  return M.runKernel(fl, {}, s);
+  return M.runKernel(hl, {}, s);
 }
 function wR(e) {
   let n = { x: _(e, "x", "floor", "float32") };
   return M.runKernel(Fa, n);
 }
-var mp = L({ floor_: wR });
+var fp = L({ floor_: wR });
 function kR(e, t, n = 0, s = 0) {
   let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
-  return M.runKernel(bo, i, o);
+  return M.runKernel(go, i, o);
 }
-var Ku = L({ gather_: kR });
+var ju = L({ gather_: kR });
 function IR(e, t) {
   let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
   [n, s] = vt(n, s), it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(vo, r);
+  return M.runKernel(yo, r);
 }
 var Wn = L({ greater_: IR });
 function SR(e, t) {
@@ -7221,59 +7221,59 @@ function SR(e, t) {
   let r = { a: n, b: s };
   return M.runKernel(za, r);
 }
-var Ko = L({ greaterEqual_: SR });
+var jo = L({ greaterEqual_: SR });
 function CR(e) {
   let n = { input: _(e, "input", "imag") };
-  return M.runKernel(Zd, n);
+  return M.runKernel(Qd, n);
 }
-var jg = L({ imag_: CR });
+var qg = L({ imag_: CR });
 function NR(e) {
   let n = { x: _(e, "x", "isFinite") };
-  return M.runKernel(ml, n);
+  return M.runKernel(fl, n);
 }
 var Dde = L({ isFinite_: NR });
 function TR(e) {
   let n = { x: _(e, "x", "isInf") };
-  return M.runKernel(gl, n);
+  return M.runKernel(ml, n);
 }
 var Fde = L({ isInf_: TR });
 function $R(e) {
   let n = { x: _(e, "x", "isNaN") };
-  return M.runKernel(bl, n);
+  return M.runKernel(gl, n);
 }
 var _R = L({ isNaN_: $R });
 function AR(e, t = 0.2) {
   let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
   return M.runKernel(La, s, r);
 }
-var Kg = L({ leakyRelu_: AR });
+var jg = L({ leakyRelu_: AR });
 function ER(e, t) {
   let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
   [n, s] = vt(n, s), it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(xo, r);
+  return M.runKernel(vo, r);
 }
 var Jk = L({ less_: ER });
 function RR(e, t) {
   let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
   [n, s] = vt(n, s), it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(wo, r);
+  return M.runKernel(xo, r);
 }
-var Xo = L({ lessEqual_: RR });
+var Ko = L({ lessEqual_: RR });
 function DR(e, t, n) {
   if (n <= 0)
     throw new Error("The number of values should be positive.");
   let s = { start: e, stop: t, num: n };
-  return M.runKernel(vg, {}, s);
+  return M.runKernel(yg, {}, s);
 }
 function FR(e, t = 5, n = 1, s = 1, r = 0.5) {
   let a = _(e, "x", "localResponseNormalization");
   O(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
-               rank ${a.rank}.`), O(Yi(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
+               rank ${a.rank}.`), O(Xi(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
   let i = a, o = false;
   a.rank === 3 && (o = true, i = G(a, [1, a.shape[0], a.shape[1], a.shape[2]]));
-  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = M.runKernel(ep, u, l);
+  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = M.runKernel(Jd, u, l);
   return o ? G(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
 }
 var OR = L({ localResponseNormalization_: FR });
@@ -7284,25 +7284,25 @@ function PR(e) {
 var Kn = L({ log_: PR });
 function zR(e) {
   let n = { x: _(e, "x", "log1p") };
-  return M.runKernel(yl, n);
+  return M.runKernel(bl, n);
 }
-var Xg = L({ log1p_: zR });
+var Kg = L({ log1p_: zR });
 function Ode(e) {
   return O(hr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
     let s = _(t, "x", "tf.grad", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grad") : null;
     return M.tidy(() => {
       let { value: a, grads: i } = M.gradients(() => e(s), [s], r);
-      return r != null && dn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), gp(i), i[0];
+      return r != null && dn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), mp(i), i[0];
     });
   };
 }
 function Pde(e) {
   return O(hr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
     O(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
-    let s = Gu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
+    let s = Uu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
     return M.tidy(() => {
       let { value: a, grads: i } = M.gradients(() => e(...s), s, r);
-      return r != null && dn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), gp(i), i;
+      return r != null && dn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), mp(i), i;
     });
   };
 }
@@ -7310,18 +7310,18 @@ function zde(e) {
   return O(hr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
     O(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
     let { grads: s, value: r } = M.gradients(() => e(t), [t], n);
-    return gp(s), { grad: s[0], value: r };
+    return mp(s), { grad: s[0], value: r };
   };
 }
 function Mde(e) {
   return O(hr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
     O(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
     let s = M.gradients(() => e(...t), t, n);
-    return n != null && dn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), gp(s.grads), s;
+    return n != null && dn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), mp(s.grads), s;
   };
 }
 function MR(e, t) {
-  O(hr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof gd), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
+  O(hr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof md), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
   let n = t != null;
   if (!n) {
     t = [];
@@ -7340,24 +7340,24 @@ function MR(e, t) {
 function js(e) {
   return M.customGrad(e);
 }
-function gp(e) {
+function mp(e) {
   if (e.filter((n) => n == null).length > 0)
     throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
     the f you passed encloses all operations that lead from x to y.`);
 }
 function LR(e) {
   let n = { x: _(e, "x", "neg") };
-  return M.runKernel(Io, n);
+  return M.runKernel(ko, n);
 }
 var kt = L({ neg_: LR });
 function BR(e) {
   let n = { x: _(e, "x", "softplus") };
-  return M.runKernel(Tl, n);
+  return M.runKernel(Nl, n);
 }
-var Pl = L({ softplus_: BR });
+var Ol = L({ softplus_: BR });
 function VR(e) {
   let t = _(e, "x", "logSigmoid");
-  return js((s) => ({ value: kt(Pl(kt(s))), gradFunc: (i) => V(i, qs(kt(s))) }))(t);
+  return js((s) => ({ value: kt(Ol(kt(s))), gradFunc: (i) => V(i, qs(kt(s))) }))(t);
 }
 var Lde = L({ logSigmoid_: VR });
 function WR(e, t = null, n = false) {
@@ -7392,7 +7392,7 @@ function HR(e, t = -1) {
   })(n);
 }
 var eI = L({ logSoftmax_: HR });
-function Yg(e, t) {
+function Xg(e, t) {
   for (let n = 0; n < e.length; ++n)
     if (e[e.length - n - 1] !== t - 1 - n)
       return false;
@@ -7416,17 +7416,17 @@ function la(e, t) {
   return tI(e, n, t);
 }
 function qR(e, t, n) {
-  O(Yg(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
+  O(Xg(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
 }
 function sI(e, t) {
-  if (Yg(e, t))
+  if (Xg(e, t))
     return null;
   let n = [];
   for (let s = 0; s < t; ++s)
     e.indexOf(s) === -1 && n.push(s);
   return e.forEach((s) => n.push(s)), n;
 }
-function Qg(e) {
+function Yg(e) {
   return e.map((t, n) => [n, t]).sort((t, n) => t[1] - n[1]).map((t) => t[0]);
 }
 function jR(e, t) {
@@ -7448,24 +7448,24 @@ function YR(e, t) {
   let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
   it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(ko, r);
+  return M.runKernel(wo, r);
 }
 var Ds = L({ logicalAnd_: YR });
 function QR(e) {
   let n = { x: _(e, "x", "logicalNot", "bool") };
-  return M.runKernel(vl, n);
+  return M.runKernel(yl, n);
 }
-var Zg = L({ logicalNot_: QR });
+var Qg = L({ logicalNot_: QR });
 function ZR(e, t) {
   let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
   it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(Jd, r);
+  return M.runKernel(Zd, r);
 }
 var rI = L({ logicalOr_: ZR });
 function JR(e, t) {
   let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
-  return it(n.shape, s.shape), Ds(rI(e, t), Zg(Ds(e, t)));
+  return it(n.shape, s.shape), Ds(rI(e, t), Qg(Ds(e, t)));
 }
 var Bde = L({ logicalXor_: JR });
 function eD(e, t, n, s, r) {
@@ -7474,16 +7474,16 @@ function eD(e, t, n, s, r) {
   let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(Ua, l, c);
   return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
-var Jg = L({ maxPool_: eD });
+var Zg = L({ maxPool_: eD });
 function tD(e, t = [1, 1, 1], n, s, r, a = "NDHWC") {
   let i = _(e, "x", "maxPool3d"), o = i, u = false;
   i.rank === 4 && (u = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), pn("maxPool3d", s, r);
-  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(tp, l, c);
+  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(ep, l, c);
   return u ? G(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
 }
 var aI = L({ maxPool3d_: tD });
 function nD(e, t, n, s, r = false) {
-  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = M.runKernel(Ig, i, o);
+  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = M.runKernel(kg, i, o);
   return { result: u[0], indexes: u[1] };
 }
 var sD = L({ maxPoolWithArgmax_: nD });
@@ -7504,7 +7504,7 @@ function $t(e, t = "float32") {
     let s = $t(e, "float32"), r = $t(e, "float32");
     return aa(s, r);
   }
-  let n = Hd(pt(e), t);
+  let n = Gd(pt(e), t);
   return M.makeTensor(n, e, t);
 }
 function zn(e, t = "float32") {
@@ -7512,7 +7512,7 @@ function zn(e, t = "float32") {
     let s = zn(e, "float32"), r = $t(e, "float32");
     return aa(s, r);
   }
-  let n = tg(pt(e), t);
+  let n = eg(pt(e), t);
   return M.makeTensor(n, e, t);
 }
 function Vde(e, t, { indexing: n = "xy" } = {}) {
@@ -7530,14 +7530,14 @@ function iD(e, t = null, n = false) {
   let r = { x: _(e, "x", "min") }, a = { axis: t, keepDims: n };
   return M.runKernel(Ha, r, a);
 }
-var hm = L({ min_: iD });
+var pm = L({ min_: iD });
 function oD(e, t) {
   let n = _(e, "a", "minimum"), s = _(t, "b", "minimum");
   [n, s] = vt(n, s), n.dtype === "bool" && (n = ce(n, "int32"), s = ce(s, "int32")), it(n.shape, s.shape);
   let r = { a: n, b: s };
   return M.runKernel(qa, r);
 }
-var bp = L({ minimum_: oD });
+var gp = L({ minimum_: oD });
 function uD(e, t, n) {
   O(n === "reflect" || n === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);
   let s = _(e, "x", "mirrorPad");
@@ -7555,7 +7555,7 @@ function cD(e, t) {
   let n = _(e, "a", "mod"), s = _(t, "b", "mod");
   [n, s] = vt(n, s);
   let r = { a: n, b: s };
-  return M.runKernel(xl, r);
+  return M.runKernel(vl, r);
 }
 var dD = L({ mod_: cD });
 function pD(e) {
@@ -7570,9 +7570,9 @@ function hD(e, t = null, n = false) {
   let i = ct(ge(ce(e, "float32"), G(r, a))), o = It(i, s, n);
   return { mean: r, variance: o };
 }
-var eb = L({ moments_: hD });
+var Jg = L({ moments_: hD });
 function fD(e, t, n, s) {
-  let r = _(t, "data", "multiRNNCell"), a = Gu(n, "c", "multiRNNCell"), i = Gu(s, "h", "multiRNNCell"), o = r, u = [];
+  let r = _(t, "data", "multiRNNCell"), a = Uu(n, "c", "multiRNNCell"), i = Uu(s, "h", "multiRNNCell"), o = r, u = [];
   for (let p = 0; p < e.length; p++) {
     let d = e[p](o, a[p], i[p]);
     u.push(d[0]), u.push(d[1]), o = d[1];
@@ -7590,7 +7590,7 @@ function mD(e, t, n, s = false) {
   if (i > 2)
     throw new Error(`Rank of probabilities must be 1 or 2, but is ${i}`);
   n = n || Math.random();
-  let u = { logits: i === 1 ? G(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = M.runKernel(Sg, u, l);
+  let u = { logits: i === 1 ? G(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = M.runKernel(Ig, u, l);
   return i === 1 ? G(c, [c.size]) : c;
 }
 var gD = L({ multinomial_: mD });
@@ -7598,12 +7598,12 @@ function bD(e, t) {
   let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
   [n, s] = vt(n, s), it(n.shape, s.shape);
   let r = { a: n, b: s };
-  return M.runKernel(So, r);
+  return M.runKernel(Io, r);
 }
-var Xu = L({ notEqual_: bD });
+var Ku = L({ notEqual_: bD });
 function yD(e) {
   let n = { x: _(e, "x", "onesLike") };
-  return M.runKernel(To, n);
+  return M.runKernel(No, n);
 }
 var Xn = L({ onesLike_: yD });
 function vD(e, t) {
@@ -7641,16 +7641,16 @@ function CD(e, t, n) {
   let s = _(e, "x", "spaceToBatchND");
   O(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), O(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), O(s.shape.reduce((i, o, u) => u > 0 && u <= t.length ? i && (o + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : i, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
   let r = { x: s }, a = { blockShape: t, paddings: n };
-  return M.runKernel(Mo, r, a);
+  return M.runKernel(zo, r, a);
 }
-var tb = L({ spaceToBatchND_: CD });
+var eb = L({ spaceToBatchND_: CD });
 function ND(e, t, n, s, r, a, i) {
   r == null && (r = [1, 1]), a == null && (a = 1), s === 0 && (s = "valid");
   let o = _(e, "x", "maxPool"), u = o, l = false;
   o.rank === 3 && (l = true, u = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
   let c = Vk(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
   s === "same" ? d = $D([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
-  let h = p[0] === 1 && p[1] === 1, [f, m] = TD([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : tb(u, p, f), v = (n === "avg" ? () => Ug(b, t, a, g, i) : () => Jg(b, t, a, g, i))(), x = h ? v : Gg(v, p, m);
+  let h = p[0] === 1 && p[1] === 1, [f, m] = TD([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : eb(u, p, f), v = (n === "avg" ? () => Wg(b, t, a, g, i) : () => Zg(b, t, a, g, i))(), x = h ? v : Ug(v, p, m);
   return l ? G(x, [x.shape[1], x.shape[2], x.shape[3]]) : x;
 }
 function TD(e, t, n) {
@@ -7673,12 +7673,12 @@ function AD(e, t) {
   let n = _(e, "x", "prelu"), s = _(t, "alpha", "prelu"), r = { x: n, alpha: s };
   return M.runKernel(Qa, r);
 }
-var nb = L({ prelu_: AD });
+var tb = L({ prelu_: AD });
 function ED(e, t = null, n = false) {
   let s = _(e, "x", "prod");
   s.dtype === "bool" && (s = ce(s, "int32"));
   let r = { x: s }, a = { axis: t, keepDims: n };
-  return M.runKernel(Ao, r, a);
+  return M.runKernel(_o, r, a);
 }
 var iI = L({ prod_: ED });
 function RD(e, t, n) {
@@ -7696,12 +7696,12 @@ function RD(e, t, n) {
   return M.makeTensor(r, e, n);
 }
 var Xde = L({ rand_: RD });
-var sb = ya(Wd());
-var rb = class {
+var nb = ya(Vd());
+var sb = class {
   constructor(e, t, n, s, r) {
     this.mean = e, this.stdDev = t, this.dtype = n, this.nextVal = NaN, this.truncated = s, this.truncated && (this.upper = this.mean + this.stdDev * 2, this.lower = this.mean - this.stdDev * 2);
     let a = r || Math.random();
-    this.random = sb.alea(a.toString());
+    this.random = nb.alea(a.toString());
   }
   nextValue() {
     if (!isNaN(this.nextVal)) {
@@ -7730,7 +7730,7 @@ var DD = class {
   constructor(e, t, n, s) {
     this.alpha = e, this.beta = 1 / t, this.dtype = n;
     let r = s || Math.random();
-    this.randu = sb.alea(r.toString()), this.randn = new rb(0, 1, n, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
+    this.randu = nb.alea(r.toString()), this.randn = new sb(0, 1, n, false, this.randu()), e < 1 ? this.d = e + 2 / 3 : this.d = e - 1 / 3, this.c = 1 / Math.sqrt(9 * this.d);
   }
   nextValue() {
     let e, t, n, s, r, a;
@@ -7751,7 +7751,7 @@ var FD = class {
   constructor(e = 0, t = 1, n, s) {
     if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t - e, this.dtype = n, s == null && (s = Math.random()), typeof s == "number" && (s = s.toString()), !this.canReturnFloat() && this.range <= 1)
       throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);
-    this.random = sb.alea(s);
+    this.random = nb.alea(s);
   }
   convertValue(e) {
     return this.canReturnFloat() ? e : Math.round(e);
@@ -7772,7 +7772,7 @@ var Yde = L({ randomGamma_: OD });
 function PD(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error(`Unsupported data type ${s}`);
-  let a = new rb(t, n, s, false, r), i = De(e, s);
+  let a = new sb(t, n, s, false, r), i = De(e, s);
   for (let o = 0; o < i.values.length; o++)
     i.values[o] = a.nextValue();
   return i.toTensor();
@@ -7784,21 +7784,21 @@ function MD(e, t = 0, n = 1, s = "float32", r) {
     a.values[o] = i.nextValue();
   return a.toTensor();
 }
-var zl = L({ randomUniform_: MD });
-function Yu(e, t, n = 1, s = "float32") {
+var Pl = L({ randomUniform_: MD });
+function Xu(e, t, n = 1, s = "float32") {
   if (n === 0)
     throw new Error("Cannot have a step of zero");
   let r = { start: e, stop: t, step: n, dtype: s };
-  return M.runKernel(kl, {}, r);
+  return M.runKernel(wl, {}, r);
 }
 function LD(e) {
   let n = { input: _(e, "input", "real") };
-  return M.runKernel(np, n);
+  return M.runKernel(tp, n);
 }
-var wd = L({ real_: LD });
+var xd = L({ real_: LD });
 function BD(e) {
   let n = { x: _(e, "x", "reciprocal") };
-  return M.runKernel(Il, n);
+  return M.runKernel(kl, n);
 }
 var VD = L({ reciprocal_: BD });
 function WD(e) {
@@ -7813,7 +7813,7 @@ function UD(e) {
 var oI = L({ relu6_: UD });
 function GD(e, t) {
   let s = { x: _(e, "x", "reverse") }, r = { dims: t };
-  return M.runKernel(Ro, s, r);
+  return M.runKernel(Eo, s, r);
 }
 var Yn = L({ reverse_: GD });
 function HD(e) {
@@ -7838,7 +7838,7 @@ function KD(e, t) {
 var epe = L({ reverse4d_: KD });
 function XD(e) {
   let n = { x: _(e, "x", "round") };
-  return M.runKernel(Do, n);
+  return M.runKernel(Ro, n);
 }
 var uI = L({ round_: XD });
 function YD(e) {
@@ -7855,7 +7855,7 @@ function Ie(e, t) {
 }
 function QD(e) {
   let n = { x: _(e, "x", "selu") };
-  return M.runKernel(Cl, n);
+  return M.runKernel(Sl, n);
 }
 var cI = L({ selu_: QD });
 function ZD(e, t, n, s, r, a = [1, 1], i = "NHWC") {
@@ -7865,7 +7865,7 @@ function ZD(e, t, n, s, r, a = [1, 1], i = "NHWC") {
   O(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), O(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), O(l.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);
   let d = u.shape[2], h = u.shape[3];
   O(l.shape[2] === d * h, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${d * h}, but got ${l.shape[2]}.`);
-  let f = hp(c, u, s, r, i, a), g = ua(f, l, 1, "valid", i);
+  let f = pp(c, u, s, r, i, a), g = ua(f, l, 1, "valid", i);
   return p ? G(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
 }
 var JD = L({ separableConv2d_: ZD });
@@ -7883,7 +7883,7 @@ async function e3(e, t) {
 var t3 = e3;
 function n3(e) {
   let n = { x: _(e, "x", "sign") };
-  return M.runKernel(Nl, n);
+  return M.runKernel(Cl, n);
 }
 var s3 = L({ sign_: n3 });
 function r3(e) {
@@ -7893,14 +7893,14 @@ function r3(e) {
 var dI = L({ sin_: r3 });
 function a3(e) {
   let n = { x: _(e, "x", "sinh") };
-  return M.runKernel(zo, n);
+  return M.runKernel(Po, n);
 }
 var pI = L({ sinh_: a3 });
 function i3(e, t, n) {
   let s = _(e, "x", "slice1d");
   return O(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), He(s, [t], [n]);
 }
-var ab = L({ slice1d_: i3 });
+var rb = L({ slice1d_: i3 });
 function o3(e, t, n) {
   let s = _(e, "x", "slice2d");
   return O(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
@@ -7910,12 +7910,12 @@ function u3(e, t, n) {
   let s = _(e, "x", "slice3d");
   return O(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
 }
-var ib = L({ slice3d_: u3 });
+var ab = L({ slice3d_: u3 });
 function l3(e, t, n) {
   let s = _(e, "x", "slice4d");
   return O(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
 }
-var kd = L({ slice4d_: l3 });
+var wd = L({ slice4d_: l3 });
 function c3(e, t = -1) {
   let n = _(e, "logits", "softmax", "float32");
   if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
@@ -7923,29 +7923,29 @@ function c3(e, t = -1) {
   let s = { logits: n }, r = { dim: t };
   return M.runKernel(ii, s, r);
 }
-var ob = L({ softmax_: c3 });
+var ib = L({ softmax_: c3 });
 function d3(e) {
   O(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
   let t = { input: e };
-  return M.runKernel(bg, t);
+  return M.runKernel(gg, t);
 }
-var ub = L({ fft_: d3 });
+var ob = L({ fft_: d3 });
 function p3(e) {
   O(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
   let t = { input: e };
-  return M.runKernel(yg, t);
+  return M.runKernel(bg, t);
 }
-var Id = L({ ifft_: p3 });
+var kd = L({ ifft_: p3 });
 function h3(e) {
   let t = e.shape[e.shape.length - 1], n = e.size / t, s;
   if (t <= 2) {
     let r = G(e, [n, t]);
-    s = Id(r);
+    s = kd(r);
   } else {
-    let r = [n, 2 * (t - 1)], a = G(wd(e), [n, t]), i = G(jg(e), [n, t]), o = Yn(He(a, [0, 1], [n, t - 2]), 1), u = V(Yn(He(i, [0, 1], [n, t - 2]), 1), Ie(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = G(aa(l, c), [r[0], r[1]]);
-    s = Id(p);
+    let r = [n, 2 * (t - 1)], a = G(xd(e), [n, t]), i = G(qg(e), [n, t]), o = Yn(He(a, [0, 1], [n, t - 2]), 1), u = V(Yn(He(i, [0, 1], [n, t - 2]), 1), Ie(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = G(aa(l, c), [r[0], r[1]]);
+    s = kd(p);
   }
-  if (s = wd(s), e.rank === 3 && e.shape[0] !== 0) {
+  if (s = xd(s), e.rank === 3 && e.shape[0] !== 0) {
     let r = s, a = e.shape[0];
     s = G(s, [a, s.shape[0] / a, s.shape[1]]), r.dispose();
   }
@@ -7954,7 +7954,7 @@ function h3(e) {
 var fI = L({ irfft_: h3 });
 function f3(e, t, n = 0) {
   let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
-  return M.runKernel(Lo, r, a);
+  return M.runKernel(Mo, r, a);
 }
 var Ln = L({ split_: f3 });
 function m3(e, t) {
@@ -7968,10 +7968,10 @@ function m3(e, t) {
     f[e.shape.length - 1] = t - n, r = Ft([e, $t(f)], e.shape.length - 1), n = t;
   } else
     r = e;
-  let a = je(r), i = G(aa(r, a), [s, n]), o = ub(i), u = Math.floor(n / 2) + 1, l = wd(o), c = jg(o), p = Ln(l, [u, n - u], l.shape.length - 1), d = Ln(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
+  let a = je(r), i = G(aa(r, a), [s, n]), o = ob(i), u = Math.floor(n / 2) + 1, l = xd(o), c = qg(o), p = Ln(l, [u, n - u], l.shape.length - 1), d = Ln(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
   return h[r.shape.length - 1] = u, G(aa(p[0], d[0]), h);
 }
-var lb = L({ rfft_: m3 });
+var ub = L({ rfft_: m3 });
 function g3(e) {
   let n = { x: _(e, "x", "sqrt", "float32") };
   return M.runKernel(ri, n);
@@ -7990,25 +7990,25 @@ function y3(e, t) {
 }
 var mr = L({ squeeze_: y3 });
 function v3(e, t = 0) {
-  let n = Gu(e, "tensors", "stack", "string_or_numeric");
+  let n = Uu(e, "tensors", "stack", "string_or_numeric");
   O(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && O(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
   let s = n, r = { axis: t };
-  return M.runKernel(_o, s, r);
+  return M.runKernel($o, s, r);
 }
 var Qn = L({ stack_: v3 });
 function x3(e, t = 0) {
   let s = { x: _(e, "x", "step") }, r = { alpha: t };
   return M.runKernel(di, s, r);
 }
-var yp = L({ step_: x3 });
+var bp = L({ step_: x3 });
 function w3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
   let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
-  return M.runKernel(Bo, c, p);
+  return M.runKernel(Lo, c, p);
 }
 var k3 = L({ stridedSlice_: w3 });
 function I3(e) {
   let n = { x: _(e, "x", "tan", "float32") };
-  return M.runKernel(Vo, n);
+  return M.runKernel(Bo, n);
 }
 var S3 = L({ tan_: I3 });
 function Qt(e, t) {
@@ -8018,7 +8018,7 @@ function Qt(e, t) {
     throw new Error("tensor1d() requires values to be a flat/TypedArray");
   return Cr(e, null, n, t);
 }
-function Ki(e, t, n) {
+function ji(e, t, n) {
   if (va(e), t != null && t.length !== 2)
     throw new Error("tensor2d() requires shape to have two numbers");
   let s = Rs(e, n);
@@ -8067,38 +8067,38 @@ function C3(e, t = 1, n = true) {
     throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);
   if (t > r)
     throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);
-  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Wo, a, i);
+  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Vo, a, i);
   return { values: o, indices: u };
 }
 var N3 = L({ topk_: C3 });
 function T3(e, t = 0, n = 1, s, r) {
   if (s != null && s === "bool")
     throw new Error("Unsupported data type $ { dtype }");
-  let a = new rb(t, n, s, true, r), i = De(e, s);
+  let a = new sb(t, n, s, true, r), i = De(e, s);
   for (let o = 0; o < i.values.length; o++)
     i.values[o] = a.nextValue();
   return i.toTensor();
 }
-var cb = L({ truncatedNormal_: T3 });
+var lb = L({ truncatedNormal_: T3 });
 function $3(e, t = 0) {
   let n = _(e, "x", "unique", "string_or_numeric");
   O(n.rank > 0, () => "The input tensor must be at least 1D");
-  let s = { x: n }, r = { axis: t }, [a, i] = M.runKernel(_g, s, r);
+  let s = { x: n }, r = { axis: t }, [a, i] = M.runKernel($g, s, r);
   return { values: a, indices: i };
 }
-var dx = L({ unique_: $3 });
+var cx = L({ unique_: $3 });
 function _3(e, t, n) {
   let s = _(e, "x", "unsortedSegmentSum"), r = _(t, "segmentIds", "unsortedSegmentSum", "int32");
-  O(Yi(n), () => "numSegments must be of dtype int");
+  O(Xi(n), () => "numSegments must be of dtype int");
   let a = { x: s, segmentIds: r }, i = { numSegments: n };
-  return M.runKernel(up, a, i);
+  return M.runKernel(op, a, i);
 }
 var A3 = L({ unsortedSegmentSum_: _3 });
 function E3(e, t = 0) {
   let n = _(e, "x", "unstack", "string_or_numeric");
   O(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
   let s = { value: n }, r = { axis: t };
-  return M.runKernel(Go, s, r);
+  return M.runKernel(Uo, s, r);
 }
 var Fs = L({ unstack_: E3 });
 function R3(e, t = true, n, s) {
@@ -8126,7 +8126,7 @@ async function F3(e, t, n) {
   let u = 1;
   for (let m = a; m < a + i; m++)
     u *= o[m];
-  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = G(s, l), p = G(r, [-1]), d = await bI(p), h = mr(d, [1]), f = Ku(c, h, a);
+  let l = o.slice(0, a).concat([u], o.slice(a + i)), c = G(s, l), p = G(r, [-1]), d = await bI(p), h = mr(d, [1]), f = ju(c, h, a);
   return e !== s && s.dispose(), t !== r && r.dispose(), h.dispose(), c.dispose(), p.dispose(), d.dispose(), f;
 }
 var rpe = F3;
@@ -8150,7 +8150,7 @@ function yI(e, t, n = null) {
     if (t === 1 / 0)
       return As(Mt(e), n);
     if (t === -1 / 0)
-      return hm(Mt(e), n);
+      return pm(Mt(e), n);
     if (t === "euclidean" || t === 2)
       return ln(ye(ca(Mt(e), Ie(2, "int32")), n));
     throw new Error(`Error in norm: invalid ord value: ${t}`);
@@ -8161,7 +8161,7 @@ function yI(e, t, n = null) {
     if (t === 1 / 0)
       return As(ye(Mt(e), n[1]), n[0]);
     if (t === -1 / 0)
-      return hm(ye(Mt(e), n[1]), n[0]);
+      return pm(ye(Mt(e), n[1]), n[0]);
     if (t === "fro" || t === "euclidean")
       return ln(ye(ct(e), n));
     throw new Error(`Error in norm: invalid ord value: ${t}`);
@@ -8183,9 +8183,9 @@ function P3(e, t, n, s, r = true) {
 var ape = L({ movingAverage_: P3 });
 function z3(e, t, n) {
   let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
-  Lg(r, s, n);
+  Mg(r, s, n);
   let a = { indices: s, updates: r }, i = { shape: n };
-  return M.runKernel(Fo, a, i);
+  return M.runKernel(Do, a, i);
 }
 var M3 = L({ scatterND_: z3 });
 function L3(e, t, n, s) {
@@ -8206,12 +8206,12 @@ function B3(e, t, n, s = 0) {
   let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense"), i = _(s, "defaultValue", "sparseToDense", a.dtype);
   L3(r, a, n, i);
   let o = { sparseIndices: r, sparseValues: a, defaultValue: i }, u = { outputShape: n };
-  return M.runKernel(ip, o, u);
+  return M.runKernel(ap, o, u);
 }
 var xI = L({ sparseToDense_: B3 });
 function V3(e, t) {
   let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
-  return M.runKernel(yo, r);
+  return M.runKernel(bo, r);
 }
 var W3 = L({ gatherND_: V3 });
 function U3(e, t) {
@@ -8231,7 +8231,7 @@ function G3(e, t, n, s) {
   let r = _(e, "x", "dropout");
   if (O(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), O(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
     return e instanceof et ? r.clone() : r;
-  let a = U3(r, n), i = 1 - t, o = xe(mp(ie(zl(a, 0, 1, "float32", s), i)), i);
+  let a = U3(r, n), i = 1 - t, o = xe(fp(ie(Pl(a, 0, 1, "float32", s), i)), i);
   return V(r, o);
 }
 var H3 = L({ dropout_: G3 });
@@ -8276,55 +8276,55 @@ function K3(e, t, n, s, r, a = "NHWC", i) {
   let l = a === "NHWC" ? o.shape[3] : o.shape[1], c = a === "NHWC" ? u.shape[3] : u.shape[1];
   O(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), O(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), pn("conv2dDerFilter", r, i);
   let p = { x: o, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, filterShape: n };
-  return M.runKernel(lg, p, d);
+  return M.runKernel(ug, p, d);
 }
-var db = L({ conv2DBackpropFilter_: K3 });
-function vp(e, t, n) {
+var cb = L({ conv2DBackpropFilter_: K3 });
+function yp(e, t, n) {
   if (n == null || n === "linear")
     return e;
   if (n === "relu")
-    return V(e, yp(t));
+    return V(e, bp(t));
   throw new Error(`Cannot compute gradient for fused activation ${n}.`);
 }
-function xp(e, t) {
+function vp(e, t) {
   let n = t, s = _t(e.shape, t.shape);
   return s.length > 0 && (n = ye(n, s)), G(n, e.shape);
 }
-function wp(e, t, n, s) {
+function xp(e, t, n, s) {
   if (t === "linear")
     return e;
   if (t === "relu")
     return Xs(e);
   if (t === "elu")
-    return fp(e);
+    return hp(e);
   if (t === "relu6")
     return oI(e);
   if (t === "prelu")
-    return nb(e, n);
+    return tb(e, n);
   if (t === "leakyrelu")
-    return Kg(e, s);
+    return jg(e, s);
   if (t === "sigmoid")
     return qs(e);
   throw new Error(`Unknown fused activation ${t}.`);
 }
-var kp = (e, t) => !(e > 0) || t === "linear";
+var wp = (e, t) => !(e > 0) || t === "linear";
 function X3({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
-  if (u = u || "linear", kp(M.state.gradientDepth, u) === false) {
+  if (u = u || "linear", wp(M.state.gradientDepth, u) === false) {
     let k = ua(e, t, n, s, r, a, i);
-    return o != null && (k = ie(k, o)), wp(k, u, l, c);
+    return o != null && (k = ie(k, o)), xp(k, u, l, c);
   }
   let p = _(e, "x", "conv2d", "float32"), d = _(t, "filter", "conv2d", "float32"), h = p, f = false;
   p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), pn("fused conv2d", s, i), O(h.shape[3] === d.shape[2], () => `Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NHWC", () => `Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);
-  let m = Fl(h.shape, d.shape, n, a, s, i), g;
+  let m = Dl(h.shape, d.shape, n, a, s, i), g;
   o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), it(m.outShape, g.shape));
   let b;
   l != null && (b = _(l, "prelu weights", "fused conv2d"));
   let y = (k, C) => {
-    let [T, E, A, P] = C, R = vp(k, A, u);
+    let [T, E, A, P] = C, R = yp(k, A, u);
     O(fr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
-    let F = Hg(E.shape, R, T, n, s), $ = db(E, R, T.shape, n, s), z = [F, $];
+    let F = Gg(E.shape, R, T, n, s), $ = cb(E, R, T.shape, n, s), z = [F, $];
     if (P != null) {
-      let W = xp(P, R);
+      let W = vp(P, R);
       z.push(W);
     }
     return z;
@@ -8344,32 +8344,32 @@ function Q3(e, t, n, s, r, a = [1, 1], i) {
   let u = t;
   u.rank === 3 && (u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
   let l = { x: o, dy: u }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, filterShape: n };
-  return M.runKernel(hg, l, c);
+  return M.runKernel(pg, l, c);
 }
 var kI = L({ depthwiseConv2dNativeBackpropFilter_: Q3 });
 function Z3(e, t, n, s, r, a = [1, 1], i) {
   let o = t, u = false;
   t.rank === 3 && (u = true, o = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
-  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = M.runKernel(fg, l, c);
+  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = M.runKernel(hg, l, c);
   return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
 }
 var II = L({ depthwiseConv2dNativeBackpropInput_: Z3 });
 function J3({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilations: a = [1, 1], dimRoundingMode: i, bias: o, activation: u = "linear", preluActivationWeights: l, leakyreluAlpha: c }) {
-  if (kp(M.state.gradientDepth, u) === false) {
-    let k = hp(e, t, n, s, r, a, i);
-    return o != null && (k = ie(k, o)), wp(k, u, l, c);
+  if (wp(M.state.gradientDepth, u) === false) {
+    let k = pp(e, t, n, s, r, a, i);
+    return o != null && (k = ie(k, o)), xp(k, u, l, c);
   }
   let p = _(e, "x", "depthwiseConv2d", "float32"), d = _(t, "filter", "depthwiseConv2d", "float32"), h = p, f = false;
   p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), pn("fused depthwiseConv2d", s, i);
-  let m = Fl(h.shape, d.shape, n, a, s, i, true), g;
+  let m = Dl(h.shape, d.shape, n, a, s, i, true), g;
   o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), it(m.outShape, g.shape));
   let b;
   l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
   let y = (k, C) => {
     O(fr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
-    let [T, E, A, P] = C, R = vp(k, A, u), F = II(E.shape, R, T, n, s, a, i), $ = kI(E, R, T.shape, n, s, a, i);
+    let [T, E, A, P] = C, R = yp(k, A, u), F = II(E.shape, R, T, n, s, a, i), $ = kI(E, R, T.shape, n, s, a, i);
     if (P != null) {
-      let z = xp(g, R);
+      let z = vp(g, R);
       return [F, $, z];
     }
     return [F, $];
@@ -8384,9 +8384,9 @@ function J3({ x: e, filter: t, strides: n, pad: s, dataFormat: r = "NHWC", dilat
 }
 var eF = L({ fusedDepthwiseConv2d_: J3 });
 function tF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o }) {
-  if (kp(M.state.gradientDepth, a) === false) {
+  if (wp(M.state.gradientDepth, a) === false) {
     let R = We(e, t, n, s);
-    return r != null && (R = ie(R, r)), wp(R, a, i, o);
+    return r != null && (R = ie(R, r)), xp(R, a, i, o);
   }
   let u = _(e, "a", "fused matMul"), l = _(t, "b", "fused matMul");
   [u, l] = vt(u, l);
@@ -8397,9 +8397,9 @@ function tF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r,
   let T;
   i != null && (T = _(i, "prelu weights", "fused matMul"));
   let E = (R, F) => {
-    let [$, z, W, q] = F, K = vp(G(R, W.shape), W, a), Y, Z;
+    let [$, z, W, q] = F, K = yp(G(R, W.shape), W, a), Y, Z;
     if (!n && !s ? (Y = We(K, z, false, true), Z = We($, K, true, false)) : !n && s ? (Y = We(K, z, false, false), Z = We(K, $, true, false)) : n && !s ? (Y = We(z, K, false, true), Z = We($, K, false, false)) : (Y = We(z, K, true, true), Z = We(K, $, true, true)), r != null) {
-      let te = xp(q, K);
+      let te = vp(q, K);
       return [Y, Z, te];
     } else
       return [Y, Z];
@@ -8427,30 +8427,30 @@ function iF(e, t, n, s = false, r = 0) {
     i.push(He(e, a, t)), a += n;
   if (s)
     for (; a < e.size; ) {
-      let o = a + t - e.size, u = Ft([He(e, a, t - o), Ol([o], r)]);
+      let o = a + t - e.size, u = Ft([He(e, a, t - o), Fl([o], r)]);
       i.push(u), a += n;
     }
-  return i.length === 0 ? Ki([], [0, t]) : G(Ft(i), [i.length, t]);
+  return i.length === 0 ? ji([], [0, t]) : G(Ft(i), [i.length, t]);
 }
 var CI = L({ frame_: iF });
 function oF(e, t, n, s, r = SI) {
   s == null && (s = q3(t));
   let a = CI(e, t, n), i = V(a, r(t));
-  return lb(i, s);
+  return ub(i, s);
 }
 var uF = L({ stft_: oF });
 function lF(e, t, n, s, r = "bilinear", a = 0) {
   let i = _(e, "image", "cropAndResize"), o = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = o.shape[0];
   O(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), O(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), O(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), O(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), O(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
   let c = { image: i, boxes: o, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
-  return M.runKernel(co, c, p);
+  return M.runKernel(lo, c, p);
 }
 var cF = L({ cropAndResize_: lF });
 function dF(e) {
   let t = _(e, "image", "flipLeftRight", "float32");
   O(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
   let n = { image: t };
-  return M.runKernel(go, n, {});
+  return M.runKernel(mo, n, {});
 }
 var pF = L({ flipLeftRight_: dF });
 function hF(e) {
@@ -8464,19 +8464,19 @@ function mF(e, t, n = 0, s = 0.5) {
   let r = _(e, "image", "rotateWithOffset", "float32");
   O(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
   let a = { image: r }, i = { radians: t, fillValue: n, center: s };
-  return M.runKernel(qo, a, i);
+  return M.runKernel(Ho, a, i);
 }
 var gF = L({ rotateWithOffset_: mF });
-function Yo(e, t, n, s, r, a) {
+function Xo(e, t, n, s, r, a) {
   s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
   let i = e.shape[0];
   return n = Math.min(n, i), O(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), O(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), O(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), O(t.rank === 1, () => "scores must be a 1D tensor"), O(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), O(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
 }
 function bF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Yo(a, i, n, s, r);
+  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Xo(a, i, n, s, r);
   n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
   let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
-  return M.runKernel(Co, { boxes: a, scores: i }, u);
+  return M.runKernel(So, { boxes: a, scores: i }, u);
 }
 var yF = L({ nonMaxSuppression_: bF });
 function vF(e, t, n) {
@@ -8499,19 +8499,19 @@ function kF(e, t, n) {
   return i ? s : -s - 1;
 }
 function NI(e, t, n, s, r) {
-  return pb(e, t, n, s, r, 0);
+  return db(e, t, n, s, r, 0);
 }
 function TI(e, t, n, s, r, a) {
-  return pb(e, t, n, s, r, 0, false, a, true);
+  return db(e, t, n, s, r, 0, false, a, true);
 }
 function $I(e, t, n, s, r, a) {
-  return pb(e, t, n, s, r, a, true);
+  return db(e, t, n, s, r, a, true);
 }
-function pb(e, t, n, s, r, a, i = false, o = false, u = false) {
+function db(e, t, n, s, r, a, i = false, o = false, u = false) {
   let l = [];
   for (let g = 0; g < t.length; g++)
     t[g] > r && l.push({ score: t[g], boxIndex: g, suppressBeginIndex: 0 });
-  l.sort(px);
+  l.sort(dx);
   let c = a > 0 ? -0.5 / a : 0, p = [], d = [];
   for (; p.length < n && l.length > 0; ) {
     let g = l.pop(), { score: b, boxIndex: y, suppressBeginIndex: v } = g;
@@ -8527,7 +8527,7 @@ function pb(e, t, n, s, r, a, i = false, o = false, u = false) {
       if (g.score = g.score * SF(s, c, C), g.score <= r)
         break;
     }
-    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && vF(l, g, px));
+    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && vF(l, g, dx));
   }
   let h = p.length, f = n - h;
   o && f > 0 && (p.push(...new Array(f).fill(0)), d.push(...new Array(f).fill(0)));
@@ -8545,37 +8545,37 @@ function SF(e, t, n) {
   let s = Math.exp(t * n * n);
   return n <= e ? s : 0;
 }
-function px(e, t) {
+function dx(e, t) {
   return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
 }
 async function CF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
-  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Yo(a, i, n, s, r);
+  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Xo(a, i, n, s, r);
   n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
   let u = await Promise.all([a.data(), i.data()]), l = u[0], c = u[1], { selectedIndices: p } = NI(l, c, n, s, r);
   return a !== e && a.dispose(), i !== t && i.dispose(), Qt(p, "int32");
 }
 var NF = CF;
 function TF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Yo(i, o, n, s, r, a);
+  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Xo(i, o, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
-  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(No, l, c);
+  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(Co, l, c);
   return { selectedIndices: p[0], selectedScores: p[1] };
 }
 var $F = L({ nonMaxSuppressionWithScore_: TF });
 async function _F(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Yo(i, o, n, s, r, a);
+  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Xo(i, o, n, s, r, a);
   n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
   let l = await Promise.all([i.data(), o.data()]), c = l[0], p = l[1], { selectedIndices: d, selectedScores: h } = $I(c, p, n, s, r, a);
   return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Qt(d, "int32"), selectedScores: Qt(h) };
 }
 var AF = _F;
 function EF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Yo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(wl, d, h);
+  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Xo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(xl, d, h);
   return { selectedIndices: f[0], validOutputs: f[1] };
 }
 var RF = L({ nonMaxSuppressionPadded_: EF });
 async function DF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
-  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Yo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = TI(d, h, l, c, p, a);
+  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Xo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, [d, h] = await Promise.all([i.data(), o.data()]), { selectedIndices: f, validOutputs: m } = TI(d, h, l, c, p, a);
   return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Qt(f, "int32"), validOutputs: Ie(m, "int32") };
 }
 var FF = DF;
@@ -8593,7 +8593,7 @@ function zF(e, t, n = false, s = false) {
   O(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), O(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), O(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
   let a = r, i = false;
   r.rank === 3 && (i = true, a = G(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
-  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Sl, o, u);
+  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Il, o, u);
   return i ? G(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
 }
 var MF = L({ resizeNearestNeighbor_: zF });
@@ -8609,16 +8609,16 @@ function LF(e, t = "binary", n = false, s = 0.5) {
     let g = Hk(ce(uI(h), "int32"), hs([]), 256);
     l = BF(g, u);
   }
-  let f = n ? Xo(h, l) : Wn(h, l);
+  let f = n ? Ko(h, l) : Wn(h, l);
   return ce(V(f, 255), "int32");
 }
 function BF(e, t) {
   let n = Qt([-1]), s = Qt([0]), r = Qt([0]), a, i, o, u, l, c;
   for (let p = 0; p < e.size - 1; p++) {
     a = He(e, 0, p + 1), i = He(e, p + 1), l = xe(ye(a), t), c = xe(ye(i), t);
-    let d = ye(V(a, Yu(0, a.size)));
+    let d = ye(V(a, Xu(0, a.size)));
     o = xe(d, ye(a));
-    let h = Ol(i.shape, a.size), f = ie(Yu(0, i.size), h), m = V(i, f);
+    let h = Fl(i.shape, a.size), f = ie(Xu(0, i.size), h), m = V(i, f);
     u = xe(ye(m), ye(i));
     let g = ge(o, u), b = ge(o, u), y = V(l, c);
     r = V(V(y, g), b);
@@ -8632,7 +8632,7 @@ function WF(e, t, n = "nearest", s = "constant", r = 0, a) {
   let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
   O(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), O(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
   let u = { image: i, transforms: o }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
-  return M.runKernel(Uo, u, l);
+  return M.runKernel(Wo, u, l);
 }
 var UF = L({ transform_: WF });
 function GF(e, t, n) {
@@ -8645,7 +8645,7 @@ function GF(e, t, n) {
   if (!(n <= i))
     throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);
   t < 0 && (t = a), n < 0 && (n = i);
-  let o = G(Yu(0, a, 1, "int32"), [-1, 1]), u = Yu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Xo(l, Ie(+t, "int32")), Ko(l, Ie(-n, "int32"))), p = $t([a, i], s.dtype);
+  let o = G(Xu(0, a, 1, "int32"), [-1, 1]), u = Xu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Ko(l, Ie(+t, "int32")), jo(l, Ie(-n, "int32"))), p = $t([a, i], s.dtype);
   return G(Qn(Fs(G(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
 }
 var HF = L({ bandPart_: GF });
@@ -8675,25 +8675,25 @@ function qF(e) {
 var jF = L({ gramSchmidt_: qF });
 function KF(e, t = false) {
   if (O(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
-    return hx(e, t);
+    return px(e, t);
   {
     let n = e.shape.slice(0, e.shape.length - 2).reduce((u, l) => u * l), s = Fs(G(e, [n, e.shape[e.shape.length - 2], e.shape[e.shape.length - 1]]), 0), r = [], a = [];
     s.forEach((u) => {
-      let [l, c] = hx(u, t);
+      let [l, c] = px(u, t);
       r.push(l), a.push(c);
     });
     let i = G(Qn(r, 0), e.shape), o = G(Qn(a, 0), e.shape);
     return [i, o];
   }
 }
-function hx(e, t = false) {
+function px(e, t = false) {
   return M.tidy(() => {
     O(e.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);
-    let n = e.shape[0], s = e.shape[1], r = Zk(n), a = lr(e), i = Ki([[1]], [1, 1]), o = lr(i), u = n >= s ? s : n;
+    let n = e.shape[0], s = e.shape[1], r = Zk(n), a = lr(e), i = ji([[1]], [1, 1]), o = lr(i), u = n >= s ? s : n;
     for (let l = 0; l < u; ++l) {
       let c = a, p = o, d = r;
       [o, a, r] = M.tidy(() => {
-        let h = He(a, [l, l], [n - l, 1]), f = vI(h), m = He(a, [l, l], [1, 1]), g = vn(Wn(m, 0), Ki([[-1]]), Ki([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
+        let h = He(a, [l, l], [n - l, 1]), f = vI(h), m = He(a, [l, l], [1, 1]), g = vn(Wn(m, 0), ji([[-1]]), ji([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
         y.shape[0] === 1 ? o = lr(i) : o = Ft([i, He(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
         let v = kt(xe(We(g, b), f)), x = He(a, [l, 0], [n - l, s]), k = V(v, o), C = qe(o);
         if (l === 0)
@@ -8737,7 +8737,7 @@ function QF(e, t, n = 3) {
     if (r == null)
       return xe(ye(a), Ie(s.size));
     {
-      let i = V(r, zn(s.shape)), o = ce(ye(Xu(i, Ie(0))), "float32");
+      let i = V(r, zn(s.shape)), o = ce(ye(Ku(i, Ie(0))), "float32");
       return xe(ye(a), o);
     }
   }
@@ -8770,7 +8770,7 @@ var sO = L({ hingeLoss_: nO });
 function rO(e, t, n, s = 1, r = 3) {
   let a = _(e, "labels", "huberLoss"), i = _(t, "predictions", "huberLoss"), o = null;
   n != null && (o = _(n, "weights", "huberLoss")), dn(a.shape, i.shape, "Error in huberLoss: ");
-  let u = Ie(s), l = Mt(ge(i, a)), c = bp(l, u), p = ge(l, c), d = ie(V(Ie(0.5), ct(c)), V(u, p));
+  let u = Ie(s), l = Mt(ge(i, a)), c = gp(l, u), p = ge(l, c), d = ie(V(Ie(0.5), ct(c)), V(u, p));
   return Ys(d, o, r);
 }
 var aO = L({ huberLoss_: rO });
@@ -8791,7 +8791,7 @@ var lO = L({ meanSquaredError_: uO });
 function cO(e, t) {
   let n = _(e, "labels", "sigmoidCrossEntropyWithLogits"), s = _(t, "logits", "sigmoidCrossEntropyWithLogits");
   dn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
-  let r = Xs(s), a = V(s, n), i = Xg(jn(kt(Mt(s))));
+  let r = Xs(s), a = V(s, n), i = Kg(jn(kt(Mt(s))));
   return ie(ge(r, a), i);
 }
 function dO(e, t, n, s = 0, r = 3) {
@@ -8838,7 +8838,7 @@ function gO(e, t, n, s) {
     throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);
   if (o.rank !== 0)
     throw new Error(`Default value should be a scalar but received shape ${o.shape}`);
-  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = M.runKernel(sp, u);
+  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = M.runKernel(np, u);
   return { outputIndices: l[0], outputValues: l[1], emptyRowIndicator: l[2], reverseIndexMap: l[3] };
 }
 var bO = L({ sparseFillEmptyRows_: gO });
@@ -8851,7 +8851,7 @@ function yO(e, t, n) {
     throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);
   if (a.rank !== 1)
     throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);
-  let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel($l, i);
+  let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel(Tl, i);
   return { outputIndices: o[0], outputShape: o[1] };
 }
 var vO = L({ sparseReshape_: yO });
@@ -8866,7 +8866,7 @@ function xO(e, t, n) {
     throw new Error(`Segment ids should be Tensor1D but received shape
           ${a.shape}`);
   let i = { data: s, indices: r, segmentIds: a };
-  return M.runKernel(rp, i);
+  return M.runKernel(sp, i);
 }
 var wO = L({ sparseSegmentMean_: xO });
 function kO(e, t, n) {
@@ -8880,7 +8880,7 @@ function kO(e, t, n) {
     throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);
   let i = { data: s, indices: r, segmentIds: a };
-  return M.runKernel(ap, i);
+  return M.runKernel(rp, i);
 }
 var IO = L({ sparseSegmentSum_: kO });
 function SO(e, t, n, s, r, a, i, o) {
@@ -8892,7 +8892,7 @@ function SO(e, t, n, s, r, a, i, o) {
   let l = _(t, "dataSplits", "stringNGrams");
   if (l.dtype !== "int32")
     throw new Error("Data splits must be of datatype int32");
-  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = M.runKernel(op, p, c);
+  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = M.runKernel(ip, p, c);
   return { nGrams: d[0], nGramsSplits: d[1] };
 }
 var CO = L({ stringNGrams_: SO });
@@ -8902,7 +8902,7 @@ function NO(e, t, n = true) {
     throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);
   if (r.rank !== 0)
     throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);
-  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = M.runKernel(Tg, i, a);
+  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = M.runKernel(Ng, i, a);
   return { indices: o[0], values: o[1], shape: o[2] };
 }
 var TO = L({ stringSplit_: NO });
@@ -8911,16 +8911,16 @@ function $O(e, t) {
   if (t <= 0)
     throw new Error("Number of buckets must be at least 1");
   let r = { input: n };
-  return M.runKernel($g, r, s);
+  return M.runKernel(Tg, r, s);
 }
 var _O = L({ stringToHashBucketFast_: $O });
-var ope = { fft: ub, ifft: Id, rfft: lb, irfft: fI };
+var ope = { fft: ob, ifft: kd, rfft: ub, irfft: fI };
 var upe = { hammingWindow: rF, hannWindow: SI, frame: CI, stft: uF };
 var ds = { flipLeftRight: pF, grayscaleToRGB: fF, resizeNearestNeighbor: MF, resizeBilinear: PF, rotateWithOffset: gF, cropAndResize: cF, nonMaxSuppression: yF, nonMaxSuppressionAsync: NF, nonMaxSuppressionWithScore: $F, nonMaxSuppressionWithScoreAsync: AF, nonMaxSuppressionPadded: RF, nonMaxSuppressionPaddedAsync: FF, threshold: VF, transform: UF };
 var AO = { bandPart: HF, gramSchmidt: jF, qr: XF };
 var lpe = { absoluteDifference: JF, computeWeightedLoss: Ys, cosineDistance: tO, hingeLoss: sO, huberLoss: aO, logLoss: oO, meanSquaredError: lO, sigmoidCrossEntropy: pO, softmaxCrossEntropy: mO };
-var Wc = { sparseFillEmptyRows: bO, sparseReshape: vO, sparseSegmentMean: wO, sparseSegmentSum: IO };
-var zf = { stringNGrams: CO, stringSplit: TO, stringToHashBucketFast: _O };
+var Vc = { sparseFillEmptyRows: bO, sparseReshape: vO, sparseSegmentMean: wO, sparseSegmentSum: IO };
+var Pf = { stringNGrams: CO, stringSplit: TO, stringToHashBucketFast: _O };
 var $r = class extends Ok {
   minimize(e, t = false, n) {
     let { value: s, grads: r } = this.computeGradients(e, n);
@@ -8957,7 +8957,7 @@ var $r = class extends Ok {
   }
 };
 Object.defineProperty($r, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
-var hb = class extends $r {
+var pb = class extends $r {
   constructor(e, t, n = null) {
     super();
     this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = M.backend.epsilon());
@@ -8997,9 +8997,9 @@ var hb = class extends $r {
     return new e(t.learningRate, t.rho, t.epsilon);
   }
 };
-hb.className = "Adadelta";
-Nr(hb);
-var fb = class extends $r {
+pb.className = "Adadelta";
+Nr(pb);
+var hb = class extends $r {
   constructor(e, t = 0.1) {
     super();
     this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
@@ -9007,7 +9007,7 @@ var fb = class extends $r {
   applyGradients(e) {
     (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
       let r = M.registeredVariables[n];
-      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: j(() => Ol(r.shape, this.initialAccumulatorValue).variable(false)) });
+      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: j(() => Fl(r.shape, this.initialAccumulatorValue).variable(false)) });
       let a = Array.isArray(e) ? e[s].tensor : e[n];
       if (a == null)
         return;
@@ -9038,9 +9038,9 @@ var fb = class extends $r {
     return new e(t.learningRate, t.initialAccumulatorValue);
   }
 };
-fb.className = "Adagrad";
-Nr(fb);
-var mb = class extends $r {
+hb.className = "Adagrad";
+Nr(hb);
+var fb = class extends $r {
   constructor(e, t, n, s = null) {
     super();
     this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], j(() => {
@@ -9085,9 +9085,9 @@ var mb = class extends $r {
     return new e(t.learningRate, t.beta1, t.beta2, t.epsilon);
   }
 };
-mb.className = "Adam";
-Nr(mb);
-var gb = class extends $r {
+fb.className = "Adam";
+Nr(fb);
+var mb = class extends $r {
   constructor(e, t, n, s = null, r = 0) {
     super();
     this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], j(() => {
@@ -9127,9 +9127,9 @@ var gb = class extends $r {
     return new e(t.learningRate, t.beta1, t.beta2, t.epsilon, t.decay);
   }
 };
-gb.className = "Adamax";
-Nr(gb);
-var Ip = class extends $r {
+mb.className = "Adamax";
+Nr(mb);
+var kp = class extends $r {
   constructor(e) {
     super();
     this.learningRate = e, this.setLearningRate(e);
@@ -9166,9 +9166,9 @@ var Ip = class extends $r {
     return new e(t.learningRate);
   }
 };
-Ip.className = "SGD";
-Nr(Ip);
-var bb = class extends Ip {
+kp.className = "SGD";
+Nr(kp);
+var gb = class extends kp {
   constructor(e, t, n = false) {
     super(e);
     this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = Ie(this.momentum);
@@ -9205,9 +9205,9 @@ var bb = class extends Ip {
     return new e(t.learningRate, t.momentum, t.useNesterov);
   }
 };
-bb.className = "Momentum";
-Nr(bb);
-var yb = class extends $r {
+gb.className = "Momentum";
+Nr(gb);
+var bb = class extends $r {
   constructor(e, t = 0.9, n = 0, s = null, r = false) {
     super();
     if (this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = M.backend.epsilon()), e == null)
@@ -9256,29 +9256,29 @@ var yb = class extends $r {
     return new e(t.learningRate, t.decay, t.momentum, t.epsilon, t.centered);
   }
 };
-yb.className = "RMSProp";
-Nr(yb);
+bb.className = "RMSProp";
+Nr(bb);
 var Vr = class {
   static sgd(e) {
-    return new Ip(e);
+    return new kp(e);
   }
   static momentum(e, t, n = false) {
-    return new bb(e, t, n);
+    return new gb(e, t, n);
   }
   static rmsprop(e, t = 0.9, n = 0, s = null, r = false) {
-    return new yb(e, t, n, s, r);
+    return new bb(e, t, n, s, r);
   }
   static adam(e = 1e-3, t = 0.9, n = 0.999, s = null) {
-    return new mb(e, t, n, s);
+    return new fb(e, t, n, s);
   }
   static adadelta(e = 1e-3, t = 0.95, n = null) {
-    return new hb(e, t, n);
+    return new pb(e, t, n);
   }
   static adamax(e = 2e-3, t = 0.9, n = 0.999, s = null, r = 0) {
-    return new gb(e, t, n, s, r);
+    return new mb(e, t, n, s, r);
   }
   static adagrad(e, t = 0.1) {
-    return new fb(e, t);
+    return new hb(e, t);
   }
 };
 var Fi = { sgd: Vr.sgd, momentum: Vr.momentum, adadelta: Vr.adadelta, adagrad: Vr.adagrad, rmsprop: Vr.rmsprop, adamax: Vr.adamax, adam: Vr.adam };
@@ -9287,7 +9287,7 @@ function RO() {
   return new Promise((e) => EO(() => e()));
 }
 var N = {};
-Ae(N, { ERF_A1: () => UO, ERF_A2: () => GO, ERF_A3: () => HO, ERF_A4: () => qO, ERF_A5: () => jO, ERF_P: () => WO, PARALLELIZE_THRESHOLD: () => vb, SELU_SCALE: () => AI, SELU_SCALEALPHA: () => _I, applyActivation: () => wp, assertAndGetBroadcastShape: () => it, assertAxesAreInnerMostDims: () => qR, assertParamsConsistent: () => DO, assignToTypedArray: () => JO, axesAreInnerMostDims: () => Yg, calculateShapes: () => Ck, checkEinsumDimSizes: () => aP, checkPadOnDimRoundingMode: () => pn, combineLocations: () => tI, complexWithEvenIndex: () => YO, complexWithOddIndex: () => QO, computeConv2DInfo: () => Fl, computeConv3DInfo: () => Wk, computeDefaultPad: () => Wg, computeDilation2DInfo: () => tE, computeOptimalWindowSize: () => OO, computeOutAndReduceShapes: () => nI, computeOutShape: () => FO, computePool2DInfo: () => Vk, computePool3DInfo: () => nE, convertConv2DDataFormat: () => Uk, decodeEinsumEquation: () => sP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => la, exponent: () => tP, exponents: () => eP, fromStringArrayToUint8: () => NP, fromUint8ToStringArray: () => CP, getAxesPermutation: () => sI, getBroadcastDims: () => vk, getComplexWithIndex: () => ZO, getEinsumComputePath: () => iP, getEinsumPermutation: () => rP, getFusedBiasGradient: () => xp, getFusedDyActivation: () => vp, getImageCenter: () => PO, getInnerMostAxes: () => jR, getPermuted: () => MO, getReductionAxes: () => _t, getReshaped: () => zO, getReshapedPermuted: () => LO, getSliceBeginCoords: () => BO, getSliceSize: () => VO, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => cP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => dP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => pP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => mP, getSparseReshapeInputOutputMismatchErrorMessage: () => bP, getSparseReshapeInputOutputMultipleErrorMessage: () => gP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => hP, getSparseReshapeNegativeOutputDimErrorMessage: () => fP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => wP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => yP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => vP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => xP, getUndoAxesPermutation: () => Qg, isIdentityPermutation: () => oP, log: () => I$, mergeRealAndImagArrays: () => KO, prepareAndValidate: () => Ik, prepareSplitSize: () => lP, segment_util: () => EI, shouldFuse: () => kp, slice_util: () => wt, splitRealAndImagArrays: () => XO, tupleValuesAreOne: () => fr, upcastType: () => yn, validateInput: () => Lg, validateUpdateShape: () => Mg, warn: () => ar });
+Ae(N, { ERF_A1: () => UO, ERF_A2: () => GO, ERF_A3: () => HO, ERF_A4: () => qO, ERF_A5: () => jO, ERF_P: () => WO, PARALLELIZE_THRESHOLD: () => yb, SELU_SCALE: () => AI, SELU_SCALEALPHA: () => _I, applyActivation: () => xp, assertAndGetBroadcastShape: () => it, assertAxesAreInnerMostDims: () => qR, assertParamsConsistent: () => DO, assignToTypedArray: () => JO, axesAreInnerMostDims: () => Xg, calculateShapes: () => Ck, checkEinsumDimSizes: () => aP, checkPadOnDimRoundingMode: () => pn, combineLocations: () => tI, complexWithEvenIndex: () => YO, complexWithOddIndex: () => QO, computeConv2DInfo: () => Dl, computeConv3DInfo: () => Wk, computeDefaultPad: () => Vg, computeDilation2DInfo: () => tE, computeOptimalWindowSize: () => OO, computeOutAndReduceShapes: () => nI, computeOutShape: () => FO, computePool2DInfo: () => Vk, computePool3DInfo: () => nE, convertConv2DDataFormat: () => Uk, decodeEinsumEquation: () => sP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => la, exponent: () => tP, exponents: () => eP, fromStringArrayToUint8: () => NP, fromUint8ToStringArray: () => CP, getAxesPermutation: () => sI, getBroadcastDims: () => vk, getComplexWithIndex: () => ZO, getEinsumComputePath: () => iP, getEinsumPermutation: () => rP, getFusedBiasGradient: () => vp, getFusedDyActivation: () => yp, getImageCenter: () => PO, getInnerMostAxes: () => jR, getPermuted: () => MO, getReductionAxes: () => _t, getReshaped: () => zO, getReshapedPermuted: () => LO, getSliceBeginCoords: () => BO, getSliceSize: () => VO, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => cP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => dP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => pP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => mP, getSparseReshapeInputOutputMismatchErrorMessage: () => bP, getSparseReshapeInputOutputMultipleErrorMessage: () => gP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => hP, getSparseReshapeNegativeOutputDimErrorMessage: () => fP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => wP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => yP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => vP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => xP, getUndoAxesPermutation: () => Yg, isIdentityPermutation: () => oP, log: () => I$, mergeRealAndImagArrays: () => KO, prepareAndValidate: () => Ik, prepareSplitSize: () => lP, segment_util: () => EI, shouldFuse: () => wp, slice_util: () => wt, splitRealAndImagArrays: () => XO, tupleValuesAreOne: () => fr, upcastType: () => yn, validateInput: () => Mg, validateUpdateShape: () => zg, warn: () => ar });
 function DO(e, t) {
   let n = e[0].length;
   e.forEach((r, a) => {
@@ -9305,9 +9305,9 @@ function FO(e, t) {
     n[t] += e[s][t];
   return n;
 }
-var vb = 30;
+var yb = 30;
 function OO(e) {
-  return e <= vb ? e : hd(e, Math.floor(Math.sqrt(e)));
+  return e <= yb ? e : pd(e, Math.floor(Math.sqrt(e)));
 }
 function PO(e, t, n) {
   let s = n * (typeof e == "number" ? e : e[0]), r = t * (typeof e == "number" ? e : e[1]);
@@ -9412,20 +9412,20 @@ function tP(e, t, n) {
   let s = (n ? 2 : -2) * Math.PI * (e / t), r = Math.cos(s), a = Math.sin(s);
   return { real: r, imag: a };
 }
-var Mf = "->";
+var zf = "->";
 var nP = /->/g;
-var fx = ",";
-var mx = "...";
+var hx = ",";
+var fx = "...";
 function sP(e, t) {
   e = e.replace(/\s/g, "");
-  let n = (e.length - e.replace(nP, "").length) / Mf.length;
+  let n = (e.length - e.replace(nP, "").length) / zf.length;
   if (n < 1)
     throw new Error("Equations without an arrow are not supported.");
   if (n > 1)
-    throw new Error(`Equation must contain exactly one arrow ("${Mf}").`);
-  let [s, r] = e.split(Mf);
-  O(s.indexOf(mx) === -1, () => `The ellipsis notation ("${mx}") is not supported yet.`);
-  let a = s.split(fx), i = a.length;
+    throw new Error(`Equation must contain exactly one arrow ("${zf}").`);
+  let [s, r] = e.split(zf);
+  O(s.indexOf(fx) === -1, () => `The ellipsis notation ("${fx}") is not supported yet.`);
+  let a = s.split(hx), i = a.length;
   if (t !== i)
     throw new Error(`Expected ${i} input tensors, received ${t}`);
   if (i > 2)
@@ -9439,7 +9439,7 @@ function sP(e, t) {
   }
   for (let d = 0; d < s.length; ++d) {
     let h = s[d];
-    o.indexOf(h) === -1 && h !== fx && o.push(h);
+    o.indexOf(h) === -1 && h !== hx && o.push(h);
   }
   let u = new Array(a.length);
   for (let d = 0; d < i; ++d) {
@@ -9554,8 +9554,8 @@ var EI = {};
 Ae(EI, { collectGatherOpShapeInfo: () => SP, computeOutShape: () => IP, segOpComputeOptimalWindowSize: () => kP });
 function kP(e, t) {
   let n = false, s;
-  for (e <= vb ? (s = e, n = true) : s = hd(e, Math.floor(Math.sqrt(e))); !n; )
-    s > t || s === e ? n = true : s = hd(e, s + 1);
+  for (e <= yb ? (s = e, n = true) : s = pd(e, Math.floor(Math.sqrt(e))); !n; )
+    s > t || s === e ? n = true : s = pd(e, s + 1);
   return s;
 }
 function IP(e, t, n) {
@@ -9589,28 +9589,28 @@ function SP(e, t, n, s) {
 }
 function CP(e) {
   try {
-    return e.map((t) => md(t));
+    return e.map((t) => fd(t));
   } catch (t) {
     throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`);
   }
 }
 function NP(e) {
-  return e.map((t) => Rl(t));
+  return e.map((t) => El(t));
 }
 var xs = {};
 Ae(xs, { nonMaxSuppressionV3Impl: () => NI, nonMaxSuppressionV4Impl: () => TI, nonMaxSuppressionV5Impl: () => $I, whereImpl: () => gI });
-var RI = { kernelName: io, inputsToSave: ["x"], gradFunc: (e, t) => {
+var RI = { kernelName: ao, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(e, yp(ce(n, "float32"), -1)) };
+  return { x: () => V(e, bp(ce(n, "float32"), -1)) };
 } };
-var TP = { kernelName: sl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var TP = { kernelName: nl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = ct(ce(n, "float32")), r = ln(ge(Ie(1), s));
     return kt(xe(e, r));
   } };
 } };
-var $P = { kernelName: rl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var $P = { kernelName: sl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = ln(ge(ct(ce(n, "float32")), 1));
@@ -9637,22 +9637,22 @@ var EP = { kernelName: wa, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => je(n) };
 } };
-var RP = { kernelName: ol, inputsToSave: ["x"], gradFunc: (e, t) => {
+var RP = { kernelName: il, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => je(n) };
 } };
-var DP = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
+var DP = { kernelName: ol, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ln(ge(Ie(1), ct(ce(n, "float32"))))) };
 } };
-var FP = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
+var FP = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = ln(ie(Ie(1), ct(ce(n, "float32"))));
     return xe(e, s);
   } };
 } };
-var OP = { kernelName: pl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var OP = { kernelName: dl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t, r = it(n.shape, s.shape);
   return { a: () => {
     let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
@@ -9662,22 +9662,22 @@ var OP = { kernelName: pl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
     return l.length > 0 && (u = ye(u, l)), G(u, s.shape);
   } };
 } };
-var PP = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var PP = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ie(ct(ce(n, "float32")), 1)) };
 } };
-var zP = { kernelName: dl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var zP = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ge(Ie(1), ct(ce(n, "float32")))) };
 } };
 function MP(e, t, n, s, r, a) {
   let i = _(e, "dy", "avgPool3dGrad"), o = _(t, "input", "avgPool3dGrad"), u = i, l = o, c = false;
   o.rank === 4 && (c = true, u = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), l = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), O(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), pn("avgPool3dGrad", r, a);
-  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = M.runKernel(ig, p, d);
+  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = M.runKernel(ag, p, d);
   return c ? G(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
 }
 var LP = L({ avgPool3dGrad_: MP });
-var BP = { kernelName: qd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var BP = { kernelName: Hd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
   return { x: () => LP(e, s, r, a, i, o) };
 } };
@@ -9686,7 +9686,7 @@ function VP(e, t, n, s, r) {
   O(i.rank === a.rank, () => `Rank of input (${i.rank}) does not match rank of dy (${a.rank})`);
   let o = i, u = a, l = false;
   i.rank === 3 && (l = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), u = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), O(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
-  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = M.runKernel(ag, c, p);
+  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = M.runKernel(rg, c, p);
   return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
 }
 var WP = L({ avgPoolGrad_: VP });
@@ -9698,9 +9698,9 @@ var GP = { kernelName: Ia, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
   let [s, r] = t, { transposeA: a, transposeB: i } = n;
   return !a && !i ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && i ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !i ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
 } };
-var HP = { kernelName: oo, gradFunc: (e, t, n) => {
+var HP = { kernelName: io, gradFunc: (e, t, n) => {
   let { blockShape: s, crops: r } = n;
-  return { x: () => tb(e, s, r) };
+  return { x: () => eb(e, s, r) };
 } };
 var qP = { kernelName: w$, gradFunc: (e, t, n) => {
   let s = n, r = s.inputShape, a = s.shape, i = Array.from(a);
@@ -9718,20 +9718,20 @@ var jP = { kernelName: Sa, gradFunc: (e) => ({ x: () => e.clone() }) };
 var KP = { kernelName: Ca, gradFunc: (e) => ({ x: () => je(e) }) };
 var XP = { kernelName: Ir, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
-  return { x: () => vn(Ds(Ko(s, r), Xo(s, a)), e, je(e)) };
+  return { x: () => vn(Ds(jo(s, r), Ko(s, a)), e, je(e)) };
 } };
-var YP = { kernelName: Kd, inputsToSave: ["x"], gradFunc: RI.gradFunc };
-var QP = { kernelName: uo, saveAllInputs: true, gradFunc: (e, t, n) => {
+var YP = { kernelName: jd, inputsToSave: ["x"], gradFunc: RI.gradFunc };
+var QP = { kernelName: oo, saveAllInputs: true, gradFunc: (e, t, n) => {
   let s = t.map((u) => u.shape), { axis: r } = n, a = Jn(r, t[0].shape)[0], i = s.map((u) => u[a]);
   return Ln(e, i, a).map((u) => () => u);
 } };
 var ZP = { kernelName: Na, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
   let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
-  return O(fr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => Hg(s.shape, e, r, i, o, u), filter: () => db(s, e, r.shape, i, o, u) };
+  return O(fr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => Gg(s.shape, e, r, i, o, u), filter: () => cb(s, e, r.shape, i, o, u) };
 } };
 var JP = { kernelName: Ta, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
   let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
-  return { dy: () => ua(e, r, a, i, o, 1, u), filter: () => db(e, s, r.shape, a, i, o, u) };
+  return { dy: () => ua(e, r, a, i, o, 1, u), filter: () => cb(e, s, r.shape, a, i, o, u) };
 } };
 function ez(e, t, n, s, r) {
   let a = e;
@@ -9739,10 +9739,10 @@ function ez(e, t, n, s, r) {
   let i = t;
   i.rank === 4 && (i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), O(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), O(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), O(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), O(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), O(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
   let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
-  return M.runKernel(cg, o, u);
+  return M.runKernel(lg, o, u);
 }
 var tz = L({ conv3DBackpropFilter_: ez });
-var nz = { kernelName: Xd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+var nz = { kernelName: Kd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
   let { dilations: s, strides: r, pad: a } = n;
   O(fr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
   let [i, o] = t;
@@ -9756,7 +9756,7 @@ var rz = { kernelName: _a, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(pI(ce(n, "float32")), e) };
 } };
-var az = { kernelName: lo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var az = { kernelName: uo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
   return { x: () => {
     let o = sI([r], s.rank), u = Qk(e, r, a, !i);
@@ -9769,15 +9769,15 @@ var iz = { kernelName: Aa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) =>
   let [u, l] = t;
   return O(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), O(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), O(Ps(r, o), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`), pn("depthwiseConv2d", a, i), { x: () => II(u.shape, e, l, r, a, o, i), filter: () => kI(u, e, l.shape, r, a, o, i) };
 } };
-var oz = { kernelName: Yd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
+var oz = { kernelName: Xd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
   let [s, r] = t, a = { x: s, filter: r, dy: e }, i = { x: s, filter: r, dy: e };
-  return { x: () => M.runKernel(Xf, a, n), filter: () => M.runKernel(Yf, i, n) };
+  return { x: () => M.runKernel(Kf, a, n), filter: () => M.runKernel(Xf, i, n) };
 } };
 var uz = { kernelName: Ra, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t, s = { dy: e, y: n };
-  return { x: () => M.runKernel(gg, s) };
+  return { x: () => M.runKernel(mg, s) };
 } };
-var lz = { kernelName: hl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var lz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t, s = V(jn(kt(ct(n))), 2 / Math.sqrt(Math.PI));
   return { x: () => V(e, s) };
 } };
@@ -9785,11 +9785,11 @@ var cz = { kernelName: Da, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, n) };
 } };
-var dz = { kernelName: fo, inputsToSave: ["input"], gradFunc: (e, t) => {
+var dz = { kernelName: ho, inputsToSave: ["input"], gradFunc: (e, t) => {
   let [n] = t;
   return { input: () => G(e, n.shape) };
 } };
-var pz = { kernelName: mo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var pz = { kernelName: fo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, jn(n)) };
 } };
@@ -9828,20 +9828,20 @@ var mz = { kernelName: Pa, inputsToSave: ["x", "mean", "variance", "scale"], gra
     return a.rank === 1 && (x = ye(x, l)), G(x, a.shape);
   } };
 } };
-var gz = { kernelName: bo, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
+var gz = { kernelName: go, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
   let [s, r] = t, { axis: a } = n, i = Jn(a, s.shape)[0];
   return { x: () => {
-    let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = gx(0, p), m = gx(p + 1, p + 1 + h), g = bx([c, [l], d]), b = G(e, g), y = G(r, [l]), v = bx([[p], f, m]), x = qe(b, v), k = A3(x, y, s.shape[i]), C = Qg(v);
+    let u = s.shape, l = r.size, c = u.slice(0, i), p = c.length, d = u.slice(a, u.length).slice(1), h = d.length, f = mx(0, p), m = mx(p + 1, p + 1 + h), g = gx([c, [l], d]), b = G(e, g), y = G(r, [l]), v = gx([[p], f, m]), x = qe(b, v), k = A3(x, y, s.shape[i]), C = Yg(v);
     return k = qe(k, C), k;
   }, indices: () => r };
 } };
-function gx(e, t) {
+function mx(e, t) {
   let n = [];
   for (let s = e; s < t; ++s)
     n.push(s);
   return n;
 }
-function bx(e) {
+function gx(e) {
   let t = [];
   for (let n = 0; n < e.length; ++n)
     for (let s = 0; s < e[n].length; ++s)
@@ -9853,14 +9853,14 @@ var bz = { kernelName: za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   return { a: () => je(n), b: () => je(s) };
 } };
 var yz = { kernelName: Ma, gradFunc: (e) => ({ x: () => ce(e, "float32") }) };
-var vz = { kernelName: ml, gradFunc: (e) => ({ x: () => je(e) }) };
-var xz = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
-var wz = { kernelName: bl, gradFunc: (e) => ({ x: () => je(e) }) };
+var vz = { kernelName: fl, gradFunc: (e) => ({ x: () => je(e) }) };
+var xz = { kernelName: ml, gradFunc: (e) => ({ x: () => je(e) }) };
+var wz = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
 var kz = { kernelName: La, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { alpha: r } = n, a = Wn(s, 0);
   return { x: () => vn(a, e, V(e, r)) };
 } };
-var Iz = { kernelName: yl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Iz = { kernelName: bl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, ie(n, 1)) };
 } };
@@ -9877,32 +9877,32 @@ var Cz = { kernelName: k$, inputsToSave: [], outputsToSave: [true], gradFunc: (e
 } };
 function Nz(e, t, n, s = 5, r = 1, a = 1, i = 0.5) {
   let o = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: i };
-  return M.runKernel(xg, o, u);
+  return M.runKernel(vg, o, u);
 }
 var Tz = L({ localResponseNormalizationBackprop_: Nz });
-var $z = { kernelName: ep, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+var $z = { kernelName: Jd, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
   let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
   return { x: () => Tz(s, r, e, a, i, o, u) };
 } };
 function DI(e, t, n, s) {
   return t.rank < n.rank && (t = G(t, la(t.shape, s))), e.rank < n.rank && (e = G(e, la(e.shape, s))), { x: () => V(e, ce(qn(n, t), e.dtype)) };
 }
-var yx = { kernelName: Va, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+var bx = { kernelName: Va, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
   let s = n, { reductionIndices: r } = s, a = t[0], i = t[1], o = Jn(r, a.shape), u = DI(e, i, a, o);
   return { x: () => u.x() };
 } };
 var _z = { kernelName: Wa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
-  return { a: () => V(e, ce(Ko(n, s), "float32")), b: () => V(e, ce(Jk(n, s), "float32")) };
+  return { a: () => V(e, ce(jo(n, s), "float32")), b: () => V(e, ce(Jk(n, s), "float32")) };
 } };
 function Az(e, t, n, s, r, a, i) {
   let o = _(e, "dy", "maxPool3dGrad"), u = _(t, "input", "maxPool3dGrad"), l = _(n, "output", "maxPool3dGrad"), c = o, p = u, d = l, h = false;
   u.rank === 4 && (h = true, c = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), p = G(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = G(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), O(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), O(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), O(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), pn("maxPool3dGrad", a, i);
-  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = M.runKernel(kg, f, m);
+  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = M.runKernel(wg, f, m);
   return h ? G(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
 }
 var Ez = L({ maxPool3dGrad_: Az });
-var Rz = { kernelName: tp, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
+var Rz = { kernelName: ep, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
   let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
   return { x: () => Ez(e, s, r, a, i, o, u) };
 } };
@@ -9910,7 +9910,7 @@ function Dz(e, t, n, s, r, a, i) {
   let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
   O(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), O(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), O(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), pn("maxPoolGrad", a, i);
   let c = { dy: o, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: i };
-  return M.runKernel(wg, c, p);
+  return M.runKernel(xg, c, p);
 }
 var Fz = L({ maxPoolGrad_: Dz });
 var Oz = { kernelName: Ua, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
@@ -9934,19 +9934,19 @@ var zz = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc:
 } };
 var Mz = { kernelName: qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t;
-  return { a: () => V(e, ce(Xo(n, s), "float32")), b: () => V(e, ce(Wn(n, s), "float32")) };
+  return { a: () => V(e, ce(Ko(n, s), "float32")), b: () => V(e, ce(Wn(n, s), "float32")) };
 } };
 var Lz = { kernelName: ja, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
   return { x: () => He(e, a, s.shape) };
 } };
-var Bz = { kernelName: xl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
+var Bz = { kernelName: vl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
   let [n, s] = t, r = it(n.shape, s.shape);
   return { a: () => {
     let o = _t(n.shape, r);
     return o.length > 0 ? G(ye(e, o), n.shape) : e;
   }, b: () => {
-    let o = V(e, kt(mp(xe(n, s)))), u = _t(s.shape, r);
+    let o = V(e, kt(fp(xe(n, s)))), u = _t(s.shape, r);
     return u.length > 0 ? G(ye(o, u), s.shape) : o;
   } };
 } };
@@ -9960,17 +9960,17 @@ var Vz = { kernelName: Ka, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
     return u.length > 0 ? G(ye(o, u), s.shape) : o;
   } };
 } };
-var Wz = { kernelName: Io, gradFunc: (e) => ({ x: () => kt(e) }) };
-var Uz = { kernelName: $o, inputsToSave: ["indices"], gradFunc: (e, t) => {
+var Wz = { kernelName: ko, gradFunc: (e) => ({ x: () => kt(e) }) };
+var Uz = { kernelName: To, inputsToSave: ["indices"], gradFunc: (e, t) => {
   let n = t[0];
   return { indices: () => $t(n.shape, "float32") };
 } };
-var Gz = { kernelName: To, gradFunc: (e) => ({ x: () => je(e) }) };
-var Hz = { kernelName: _o, saveAllInputs: true, gradFunc: (e, t, n) => {
+var Gz = { kernelName: No, gradFunc: (e) => ({ x: () => je(e) }) };
+var Hz = { kernelName: $o, saveAllInputs: true, gradFunc: (e, t, n) => {
   let { axis: s } = n;
   return Fs(e, s).map((a) => () => a);
 } };
-var vx = { kernelName: Xa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var yx = { kernelName: Xa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
   return { x: () => He(e, a, s.shape) };
 } };
@@ -10003,44 +10003,44 @@ var Kz = { kernelName: Ea, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
     return kt(xe(o, ce(l, "float32")));
   } };
 } };
-var Xz = { kernelName: Il, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Xz = { kernelName: kl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, kt(ct(n))) };
 } };
 var Yz = { kernelName: ei, inputsToSave: ["x"], gradFunc: (e, t) => {
-  let [n] = t, s = V(Xo(n, 6), yp(n));
+  let [n] = t, s = V(Ko(n, 6), bp(n));
   return { x: () => V(e, ce(s, "float32")) };
 } };
 var Qz = { kernelName: Za, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(e, ce(yp(n), "float32")) };
+  return { x: () => V(e, ce(bp(n), "float32")) };
 } };
-var Zz = { kernelName: Eo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var Zz = { kernelName: Ao, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => G(e, n.shape) };
 } };
 var Jz = { kernelName: Ja, inputsToSave: ["images"], gradFunc: (e, t, n) => {
-  let [s] = t, r = { dy: e, images: s };
-  return { images: () => M.runKernel(Ng, r, n) };
-} };
-var eM = { kernelName: Sl, inputsToSave: ["images"], gradFunc: (e, t, n) => {
   let [s] = t, r = { dy: e, images: s };
   return { images: () => M.runKernel(Cg, r, n) };
 } };
-var tM = { kernelName: Ro, gradFunc: (e, t, n) => {
+var eM = { kernelName: Il, inputsToSave: ["images"], gradFunc: (e, t, n) => {
+  let [s] = t, r = { dy: e, images: s };
+  return { images: () => M.runKernel(Sg, r, n) };
+} };
+var tM = { kernelName: Eo, gradFunc: (e, t, n) => {
   let { dims: s } = n, r = Jn(s, e.shape);
   return { x: () => Yn(e, r) };
 } };
-var nM = { kernelName: Do, gradFunc: (e) => ({ x: () => je(e) }) };
+var nM = { kernelName: Ro, gradFunc: (e) => ({ x: () => je(e) }) };
 var sM = { kernelName: ti, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => kt(xe(e, V(ca(n, 1.5), 2))) };
 } };
-var rM = { kernelName: Oo, inputsToSave: ["condition"], gradFunc: (e, t) => {
+var rM = { kernelName: Fo, inputsToSave: ["condition"], gradFunc: (e, t) => {
   let [n] = t;
-  return { condition: () => ce(je(n), "float32"), t: () => V(e, ce(n, e.dtype)), e: () => V(e, ce(Zg(n), e.dtype)) };
+  return { condition: () => ce(je(n), "float32"), t: () => V(e, ce(n, e.dtype)), e: () => V(e, ce(Qg(n), e.dtype)) };
 } };
-var aM = { kernelName: Cl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var aM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => {
     let s = Wn(n, Ie(0)), r = Ie(_I), a = Ie(AI), i = V(e, a), o = V(V(e, r), jn(ce(n, "float32")));
@@ -10051,16 +10051,16 @@ var iM = { kernelName: si, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, V(n, ge(Ie(1), n))) };
 } };
-var oM = { kernelName: Nl, gradFunc: (e) => ({ x: () => je(e) }) };
+var oM = { kernelName: Cl, gradFunc: (e) => ({ x: () => je(e) }) };
 var uM = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => V(qg(ce(n, "float32")), e) };
+  return { x: () => V(Hg(ce(n, "float32")), e) };
 } };
-var lM = { kernelName: zo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var lM = { kernelName: Po, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(Yk(ce(n, "float32")), e) };
 } };
-var cM = { kernelName: Po, inputsToSave: ["x"], gradFunc: (e, t, n) => {
+var cM = { kernelName: Oo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = Fk(s, r, a), l = [];
   for (let c = 0; c < e.rank; c++)
     l.push([o[c], i[c] - o[c] - u[c]]);
@@ -10070,15 +10070,15 @@ var dM = { kernelName: ii, outputsToSave: [true], gradFunc: (e, t, n) => {
   let [s] = t, { dim: r } = n, a = true, i = V(e, s);
   return { logits: () => ge(i, V(ye(i, [r], a), s)) };
 } };
-var pM = { kernelName: Tl, inputsToSave: ["x"], gradFunc: (e, t) => {
+var pM = { kernelName: Nl, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, qs(n)) };
 } };
-var xx = { kernelName: Mo, gradFunc: (e, t, n) => {
+var vx = { kernelName: zo, gradFunc: (e, t, n) => {
   let { blockShape: s, paddings: r } = n;
-  return { x: () => Gg(e, s, r) };
+  return { x: () => Ug(e, s, r) };
 } };
-var wx = { kernelName: Lo, gradFunc: (e, t, n) => {
+var xx = { kernelName: Mo, gradFunc: (e, t, n) => {
   let { axis: s } = n;
   return { x: () => Ft(e, s) };
 } };
@@ -10086,7 +10086,7 @@ var hM = { kernelName: ri, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => xe(e, V(ln(ce(n, "float32")), 2)) };
 } };
-var fM = { kernelName: _l, inputsToSave: ["x"], gradFunc: (e, t) => {
+var fM = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => V(e, V(ce(n, "float32"), 2)) };
 } };
@@ -10113,9 +10113,9 @@ var yM = { kernelName: ai, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   let o = G(e, r), u = V(o, zn(s.shape, "float32"));
   return { x: () => u };
 } };
-var vM = { kernelName: Vo, inputsToSave: ["x"], gradFunc: (e, t) => {
+var vM = { kernelName: Bo, inputsToSave: ["x"], gradFunc: (e, t) => {
   let [n] = t;
-  return { x: () => xe(e, ct(qg(n))) };
+  return { x: () => xe(e, ct(Hg(n))) };
 } };
 var xM = { kernelName: li, outputsToSave: [true], gradFunc: (e, t) => {
   let [n] = t;
@@ -10149,34 +10149,34 @@ var wM = { kernelName: Sr, inputsToSave: ["x"], gradFunc: (e, t, n) => {
   } };
 } };
 var kM = { kernelName: ci, gradFunc: (e, t, n) => {
-  let s = n, { perm: r } = s, a = Qg(r);
+  let s = n, { perm: r } = s, a = Yg(r);
   return { x: () => qe(e, a) };
 } };
-var IM = { kernelName: Go, gradFunc: (e, t, n) => {
+var IM = { kernelName: Uo, gradFunc: (e, t, n) => {
   let s = n, { axis: r } = s;
   return { value: () => Qn(e, r) };
 } };
-var SM = { kernelName: up, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
+var SM = { kernelName: op, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
   let [n] = t;
   return { x: () => CM(e, n) };
 } };
 function CM(e, t) {
-  let n = Tr(t, je(t)), s = Ku(e, n), r = Ko(t, Ie(0, "int32")), a = s.rank - r.rank;
+  let n = Tr(t, je(t)), s = ju(e, n), r = jo(t, Ie(0, "int32")), a = s.rank - r.rank;
   for (let o = 0; o < a; ++o)
     r = On(r, o + 1);
   r = Ds(r, zn(s.shape, "bool"));
   let i = je(s);
   return vn(r, s, i);
 }
-var NM = { kernelName: Ho, gradFunc: (e) => ({ x: () => je(e) }) };
-var TM = [RI, TP, $P, _P, AP, EP, RP, DP, FP, OP, PP, zP, BP, UP, GP, HP, qP, jP, KP, XP, YP, QP, JP, ZP, nz, sz, rz, az, iz, oz, Kz, uz, lz, cz, dz, pz, fz, hz, mz, gz, bz, yz, vz, xz, wz, kz, Iz, Sz, Cz, $z, yx, yx, _z, Rz, Oz, Pz, zz, Mz, Lz, Bz, Vz, Wz, Uz, Gz, Hz, vx, vx, qz, jz, Xz, Yz, Qz, Zz, Jz, eM, tM, nM, sM, rM, aM, iM, oM, uM, lM, cM, dM, pM, xx, xx, wx, wx, hM, mM, fM, gM, bM, yM, vM, xM, wM, kM, IM, SM, NM];
+var NM = { kernelName: Go, gradFunc: (e) => ({ x: () => je(e) }) };
+var TM = [RI, TP, $P, _P, AP, EP, RP, DP, FP, OP, PP, zP, BP, UP, GP, HP, qP, jP, KP, XP, YP, QP, JP, ZP, nz, sz, rz, az, iz, oz, Kz, uz, lz, cz, dz, pz, fz, hz, mz, gz, bz, yz, vz, xz, wz, kz, Iz, Sz, Cz, $z, bx, bx, _z, Rz, Oz, Pz, zz, Mz, Lz, Bz, Vz, Wz, Uz, Gz, Hz, yx, yx, qz, jz, Xz, Yz, Qz, Zz, Jz, eM, tM, nM, sM, rM, aM, iM, oM, uM, lM, cM, dM, pM, vx, vx, xx, xx, hM, mM, fM, gM, bM, yM, vM, xM, wM, kM, IM, SM, NM];
 for (let e of TM)
   S$(e);
 var $M = {};
 Ae($M, { maxNorm: () => RM, minMaxNorm: () => OM, nonNeg: () => FM, unitNorm: () => DM });
-var Lf;
+var Mf;
 function Rt() {
-  return Lf == null && (Lf = $A().epsilon()), Lf;
+  return Mf == null && (Mf = $A().epsilon()), Mf;
 }
 function bs() {
   return "channelsLast";
@@ -10226,7 +10226,7 @@ function Cs(e, t) {
   if (!e)
     throw new FI(t);
 }
-function kx(e, t) {
+function wx(e, t) {
   let n = 0;
   for (let s of e)
     s === t && n++;
@@ -10246,25 +10246,25 @@ function qr(e) {
   return e.length <= 1 || e.indexOf("_") === -1 ? e : e.replace(/[_]+(\w|$)/g, (t, n) => n.toUpperCase());
 }
 var Gn = {};
-function xb(e) {
+function vb(e) {
   if (e == null)
     return null;
   let t = {};
   return t.className = e.getClassName(), t.config = e.getConfig(), t;
 }
-function fm(e) {
+function hm(e) {
   if (!(e == null || typeof e != "object"))
     if (Array.isArray(e))
-      e.forEach((t) => fm(t));
+      e.forEach((t) => hm(t));
     else {
       let t = Object.keys(e);
       for (let n of t) {
         let s = e[n];
-        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : fm(s));
+        s != null && typeof s == "object" && (!Array.isArray(s) && s.type === "ndarray" && typeof s.value == "number" ? e[n] = s.value : hm(s));
       }
     }
 }
-function Ml(e, t = {}, n = {}, s = "object", r = false) {
+function zl(e, t = {}, n = {}, s = "object", r = false) {
   if (typeof e == "string") {
     let a = e, i;
     if (a in n)
@@ -10297,7 +10297,7 @@ function Ml(e, t = {}, n = {}, s = "object", r = false) {
       let p = { ...Gn };
       for (let h of Object.keys(n))
         Gn[h] = n[h];
-      fm(a.config);
+      hm(a.config);
       let d = u(o, a.config, n, r);
       return Gn = { ...p }, d;
     } else {
@@ -10312,7 +10312,7 @@ function Ml(e, t = {}, n = {}, s = "object", r = false) {
 function _M(e, t) {
   return e < t ? -1 : e > t ? 1 : 0;
 }
-function Uc(e, t) {
+function Wc(e, t) {
   return -1 * _M(e, t);
 }
 function cr(e) {
@@ -10335,7 +10335,7 @@ function hi(e, t, n) {
   if (n != null && e.indexOf(n) < 0)
     throw new U(`${n} is not a valid ${t}.  Valid values are ${e} or null/undefined.`);
 }
-function wb(e, t, n = 0, s = 1 / 0) {
+function xb(e, t, n = 0, s = 1 / 0) {
   return Cs(n >= 0), Cs(s >= n), Array.isArray(e) && e.length >= n && e.length <= s && e.every((r) => typeof r === t);
 }
 function Bt(e, t) {
@@ -10354,22 +10354,22 @@ function EM(e, t, n) {
 function PI(e) {
   return e === "relu" ? "relu" : e === "linear" ? "linear" : e === "elu" ? "elu" : null;
 }
-function kb(e, t) {
+function wb(e, t) {
   return j(() => ln(ye(V(e, e), t, true)));
 }
-var Ll = class extends ae.Serializable {
+var Ml = class extends ae.Serializable {
   getConfig() {
     return {};
   }
 };
-var Ib = class extends Ll {
+var kb = class extends Ml {
   constructor(e) {
     super();
     this.defaultMaxValue = 2, this.defaultAxis = 0, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return j(() => {
-      let t = kb(e, this.axis), n = Bn(t, 0, this.maxValue);
+      let t = wb(e, this.axis), n = Bn(t, 0, this.maxValue);
       return V(e, xe(n, ie(Rt(), t)));
     });
   }
@@ -10377,37 +10377,37 @@ var Ib = class extends Ll {
     return { maxValue: this.maxValue, axis: this.axis };
   }
 };
-Ib.className = "MaxNorm";
-ae.registerClass(Ib);
-var Sb = class extends Ll {
+kb.className = "MaxNorm";
+ae.registerClass(kb);
+var Ib = class extends Ml {
   constructor(e) {
     super();
     this.defaultAxis = 0, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
-    return j(() => xe(e, ie(Rt(), kb(e, this.axis))));
+    return j(() => xe(e, ie(Rt(), wb(e, this.axis))));
   }
   getConfig() {
     return { axis: this.axis };
   }
 };
-Sb.className = "UnitNorm";
-ae.registerClass(Sb);
-var Cb = class extends Ll {
+Ib.className = "UnitNorm";
+ae.registerClass(Ib);
+var Sb = class extends Ml {
   apply(e) {
     return Xs(e);
   }
 };
-Cb.className = "NonNeg";
-ae.registerClass(Cb);
-var Nb = class extends Ll {
+Sb.className = "NonNeg";
+ae.registerClass(Sb);
+var Cb = class extends Ml {
   constructor(e) {
     super();
     this.defaultMinValue = 0, this.defaultMaxValue = 1, this.defaultRate = 1, this.defaultAxis = 0, this.minValue = e.minValue != null ? e.minValue : this.defaultMinValue, this.maxValue = e.maxValue != null ? e.maxValue : this.defaultMaxValue, this.rate = e.rate != null ? e.rate : this.defaultRate, this.axis = e.axis != null ? e.axis : this.defaultAxis;
   }
   apply(e) {
     return j(() => {
-      let t = kb(e, this.axis), n = ie(V(this.rate, Bn(t, this.minValue, this.maxValue)), V(1 - this.rate, t));
+      let t = wb(e, this.axis), n = ie(V(this.rate, Bn(t, this.minValue, this.maxValue)), V(1 - this.rate, t));
       return V(e, xe(n, ie(Rt(), t)));
     });
   }
@@ -10415,35 +10415,35 @@ var Nb = class extends Ll {
     return { minValue: this.minValue, maxValue: this.maxValue, rate: this.rate, axis: this.axis };
   }
 };
-Nb.className = "MinMaxNorm";
-ae.registerClass(Nb);
-var Ix = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
+Cb.className = "MinMaxNorm";
+ae.registerClass(Cb);
+var kx = { maxNorm: "MaxNorm", minMaxNorm: "MinMaxNorm", nonNeg: "NonNeg", unitNorm: "UnitNorm" };
 function Ot(e) {
-  return xb(e);
+  return vb(e);
 }
-function Sx(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "constraint");
+function Ix(e, t = {}) {
+  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "constraint");
 }
 function Pt(e) {
   if (e == null)
     return null;
   if (typeof e == "string") {
-    let n = { className: e in Ix ? Ix[e] : e, config: {} };
-    return Sx(n);
+    let n = { className: e in kx ? kx[e] : e, config: {} };
+    return Ix(n);
   } else
-    return e instanceof Ll ? e : Sx(e);
+    return e instanceof Ml ? e : Ix(e);
 }
 function RM(e) {
-  return new Ib(e);
+  return new kb(e);
 }
 function DM(e) {
-  return new Sb(e);
+  return new Ib(e);
 }
 function FM() {
-  return new Cb();
+  return new Sb();
 }
 function OM(e) {
-  return new Nb(e);
+  return new Cb(e);
 }
 var PM = {};
 Ae(PM, { constant: () => aL, glorotNormal: () => pL, glorotUniform: () => dL, heNormal: () => hL, heUniform: () => fL, identity: () => lL, leCunNormal: () => mL, leCunUniform: () => gL, ones: () => rL, orthogonal: () => bL, randomNormal: () => oL, randomUniform: () => iL, truncatedNormal: () => uL, varianceScaling: () => cL, zeros: () => sL });
@@ -10465,19 +10465,19 @@ function Un(e) {
 function zI(e) {
   hi(BM, "PoolMode", e);
 }
-var Mu = [];
-var Cx = "/";
+var zu = [];
+var Sx = "/";
 function Zr(e, t) {
-  Mu.push(e);
+  zu.push(e);
   try {
     let n = t();
-    return Mu.pop(), n;
+    return zu.pop(), n;
   } catch (n) {
-    throw Mu.pop(), n;
+    throw zu.pop(), n;
   }
 }
 function UM() {
-  return Mu.length === 0 ? "" : Mu.join(Cx) + Cx;
+  return zu.length === 0 ? "" : zu.join(Sx) + Sx;
 }
 function MI(e) {
   if (!BI(e))
@@ -10509,7 +10509,7 @@ function dr(e, t, n) {
     s *= e[r];
   return s;
 }
-function Zi(e) {
+function Qi(e) {
   if (e.length === 0)
     return Number.NaN;
   let t = Number.POSITIVE_INFINITY;
@@ -10537,10 +10537,10 @@ function ys(e, t) {
     n.push(s);
   return n;
 }
-function Sp(e, t) {
+function Ip(e, t) {
   return ce(e, t);
 }
-function Bl(e, t = -1) {
+function Ll(e, t = -1) {
   let n = e.shape.slice();
   return t < 0 && (t = n.length + t + 1), n.splice(t, 0, 1), G(e, n);
 }
@@ -10548,8 +10548,8 @@ function qM(e, t) {
   return j(() => {
     if (e.shape.length !== 2)
       throw new U(`repeat() expects a rank-2 tensor, but received a rank-${e.shape.length} tensor.`);
-    let n = Bl(e, 1);
-    return mm(n, [1, t, 1]);
+    let n = Ll(e, 1);
+    return fm(n, [1, t, 1]);
   });
 }
 function jM(e) {
@@ -10566,13 +10566,13 @@ function Jr(e, t, n) {
   return j(() => {
     switch (e.rank) {
       case 1:
-        return ab(e, t, n);
+        return rb(e, t, n);
       case 2:
         return hI(e, [t, 0], [n, e.shape[1]]);
       case 3:
-        return ib(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
+        return ab(e, [t, 0, 0], [n, e.shape[1], e.shape[2]]);
       case 4:
-        return kd(e, [t, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3]]);
+        return wd(e, [t, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3]]);
       case 5:
         return He(e, [t, 0, 0, 0, 0], [n, e.shape[1], e.shape[2], e.shape[3], e.shape[4]]);
       case 6:
@@ -10582,33 +10582,33 @@ function Jr(e, t, n) {
     }
   });
 }
-function Bf(e, t, n) {
+function Lf(e, t, n) {
   return j(() => {
     switch (e.rank) {
       case 1:
-        return ab(e, t, n);
+        return rb(e, t, n);
       case 2:
         return hI(e, [0, t], [e.shape[0], n]);
       case 3:
-        return ib(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
+        return ab(e, [0, 0, t], [e.shape[0], e.shape[1], n]);
       case 4:
-        return kd(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], n]);
+        return wd(e, [0, 0, 0, t], [e.shape[0], e.shape[1], e.shape[2], n]);
       default:
         throw new U(`sliceAlongLastAxis() received an unsupported tensor rank: ${e.rank}`);
     }
   });
 }
-function Gc(e, t, n, s) {
+function Uc(e, t, n, s) {
   return j(() => {
     switch (e.rank) {
       case 1:
-        return ab(e, t, n);
+        return rb(e, t, n);
       case 2:
         switch (s) {
           case 1:
             return Jr(e, t, n);
           case 2:
-            return Bf(e, t, n);
+            return Lf(e, t, n);
           default:
             throw new U(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10617,9 +10617,9 @@ function Gc(e, t, n, s) {
           case 1:
             return Jr(e, t, n);
           case 2:
-            return ib(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
+            return ab(e, [0, t, 0], [e.shape[0], n, e.shape[2]]);
           case 3:
-            return Bf(e, t, n);
+            return Lf(e, t, n);
           default:
             throw new U(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10628,11 +10628,11 @@ function Gc(e, t, n, s) {
           case 1:
             return Jr(e, t, n);
           case 2:
-            return kd(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
+            return wd(e, [0, t, 0, 0], [e.shape[0], n, e.shape[2], e.shape[3]]);
           case 3:
-            return kd(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
+            return wd(e, [0, 0, t, 0], [e.shape[0], e.shape[1], n, e.shape[3]]);
           case 4:
-            return Bf(e, t, n);
+            return Lf(e, t, n);
           default:
             throw new U(`The axis is not within the rank of the tensor ${s}`);
         }
@@ -10641,11 +10641,11 @@ function Gc(e, t, n, s) {
     }
   });
 }
-function Tb(e, t = -1) {
+function Nb(e, t = -1) {
   let n;
   return t < 0 && (n = e[0].rank, n !== 0 ? t = n : t = 0), t === e[0].rank && (t = -1), Ft(e, t);
 }
-function Nx(e, t) {
+function Cx(e, t) {
   switch (e.rank) {
     case 1:
       return RE([e, t]);
@@ -10659,12 +10659,12 @@ function Nx(e, t) {
       throw new U(`concatAlongFirstAxis() received an unsupported tensor rank: ${e.rank}`);
   }
 }
-function mm(e, t) {
+function fm(e, t) {
   if (Array.isArray(t) || (t = [t]), e.rank !== t.length)
     throw new U(`The length of input n (${t.length}) does not match the number of dimensions in input x (${e.rank})`);
   return cs(e, t);
 }
-function Cp(e, t = 0, n = 1, s, r) {
+function Sp(e, t = 0, n = 1, s, r) {
   return zD(e, t, n, s, r);
 }
 function Es(e, t, n, s) {
@@ -10676,23 +10676,23 @@ function Es(e, t, n, s) {
       throw new Fe(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${e.shape} and  y shape = ${t.shape}`);
   }
   if (e.rank === 2 && t.rank === 2)
-    return da.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? gm(e.rank, s, bs()) : null, activation: n });
+    return da.matMul({ a: e, b: t, transposeA: false, transposeB: false, bias: s ? mm(e.rank, s, bs()) : null, activation: n });
   {
     let r = e.shape.slice(), a = r.pop();
     e = G(e, [-1, a]);
     let i = t.shape.slice(), o = i.pop(), u = i.pop(), l = [...i, o], c = Array.from({ length: t.rank }, (f, m) => m === 0 ? t.rank - 2 : m <= t.rank - 2 ? m - 1 : m);
     t = G(qe(t, c), [u, -1]);
     let p = [...r, ...l], d = false, h = false;
-    return G(da.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? gm(e.rank, s, bs()) : null, activation: n }), p);
+    return G(da.matMul({ a: e, b: t, transposeA: d, transposeB: h, bias: s ? mm(e.rank, s, bs()) : null, activation: n }), p);
   }
 }
 function VI(e, t, n) {
-  return j(() => (Array.isArray(t) ? t = Qt(t, "int32") : t = ce(t, "int32"), Ku(e, t, n)));
+  return j(() => (Array.isArray(t) ? t = Qt(t, "int32") : t = ce(t, "int32"), ju(e, t, n)));
 }
-function Vl(e) {
+function Bl(e) {
   return V(e, e);
 }
-function gm(e, t, n) {
+function mm(e, t, n) {
   let s = t.shape;
   if (t.rank !== 1 && t.rank !== e)
     throw new U(`Unexpected bias dimensions: ${t.rank}; expected it to be 1 or ${e}`);
@@ -10716,12 +10716,12 @@ function gm(e, t, n) {
   throw new U(`Unsupported input rank by biasAdd: ${t.rank}`);
 }
 function ws(e, t, n) {
-  return j(() => (n == null && (n = bs()), St(n), ie(e, gm(e.rank, t, n))));
+  return j(() => (n == null && (n = bs()), St(n), ie(e, mm(e.rank, t, n))));
 }
 function XM(e, t = 1) {
   if (t !== 1)
     throw new Fe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);
-  return fp(e);
+  return hp(e);
 }
 function YM(e) {
   return j(() => xe(e, ie(Mt(e), 1)));
@@ -10735,7 +10735,7 @@ function QM(e) {
     return Bn(t, 0, 1);
   });
 }
-function Wl(e, t, n = false) {
+function Vl(e, t, n = false) {
   return n ? e() : t();
 }
 var ZM = ["fanIn", "fanOut", "fanAvg"];
@@ -10754,21 +10754,21 @@ var es = class extends ae.Serializable {
     return {};
   }
 };
-var $b = class extends es {
+var Tb = class extends es {
   apply(e, t) {
     return $t(e, t);
   }
 };
-$b.className = "Zeros";
-ae.registerClass($b);
-var Np = class extends es {
+Tb.className = "Zeros";
+ae.registerClass(Tb);
+var Cp = class extends es {
   apply(e, t) {
     return zn(e, t);
   }
 };
-Np.className = "Ones";
-ae.registerClass(Np);
-var _b = class extends es {
+Cp.className = "Ones";
+ae.registerClass(Cp);
+var $b = class extends es {
   constructor(e) {
     super();
     if (typeof e != "object")
@@ -10784,21 +10784,37 @@ var _b = class extends es {
     return { value: this.value };
   }
 };
-_b.className = "Constant";
-ae.registerClass(_b);
-var Ab = class extends es {
+$b.className = "Constant";
+ae.registerClass($b);
+var _b = class extends es {
   constructor(e) {
     super();
     this.DEFAULT_MINVAL = -0.05, this.DEFAULT_MAXVAL = 0.05, this.minval = e.minval || this.DEFAULT_MINVAL, this.maxval = e.maxval || this.DEFAULT_MAXVAL, this.seed = e.seed;
   }
   apply(e, t) {
-    return zl(e, this.minval, this.maxval, t);
+    return Pl(e, this.minval, this.maxval, t);
   }
   getConfig() {
     return { minval: this.minval, maxval: this.maxval, seed: this.seed };
   }
 };
-Ab.className = "RandomUniform";
+_b.className = "RandomUniform";
+ae.registerClass(_b);
+var Ab = class extends es {
+  constructor(e) {
+    super();
+    this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
+  }
+  apply(e, t) {
+    if (t = t || "float32", t !== "float32" && t !== "int32")
+      throw new Fe(`randomNormal does not support dType ${t}.`);
+    return Sp(e, this.mean, this.stddev, t, this.seed);
+  }
+  getConfig() {
+    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
+  }
+};
+Ab.className = "RandomNormal";
 ae.registerClass(Ab);
 var Eb = class extends es {
   constructor(e) {
@@ -10807,32 +10823,16 @@ var Eb = class extends es {
   }
   apply(e, t) {
     if (t = t || "float32", t !== "float32" && t !== "int32")
-      throw new Fe(`randomNormal does not support dType ${t}.`);
-    return Cp(e, this.mean, this.stddev, t, this.seed);
+      throw new Fe(`truncatedNormal does not support dType ${t}.`);
+    return lb(e, this.mean, this.stddev, t, this.seed);
   }
   getConfig() {
     return { mean: this.mean, stddev: this.stddev, seed: this.seed };
   }
 };
-Eb.className = "RandomNormal";
+Eb.className = "TruncatedNormal";
 ae.registerClass(Eb);
 var Rb = class extends es {
-  constructor(e) {
-    super();
-    this.DEFAULT_MEAN = 0, this.DEFAULT_STDDEV = 0.05, this.mean = e.mean || this.DEFAULT_MEAN, this.stddev = e.stddev || this.DEFAULT_STDDEV, this.seed = e.seed;
-  }
-  apply(e, t) {
-    if (t = t || "float32", t !== "float32" && t !== "int32")
-      throw new Fe(`truncatedNormal does not support dType ${t}.`);
-    return cb(e, this.mean, this.stddev, t, this.seed);
-  }
-  getConfig() {
-    return { mean: this.mean, stddev: this.stddev, seed: this.seed };
-  }
-};
-Rb.className = "TruncatedNormal";
-ae.registerClass(Rb);
-var Db = class extends es {
   constructor(e) {
     super();
     this.gain = e.gain != null ? e.gain : 1;
@@ -10848,8 +10848,8 @@ var Db = class extends es {
     return { gain: this.gain };
   }
 };
-Db.className = "Identity";
-ae.registerClass(Db);
+Rb.className = "Identity";
+ae.registerClass(Rb);
 function nL(e, t = "channelsLast") {
   let n, s;
   if (St(t), e.length === 2)
@@ -10881,10 +10881,10 @@ var xn = class extends es {
       let i = Math.sqrt(a);
       if (t = t || "float32", t !== "float32" && t !== "int32")
         throw new Fe(`${this.getClassName()} does not support dType ${t}.`);
-      return cb(e, 0, i, t, this.seed);
+      return lb(e, 0, i, t, this.seed);
     } else {
       let i = Math.sqrt(3 * a);
-      return zl(e, -i, i, t);
+      return Pl(e, -i, i, t);
     }
   }
   getConfig() {
@@ -10893,7 +10893,7 @@ var xn = class extends es {
 };
 xn.className = "VarianceScaling";
 ae.registerClass(xn);
-var Tp = class extends xn {
+var Np = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanAvg", distribution: "uniform", seed: e == null ? null : e.seed });
   }
@@ -10901,9 +10901,9 @@ var Tp = class extends xn {
     return xn.className;
   }
 };
-Tp.className = "GlorotUniform";
-ae.registerClass(Tp);
-var $p = class extends xn {
+Np.className = "GlorotUniform";
+ae.registerClass(Np);
+var Tp = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanAvg", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10911,9 +10911,9 @@ var $p = class extends xn {
     return xn.className;
   }
 };
-$p.className = "GlorotNormal";
-ae.registerClass($p);
-var _p = class extends xn {
+Tp.className = "GlorotNormal";
+ae.registerClass(Tp);
+var $p = class extends xn {
   constructor(e) {
     super({ scale: 2, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10921,9 +10921,9 @@ var _p = class extends xn {
     return xn.className;
   }
 };
-_p.className = "HeNormal";
-ae.registerClass(_p);
-var Ap = class extends xn {
+$p.className = "HeNormal";
+ae.registerClass($p);
+var _p = class extends xn {
   constructor(e) {
     super({ scale: 2, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
   }
@@ -10931,9 +10931,9 @@ var Ap = class extends xn {
     return xn.className;
   }
 };
-Ap.className = "HeUniform";
-ae.registerClass(Ap);
-var Ep = class extends xn {
+_p.className = "HeUniform";
+ae.registerClass(_p);
+var Ap = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
   }
@@ -10941,9 +10941,9 @@ var Ep = class extends xn {
     return xn.className;
   }
 };
-Ep.className = "LeCunNormal";
-ae.registerClass(Ep);
-var Rp = class extends xn {
+Ap.className = "LeCunNormal";
+ae.registerClass(Ap);
+var Ep = class extends xn {
   constructor(e) {
     super({ scale: 1, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
   }
@@ -10951,9 +10951,9 @@ var Rp = class extends xn {
     return xn.className;
   }
 };
-Rp.className = "LeCunNormal";
-ae.registerClass(Rp);
-var Fb = class extends es {
+Ep.className = "LeCunNormal";
+ae.registerClass(Ep);
+var Db = class extends es {
   constructor(e) {
     super();
     if (this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
@@ -10964,7 +10964,7 @@ var Fb = class extends es {
       if (e.length < 2)
         throw new Fe("Shape must be at least 2D.");
       e[0] * e[1] > 2e3 && console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${e[0] * e[1]}) elements: Slowness may result.`);
-      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = Cp(n, 0, 1, "float32"), r = AO.gramSchmidt(s);
+      let n = e[0] > e[1] ? [e[1], e[0]] : e, s = Sp(n, 0, 1, "float32"), r = AO.gramSchmidt(s);
       return e[0] > e[1] && (r = qe(r)), V(this.gain, r);
     });
   }
@@ -10972,96 +10972,96 @@ var Fb = class extends es {
     return { gain: this.gain, seed: this.seed };
   }
 };
-Fb.className = "Orthogonal";
-ae.registerClass(Fb);
-var Tx = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
-function $x(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "initializer");
+Db.className = "Orthogonal";
+ae.registerClass(Db);
+var Nx = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
+function Tx(e, t = {}) {
+  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "initializer");
 }
 function yt(e) {
-  return xb(e);
+  return vb(e);
 }
 function ht(e) {
   if (typeof e == "string") {
-    let t = e in Tx ? Tx[e] : e;
+    let t = e in Nx ? Nx[e] : e;
     if (t === "GlorotNormal")
-      return new $p();
-    if (t === "GlorotUniform")
       return new Tp();
+    if (t === "GlorotUniform")
+      return new Np();
     if (t === "HeNormal")
-      return new _p();
+      return new $p();
     if (t === "HeUniform")
-      return new Ap();
+      return new _p();
     if (t === "LeCunNormal")
-      return new Ep();
+      return new Ap();
     if (t === "LeCunUniform")
-      return new Rp();
+      return new Ep();
     {
       let n = {};
-      return n.className = t, n.config = {}, $x(n);
+      return n.className = t, n.config = {}, Tx(n);
     }
   } else
-    return e instanceof es ? e : $x(e);
+    return e instanceof es ? e : Tx(e);
 }
 function sL() {
-  return new $b();
+  return new Tb();
 }
 function rL() {
-  return new Np();
+  return new Cp();
 }
 function aL(e) {
-  return new _b(e);
+  return new $b(e);
 }
 function iL(e) {
-  return new Ab(e);
+  return new _b(e);
 }
 function oL(e) {
-  return new Eb(e);
+  return new Ab(e);
 }
 function uL(e) {
-  return new Rb(e);
+  return new Eb(e);
 }
 function lL(e) {
-  return new Db(e);
+  return new Rb(e);
 }
 function cL(e) {
   return new xn(e);
 }
 function dL(e) {
-  return new Tp(e);
+  return new Np(e);
 }
 function pL(e) {
-  return new $p(e);
+  return new Tp(e);
 }
 function hL(e) {
-  return new _p(e);
+  return new $p(e);
 }
 function fL(e) {
-  return new Ap(e);
+  return new _p(e);
 }
 function mL(e) {
-  return new Ep(e);
+  return new Ap(e);
 }
 function gL(e) {
-  return new Rp(e);
+  return new Ep(e);
 }
 function bL(e) {
-  return new Fb(e);
+  return new Db(e);
 }
 var yL = {};
-Ae(yL, { Layer: () => Ge, RNN: () => _r, RNNCell: () => Hl, activation: () => eV, add: () => lV, alphaDropout: () => qV, average: () => cV, averagePooling1d: () => Gy, averagePooling2d: () => Hy, averagePooling3d: () => qy, avgPool1d: () => vV, avgPool2d: () => wV, avgPool3d: () => IV, avgPooling1d: () => xV, avgPooling2d: () => kV, avgPooling3d: () => SV, batchNormalization: () => gV, bidirectional: () => MV, concatenate: () => dV, conv1d: () => HB, conv2d: () => qB, conv2dTranspose: () => jB, conv3d: () => KB, conv3dTranspose: () => XB, convLstm2d: () => FV, convLstm2dCell: () => OV, cropping2D: () => QB, dense: () => tV, depthwiseConv2d: () => JB, dot: () => mV, dropout: () => nV, elu: () => LB, embedding: () => uV, flatten: () => rV, gaussianDropout: () => HV, gaussianNoise: () => GV, globalAveragePooling1d: () => CV, globalAveragePooling2d: () => NV, globalMaxPool1d: () => BV, globalMaxPool2d: () => VV, globalMaxPooling1d: () => z0, globalMaxPooling2d: () => M0, gru: () => $V, gruCell: () => _V, input: () => CB, inputLayer: () => MB, layerNormalization: () => bV, leakyReLU: () => VB, lstm: () => AV, lstmCell: () => EV, masking: () => jV, maxPool1d: () => WV, maxPool2d: () => UV, maxPooling1d: () => L0, maxPooling2d: () => B0, maxPooling3d: () => TV, maximum: () => pV, minimum: () => hV, multiply: () => fV, permute: () => oV, prelu: () => WB, reLU: () => BB, repeatVector: () => aV, reshape: () => iV, rnn: () => PV, separableConv2d: () => YB, simpleRNN: () => RV, simpleRNNCell: () => DV, softmax: () => UB, spatialDropout1d: () => sV, stackedRNNCells: () => zV, thresholdedReLU: () => GB, timeDistributed: () => LV, upSampling2d: () => ZB, zeroPadding2d: () => yV });
+Ae(yL, { Layer: () => Ge, RNN: () => _r, RNNCell: () => Gl, activation: () => eV, add: () => lV, alphaDropout: () => qV, average: () => cV, averagePooling1d: () => Uy, averagePooling2d: () => Gy, averagePooling3d: () => Hy, avgPool1d: () => vV, avgPool2d: () => wV, avgPool3d: () => IV, avgPooling1d: () => xV, avgPooling2d: () => kV, avgPooling3d: () => SV, batchNormalization: () => gV, bidirectional: () => MV, concatenate: () => dV, conv1d: () => HB, conv2d: () => qB, conv2dTranspose: () => jB, conv3d: () => KB, conv3dTranspose: () => XB, convLstm2d: () => FV, convLstm2dCell: () => OV, cropping2D: () => QB, dense: () => tV, depthwiseConv2d: () => JB, dot: () => mV, dropout: () => nV, elu: () => LB, embedding: () => uV, flatten: () => rV, gaussianDropout: () => HV, gaussianNoise: () => GV, globalAveragePooling1d: () => CV, globalAveragePooling2d: () => NV, globalMaxPool1d: () => BV, globalMaxPool2d: () => VV, globalMaxPooling1d: () => z0, globalMaxPooling2d: () => M0, gru: () => $V, gruCell: () => _V, input: () => CB, inputLayer: () => MB, layerNormalization: () => bV, leakyReLU: () => VB, lstm: () => AV, lstmCell: () => EV, masking: () => jV, maxPool1d: () => WV, maxPool2d: () => UV, maxPooling1d: () => L0, maxPooling2d: () => B0, maxPooling3d: () => TV, maximum: () => pV, minimum: () => hV, multiply: () => fV, permute: () => oV, prelu: () => WB, reLU: () => BB, repeatVector: () => aV, reshape: () => iV, rnn: () => PV, separableConv2d: () => YB, simpleRNN: () => RV, simpleRNNCell: () => DV, softmax: () => UB, spatialDropout1d: () => sV, stackedRNNCells: () => zV, thresholdedReLU: () => GB, timeDistributed: () => LV, upSampling2d: () => ZB, zeroPadding2d: () => yV });
 var vL = 0;
 function UI() {
   return vL++;
 }
-var Hc = {};
-function Dp(e = "") {
-  return e in Hc || (Hc[e] = 0), Hc[e] += 1, e + Hc[e].toString();
+var Gc = {};
+function Rp(e = "") {
+  return e in Gc || (Gc[e] = 0), Gc[e] += 1, e + Gc[e].toString();
 }
-function bm(e) {
+function gm(e) {
   return Array.isArray(e) && Array.isArray(e[0]);
 }
-function Sd(e) {
+function Id(e) {
   return e.length === 0 ? [] : Array.isArray(e[0]) ? e : [e];
 }
 function Oe(e) {
@@ -11082,16 +11082,16 @@ function nt(e) {
   } else
     return e;
 }
-function Cd(e) {
+function Sd(e) {
   let t = 0;
   for (let n of e)
     n.shape.length === 0 ? t += 1 : t += n.shape.reduce((s, r) => s * r);
   return t;
 }
-var _x = "Variable";
+var $x = "Variable";
 var xL = class {
-  constructor(e, t = "float32", n = _x, s = true, r = null) {
-    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = UI(), n = n == null ? _x : n, this.originalName = MI(n), this.name = LI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = R3(e, this.trainable_, this.name, this.dtype);
+  constructor(e, t = "float32", n = $x, s = true, r = null) {
+    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = UI(), n = n == null ? $x : n, this.originalName = MI(n), this.name = LI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = R3(e, this.trainable_, this.name, this.dtype);
   }
   read() {
     return this.assertNotDisposed(), this.val;
@@ -11117,10 +11117,10 @@ function wL(e, t) {
   if (e.shape.toString() !== t.shape.toString())
     throw new Error("Shape mismatch: " + JSON.stringify(e.shape) + " vs. " + JSON.stringify(t.shape));
 }
-function ym(e) {
+function bm(e) {
   return e.map((t) => t.read());
 }
-function Ob(e) {
+function Fb(e) {
   e.forEach((t) => {
     t[0].write(t[1]);
   });
@@ -11136,7 +11136,7 @@ var $s = class {
   }
 };
 var kL = 0;
-var Fp = class {
+var Dp = class {
   constructor(e, t) {
     this.callArgs = t, this.id = kL++, this.outboundLayer = e.outboundLayer, this.inboundLayers = e.inboundLayers, this.nodeIndices = e.nodeIndices, this.tensorIndices = e.tensorIndices, this.inputTensors = e.inputTensors, this.outputTensors = e.outputTensors, this.inputMasks = e.inputMasks, this.outputMasks = e.outputMasks, this.inputShapes = e.inputShapes, this.outputShapes = e.outputShapes;
     for (let n of e.inboundLayers)
@@ -11158,7 +11158,7 @@ var Ge = class extends ae.Serializable {
     let t = e.name;
     if (!t) {
       let n = this.getClassName();
-      t = Ws(n) + "_" + Dp(n);
+      t = Ws(n) + "_" + Rp(n);
     }
     if (this.name = t, this.trainable_ = e.trainable == null ? true : e.trainable, e.inputShape != null || e.batchInputShape != null) {
       let n;
@@ -11361,13 +11361,13 @@ var Ge = class extends ae.Serializable {
   countParams() {
     if (!this.built)
       throw new ps(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
-    return Cd(this.weights);
+    return Sd(this.weights);
   }
   build(e) {
     this.built = true;
   }
   getWeights(e = false) {
-    return ym(e ? this.trainableWeights : this.weights);
+    return bm(e ? this.trainableWeights : this.weights);
   }
   setWeights(e) {
     j(() => {
@@ -11376,14 +11376,14 @@ var Ge = class extends ae.Serializable {
         throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);
       if (t.length === 0)
         return;
-      let n = [], s = ym(t);
+      let n = [], s = bm(t);
       for (let r = 0; r < s.length; ++r) {
         let a = s[r], i = t[r], o = e[r];
         if (!w.arraysEqual(a.shape, o.shape))
           throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);
         n.push([i, o]);
       }
-      Ob(n);
+      Fb(n);
     });
   }
   addWeight(e, t, n, s, r, a, i, o) {
@@ -11418,11 +11418,11 @@ var Ge = class extends ae.Serializable {
   }
   addInboundNode(e, t, n, s, r, a, i = null) {
     let o = dt(e);
-    t = dt(t), n = dt(n), s = dt(s), r = Sd(r), a = Sd(a);
+    t = dt(t), n = dt(n), s = dt(s), r = Id(r), a = Id(a);
     let u = [], l = [], c = [];
     for (let p of o)
       u.push(p.sourceLayer), l.push(p.nodeIndex), c.push(p.tensorIndex);
-    new Fp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
+    new Dp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
     for (let p = 0; p < t.length; p++)
       t[p].sourceLayer = this, t[p].nodeIndex = this.inboundNodes.length - 1, t[p].tensorIndex = p;
   }
@@ -11475,9 +11475,9 @@ function GI(e, t, n) {
     }
   }
 }
-var Qo = class extends Ge {
+var Yo = class extends Ge {
   constructor(e) {
-    super({ dtype: e.dtype, name: e.name != null ? e.name : Dp("input").toString() });
+    super({ dtype: e.dtype, name: e.name != null ? e.name : Rp("input").toString() });
     if (e.batchSize == null && (e.batchSize = null), e.sparse == null && (e.sparse = false), this.trainable = false, this.built = true, this.sparse = e.sparse, e.inputShape != null && e.batchInputShape != null)
       throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");
     let t = e.batchInputShape;
@@ -11490,7 +11490,7 @@ var Qo = class extends Ge {
     let n = e.dtype || "float32";
     this.batchInputShape = t, this.dtype = n, this.inputSpec = [{ shape: t }];
     let s = new $s(this.dtype, this.batchInputShape, this, [], {}, this.name);
-    s.nodeIndex = 0, s.tensorIndex = 0, new Fp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: [s], outputTensors: [s], inputMasks: [null], outputMasks: [null], inputShapes: [t], outputShapes: [t] });
+    s.nodeIndex = 0, s.tensorIndex = 0, new Dp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: [s], outputTensors: [s], inputMasks: [null], outputMasks: [null], inputShapes: [t], outputShapes: [t] });
   }
   apply(e, t) {
     throw new U(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);
@@ -11502,8 +11502,8 @@ var Qo = class extends Ge {
     return { batchInputShape: this.batchInputShape, dtype: this.dtype, sparse: this.sparse, name: this.name };
   }
 };
-Qo.className = "InputLayer";
-ae.registerClass(Qo);
+Yo.className = "InputLayer";
+ae.registerClass(Yo);
 function HI(e) {
   if (e.batchShape == null && e.shape == null)
     throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
@@ -11512,7 +11512,7 @@ function HI(e) {
   let t = e.batchShape;
   e.shape != null && t == null && (t = [null].concat(e.shape));
   let n = e.dtype;
-  return n == null && (n = "float32"), new Qo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
+  return n == null && (n = "float32"), new Yo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
 }
 async function rr(e) {
   if (e == null)
@@ -11540,7 +11540,7 @@ function qI(e) {
     }
 }
 var NL = 125;
-var Ji = class {
+var Zi = class {
   constructor() {
     this.validationData = null;
   }
@@ -11608,7 +11608,7 @@ var TL = class {
       await t.onTrainEnd(e);
   }
 };
-var $L = class extends Ji {
+var $L = class extends Zi {
   constructor() {
     super();
   }
@@ -11640,7 +11640,7 @@ var $L = class extends Ji {
         }));
   }
 };
-var _L = class extends Ji {
+var _L = class extends Zi {
   async onTrainBegin(e) {
     this.epoch = [], this.history = {};
   }
@@ -11664,7 +11664,7 @@ var _L = class extends Ji {
       this.history[t[r]][n[r]].dispose(), this.history[t[r]][n[r]] = s[r][0];
   }
 };
-var AL = class extends Ji {
+var AL = class extends Zi {
   constructor(e, t) {
     super();
     if (this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || RO, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = NL), this.yieldEvery === "never" && e.onYield != null)
@@ -11697,7 +11697,7 @@ var AL = class extends Ji {
   }
 };
 function jI(e, t) {
-  return e == null && (e = {}), e instanceof Ji ? [e] : Array.isArray(e) && e[0] instanceof Ji ? e : dt(e).map((s) => new AL(s, t));
+  return e == null && (e = {}), e instanceof Zi ? [e] : Array.isArray(e) && e[0] instanceof Zi ? e : dt(e).map((s) => new AL(s, t));
 }
 var ks = class {
   constructor() {
@@ -11724,31 +11724,31 @@ var ks = class {
     return t.map((n) => new n());
   }
 };
-var Pb = ks;
-Pb.constructors = {};
+var Ob = ks;
+Ob.constructors = {};
 function KI(e, t, n, s, r, a, i, o, u) {
-  let l = new _L(), c = [new $L(), ...Pb.createCallbacks(t)];
+  let l = new _L(), c = [new $L(), ...Ob.createCallbacks(t)];
   e != null && c.push(...e), c.push(l);
   let p = new TL(c);
   return p.setParams({ epochs: n, initialEpoch: s, samples: r, steps: a, batchSize: i, verbose: t, doValidation: o, metrics: u }), { callbackList: p, history: l };
 }
 function fs(e, t = {}, n = false) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "layer", n);
+  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "layer", n);
 }
-function Nd(e, t) {
+function Cd(e, t) {
   return j(() => {
     e.dtype !== "float32" && (e = ce(e, "float32"));
-    let n = ye(Vl(e), t, true), s = Ol(n.shape, Rt()), r = ln(Tr(n, s));
+    let n = ye(Bl(e), t, true), s = Fl(n.shape, Rt()), r = ln(Tr(n, s));
     return xe(e, r);
   });
 }
 function fi(e, t) {
-  return j(() => It(Vl(ge(t, e)), -1));
+  return j(() => It(Bl(ge(t, e)), -1));
 }
-function Op(e, t) {
+function Fp(e, t) {
   return j(() => It(Mt(ge(t, e)), -1));
 }
-function Zo(e, t) {
+function Qo(e, t) {
   return j(() => {
     let n = ge(e, t), s = Bn(Mt(e), Rt(), Number.MAX_VALUE), r = Mt(xe(n, s));
     return V(100, It(r, -1));
@@ -11757,13 +11757,13 @@ function Zo(e, t) {
 function EL(e, t) {
   return j(() => {
     let n = Bn(t, Rt(), Number.MAX_VALUE), s = Kn(ie(1, n)), r = Bn(e, Rt(), Number.MAX_VALUE), a = Kn(ie(1, r));
-    return It(Vl(ge(s, a)), -1);
+    return It(Bl(ge(s, a)), -1);
   });
 }
 function RL(e, t) {
   return j(() => {
     let n = Tr(0, ge(1, V(e, t)));
-    return It(Vl(n), -1);
+    return It(Bl(n), -1);
   });
 }
 function DL(e, t) {
@@ -11780,14 +11780,14 @@ function FL(e, t) {
 }
 function OL(e, t) {
   return j(() => {
-    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Pl(V(-2, s))), n);
+    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Ol(V(-2, s))), n);
     return It(r, -1);
   });
 }
-function Qu(e, t, n = false) {
+function Yu(e, t, n = false) {
   return j(() => {
     if (n)
-      t = ob(t);
+      t = ib(t);
     else {
       let s = ye(t, t.shape.length - 1, true);
       t = xe(t, s);
@@ -11795,12 +11795,12 @@ function Qu(e, t, n = false) {
     return t = Bn(t, Rt(), 1 - Rt()), kt(ye(V(ce(e, "float32"), Kn(t)), t.shape.length - 1));
   });
 }
-function Td(e, t, n = false) {
+function Nd(e, t, n = false) {
   return j(() => {
-    let s = ce(mp(jM(e)), "int32");
+    let s = ce(fp(jM(e)), "int32");
     t = Bn(t, Rt(), 1 - Rt());
-    let r = t.shape, a = G(vd(s, r[r.length - 1]), r);
-    return Qu(a, t, n);
+    let r = t.shape, a = G(yd(s, r[r.length - 1]), r);
+    return Yu(a, t, n);
   });
 }
 function PL(e, t) {
@@ -11808,10 +11808,10 @@ function PL(e, t) {
     throw new U(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);
   return j(() => {
     let n = Xs(t), s = kt(Mt(t));
-    return ie(ge(n, V(t, e)), Xg(jn(s)));
+    return ie(ge(n, V(t, e)), Kg(jn(s)));
   });
 }
-function Pp(e, t) {
+function Op(e, t) {
   return j(() => {
     let n;
     return n = Bn(t, Rt(), 1 - Rt()), n = Kn(xe(n, ge(1, n))), It(PL(e, n), -1);
@@ -11829,30 +11829,30 @@ function ML(e, t) {
     return It(ge(t, V(e, n)), -1);
   });
 }
-function zb(e, t) {
+function Pb(e, t) {
   return j(() => {
-    let n = Nd(e, -1), s = Nd(t, -1), r = V(n, s);
+    let n = Cd(e, -1), s = Cd(t, -1), r = V(n, s);
     return kt(ye(r, -1));
   });
 }
-var $d = { meanSquaredError: fi, meanAbsoluteError: Op, meanAbsolutePercentageError: Zo, meanSquaredLogarithmicError: EL, squaredHinge: RL, hinge: DL, categoricalHinge: FL, logcosh: OL, categoricalCrossentropy: Qu, sparseCategoricalCrossentropy: Td, binaryCrossentropy: Pp, kullbackLeiblerDivergence: zL, poisson: ML, cosineProximity: zb };
-function Vf(e) {
+var Td = { meanSquaredError: fi, meanAbsoluteError: Fp, meanAbsolutePercentageError: Qo, meanSquaredLogarithmicError: EL, squaredHinge: RL, hinge: DL, categoricalHinge: FL, logcosh: OL, categoricalCrossentropy: Yu, sparseCategoricalCrossentropy: Nd, binaryCrossentropy: Op, kullbackLeiblerDivergence: zL, poisson: ML, cosineProximity: Pb };
+function Bf(e) {
   if (typeof e == "string") {
-    if (e in $d)
-      return $d[e];
+    if (e in Td)
+      return Td[e];
     let t = `Unknown loss ${e}`;
     throw e.toLowerCase().includes("softmaxcrossentropy") && (t = `Unknown loss ${e}. Use "categoricalCrossentropy" as the string name for tf.losses.softmaxCrossEntropy`), new U(t);
   } else
     return e;
 }
-function Mb(e, t) {
+function zb(e, t) {
   return j(() => {
-    let n = V(0.5, Xn(t)), s = Sp(Wn(t, n), e.dtype);
+    let n = V(0.5, Xn(t)), s = Ip(Wn(t, n), e.dtype);
     return It(qn(e, s), -1);
   });
 }
-function Lb(e, t) {
-  return j(() => Sp(qn(Hu(e, -1), Hu(t, -1)), "float32"));
+function Mb(e, t) {
+  return j(() => Ip(qn(Gu(e, -1), Gu(t, -1)), "float32"));
 }
 function XI(e, t) {
   return j(() => ce(ye(Ds(qn(e, 1), qn(t, 1))), "float32"));
@@ -11876,42 +11876,42 @@ function VL(e, t) {
   });
 }
 function QI(e, t) {
-  return Pp(e, t);
+  return Op(e, t);
 }
 function ZI(e, t) {
-  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = Hu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(qn(e, t), "float32");
+  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = Gu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(qn(e, t), "float32");
 }
 var WL = fi;
 var UL = fi;
-var GL = Op;
-var HL = Op;
-var qL = Zo;
-var jL = Zo;
-var Bb = Qu;
-var KL = zb;
-var JI = Td;
-var _d = { binaryAccuracy: Mb, categoricalAccuracy: Lb, precision: YI, categoricalCrossentropy: Bb, sparseCategoricalCrossentropy: JI, mse: WL, MSE: UL, mae: GL, MAE: HL, mape: qL, MAPE: jL, cosine: KL };
+var GL = Fp;
+var HL = Fp;
+var qL = Qo;
+var jL = Qo;
+var Lb = Yu;
+var KL = Pb;
+var JI = Nd;
+var $d = { binaryAccuracy: zb, categoricalAccuracy: Mb, precision: YI, categoricalCrossentropy: Lb, sparseCategoricalCrossentropy: JI, mse: WL, MSE: UL, mae: GL, MAE: HL, mape: qL, MAPE: jL, cosine: KL };
 function XL(e) {
-  if (typeof e == "string" && e in _d)
-    return _d[e];
+  if (typeof e == "string" && e in $d)
+    return $d[e];
   if (typeof e != "string" && e != null)
     return e;
   throw new U(`Unknown metric ${e}`);
 }
-function qc(e) {
+function Hc(e) {
   if (Cs(e !== null, `Unknown LossOrMetricFn ${e}`), typeof e == "string")
     return e;
   {
     let t;
-    for (let n of Object.keys($d))
-      if ($d[n] === e) {
+    for (let n of Object.keys(Td))
+      if (Td[n] === e) {
         t = n;
         break;
       }
     if (t !== void 0)
       return t;
-    for (let n of Object.keys(_d))
-      if (_d[n] === e) {
+    for (let n of Object.keys($d))
+      if ($d[n] === e) {
         t = n;
         break;
       }
@@ -11924,28 +11924,28 @@ function YL(e) {
     return t[e]();
   throw new U(`Unknown Optimizer ${e}`);
 }
-var Ax = 1 * 1024 * 1024;
-function Ex(e, t, n = false) {
-  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !vm(e))
+var _x = 1 * 1024 * 1024;
+function Ax(e, t, n = false) {
+  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !ym(e))
     throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
   if (n) {
     let s = JSON.stringify(e);
-    s.length > Ax && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${Ax}.`);
+    s.length > _x && console.warn(`User-defined metadata of model "${t}" is too large in size (length=${s.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${_x}.`);
   }
 }
-function vm(e) {
+function ym(e) {
   if (e === null)
     return true;
   if (typeof e == "object")
     if (Object.getPrototypeOf(e) === Object.prototype) {
       let t = Object.keys(e);
       for (let n of t)
-        if (typeof n != "string" || !vm(e[n]))
+        if (typeof n != "string" || !ym(e[n]))
           return false;
       return true;
     } else if (Array.isArray(e)) {
       for (let t of e)
-        if (!vm(t))
+        if (!ym(t))
           return false;
       return true;
     } else
@@ -11964,17 +11964,17 @@ function QL(e, t, n, s = console.log) {
     for (let c in e.nodesByDepth)
       i.push(...e.nodesByDepth[c]);
   }
-  s("_".repeat(t)), Ad(a, n, s), s("=".repeat(t));
+  s("_".repeat(t)), _d(a, n, s), s("=".repeat(t));
   let o = e.layers;
   for (let c = 0; c < o.length; ++c)
     r ? eB(o[c], n, s) : tB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
   e.checkTrainableWeightsConsistency();
-  let u = ZL(e), l = Cd(e.nonTrainableWeights);
+  let u = ZL(e), l = Sd(e.nonTrainableWeights);
   s(`Total params: ${u + l}`), s(`Trainable params: ${u}`), s(`Non-trainable params: ${l}`), s("_".repeat(t));
 }
 function ZL(e) {
   let t;
-  return e.collectedTrainableWeights != null ? t = Cd(e.collectedTrainableWeights) : t = Cd(e.trainableWeights), t;
+  return e.collectedTrainableWeights != null ? t = Sd(e.collectedTrainableWeights) : t = Sd(e.trainableWeights), t;
 }
 function JL(e) {
   let t = true, n = [], s = [];
@@ -12002,7 +12002,7 @@ function JL(e) {
     }
   return t;
 }
-function Ad(e, t, n = console.log) {
+function _d(e, t, n = console.log) {
   let s = "";
   for (let r = 0; r < e.length; ++r)
     r > 0 && (s = s.slice(0, s.length - 1) + " "), s += e[r], s = s.slice(0, t[r]), s += " ".repeat(t[r] - s.length);
@@ -12021,7 +12021,7 @@ function eB(e, t, n) {
     s = "multiple";
   }
   let a = e.name, i = e.getClassName(), o = [`${a} (${i})`, r, s, e.countParams().toString()];
-  Ad(o, t, n);
+  _d(o, t, n);
 }
 function tB(e, t, n, s) {
   let r, a;
@@ -12043,14 +12043,14 @@ function tB(e, t, n, s) {
         i.push(`${h}[${f}][${m}]`);
       }
   let o = e.name, u = e.getClassName(), l = i.length === 0 ? "" : i[0], c = [`${o} (${u})`, a, r, e.countParams().toString(), l];
-  Ad(c, t, s);
+  _d(c, t, s);
   for (let p = 1; p < i.length; ++p)
-    Ad(["", "", "", "", i[p]], t, s);
+    _d(["", "", "", "", i[p]], t, s);
 }
 function e0(e, t, n) {
   return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
 }
-function Zu(e, t) {
+function Qu(e, t) {
   if (e === null)
     return null;
   if (typeof e == "string")
@@ -12061,7 +12061,7 @@ function Zu(e, t) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      e0(t, r, a) ? n.push(a) : n.push(Zu(a, t));
+      e0(t, r, a) ? n.push(a) : n.push(Qu(a, t));
     }
     return n;
   } else {
@@ -12072,13 +12072,13 @@ function Zu(e, t) {
         n[s] = r;
       else {
         let a = qr(s);
-        n[a] = Zu(r, a);
+        n[a] = Qu(r, a);
       }
     }
     return n;
   }
 }
-function xm(e, t) {
+function vm(e, t) {
   if (e == null)
     return null;
   if (typeof e == "string")
@@ -12089,14 +12089,14 @@ function xm(e, t) {
     let n = [], s = e.length;
     for (let r = 0; r < s; ++r) {
       let a = e[r];
-      e0(t, r, a) ? n.push(a) : n.push(xm(a, t));
+      e0(t, r, a) ? n.push(a) : n.push(vm(a, t));
     }
     return n;
   } else {
     let n = {};
     for (let s of Object.keys(e)) {
       let r = e[s], a = Ws(s);
-      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = xm(r, s);
+      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = vm(r, s);
     }
     return n;
   }
@@ -12167,27 +12167,27 @@ var Xr = class {
     this.id2Mask != null && Re(this.id2Mask);
   }
 };
-var Wf = {};
-var Rx = {};
-function Au(e, t, n, s) {
+var Vf = {};
+var Ex = {};
+function _u(e, t, n, s) {
   let r = n == null ? false : n.training, a = Array.isArray(e), i = a ? e : [e], o = i.map((f) => f.name), u = [], l = t.names();
   for (let f of o)
     l.indexOf(f) !== -1 ? u.push(t.getValue(f)) : u.push(null);
   s != null && (s.maxNumTensors = -1 / 0, s.minNumTensors = 1 / 0);
   let c = o.join(",") + "|" + t.names().join(","), p, d;
-  if (Wf[c] == null) {
+  if (Vf[c] == null) {
     let f = sB(i, t);
-    p = f.sorted, d = f.recipientCounts, Wf[c] = p, Rx[c] = d;
+    p = f.sorted, d = f.recipientCounts, Vf[c] = p, Ex[c] = d;
   }
-  p = Wf[c], d = {}, r || Object.assign(d, Rx[c]);
+  p = Vf[c], d = {}, r || Object.assign(d, Ex[c]);
   let h = new Xr(t);
   for (let f = 0; f < p.length; ++f) {
     if (s != null) {
-      let A = cm().numTensors;
+      let A = lm().numTensors;
       A > s.maxNumTensors && (s.maxNumTensors = A), A < s.minNumTensors && (s.minNumTensors = A);
     }
     let m = p[f], g = m.sourceLayer;
-    if (g instanceof Qo)
+    if (g instanceof Yo)
       continue;
     let b = [], y = [], v = [], x = false;
     for (let A of m.inputs) {
@@ -12211,12 +12211,12 @@ function sB(e, t) {
   w.assert(e != null && e.length > 0, () => "Expected at least one fetch, got none");
   let n = [], s = {};
   if (e.length === 1) {
-    let r = Dx(e[0], t);
+    let r = Rx(e[0], t);
     n = r.sorted, s = r.recipientMap;
   } else {
     let r = /* @__PURE__ */ new Set();
     for (let a of e) {
-      let { sorted: i, recipientMap: o } = Dx(a, t);
+      let { sorted: i, recipientMap: o } = Rx(a, t);
       for (let u of i)
         r.has(u.name) || (n.push(u), r.add(u.name));
       for (let u in o)
@@ -12231,7 +12231,7 @@ function rB(e) {
     t[n] = e[n].size;
   return t;
 }
-function Dx(e, t) {
+function Rx(e, t) {
   let n = /* @__PURE__ */ new Set(), s = [], r = {};
   for (let o of t.names())
     n.add(o);
@@ -12274,7 +12274,7 @@ var Is = class extends Ge {
     super({});
     if (this.containerNodes = /* @__PURE__ */ new Set(), this.name = e.name, this.name == null) {
       let b = this.getClassName().toLowerCase();
-      this.name = Dp(b);
+      this.name = Rp(b);
     }
     if (this.supportsMasking = false, this.trainable_ = true, Array.isArray(e.inputs) ? this.inputs = e.inputs.slice() : this.inputs = [e.inputs], Array.isArray(e.outputs) ? this.outputs = e.outputs.slice() : this.outputs = [e.outputs], cr(this.inputs).length !== this.inputs.length)
       throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((b) => b.name)}`);
@@ -12290,7 +12290,7 @@ var Is = class extends Ge {
     this.inputNames = [], this.outputNames = [], this.feedInputShapes = [], this.feedInputNames = [], this.feedOutputNames = [];
     for (let b = 0; b < this.inputLayers.length; b++) {
       let y = this.inputLayers[b];
-      if (!(y instanceof Qo))
+      if (!(y instanceof Yo))
         throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);
       this.inputNames.push(y.name), this.feedInputShapes.push(y.batchInputShape), this.feedInputNames.push(y.name);
     }
@@ -12336,7 +12336,7 @@ var Is = class extends Ge {
       let y = s[b];
       y in d || (d[y] = []), d[y].push(r[b]);
     }
-    let h = Object.keys(d).map((b) => parseInt(b, 10)).sort(Uc);
+    let h = Object.keys(d).map((b) => parseInt(b, 10)).sort(Wc);
     this.layers = [];
     for (let b of h) {
       let y = d[b];
@@ -12347,7 +12347,7 @@ var Is = class extends Ge {
       for (let v of y)
         v instanceof Is && this.internalContainerRefs.push(v), this.layers.push(v);
     }
-    this.layersByDepth = d, h = Object.keys(p).map((b) => parseInt(b, 10)).sort(Uc);
+    this.layersByDepth = d, h = Object.keys(p).map((b) => parseInt(b, 10)).sort(Wc);
     let f = this.inputs.slice(), m = [];
     for (let b of h)
       for (let y of p[b]) {
@@ -12368,7 +12368,7 @@ var Is = class extends Ge {
       if (y !== 1)
         throw new ps(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(g));
     }
-    this.outboundNodes = [], this.inboundNodes = [], new Fp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
+    this.outboundNodes = [], this.inboundNodes = [], new Dp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
   }
   assertNotDisposed() {
     if (this._refCount === 0)
@@ -12446,14 +12446,14 @@ var Is = class extends Ge {
       if (a.length > 0)
         throw new U(`${a.length} of ${s} weights are not set: ${a}`);
     }
-    Ob(r);
+    Fb(r);
   }
   updatedConfig() {
     let e = this.getConfig(), t = {};
     return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${t0}`, t.backend = "TensorFlow.js", t;
   }
   toJSON(e, t = true) {
-    let n = xm(this.updatedConfig());
+    let n = vm(this.updatedConfig());
     return t ? JSON.stringify(n) : n;
   }
   call(e, t) {
@@ -12462,7 +12462,7 @@ var Is = class extends Ge {
       let n = new Xr();
       for (let s = 0; s < this.inputs.length; ++s)
         n.add(this.inputs[s], e[s]);
-      return Au(this.outputs, n, t);
+      return _u(this.outputs, n, t);
     });
   }
   computeMask(e, t) {
@@ -12473,7 +12473,7 @@ var Is = class extends Ge {
     });
   }
   computeOutputShape(e) {
-    let t = Sd(e);
+    let t = Id(e);
     if (t.length !== this.inputLayers.length)
       throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);
     let n = {};
@@ -12481,7 +12481,7 @@ var Is = class extends Ge {
       let o = this.inputLayers[i], u = t[i], l = o.name + "_0_0";
       n[l] = u;
     }
-    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(Uc);
+    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(Wc);
     if (s.length > 1)
       for (let i of s) {
         let o = this.nodesByDepth[i];
@@ -12494,7 +12494,7 @@ var Is = class extends Ge {
             let m = u.inboundLayers[f], g = u.nodeIndices[f], b = u.tensorIndices[f], y = `${m.name}_${g}_${b}`, v = n[y];
             c.push(v);
           }
-          let p = l.computeOutputShape(gn(c)), d = Sd(p), h = l.inboundNodes.indexOf(u);
+          let p = l.computeOutputShape(gn(c)), d = Id(p), h = l.inboundNodes.indexOf(u);
           for (let f = 0; f < d.length; f++) {
             let m = `${l.name}_${h}_${f}`;
             n[m] = d[f];
@@ -12519,7 +12519,7 @@ var Is = class extends Ge {
       let u = this.inputs[o], l = e[o], c = t[o];
       n[u.id] = [l, c];
     }
-    let s = Object.keys(this.nodesByDepth).map((o) => parseInt(o, 10)).sort(Uc);
+    let s = Object.keys(this.nodesByDepth).map((o) => parseInt(o, 10)).sort(Wc);
     for (let o of s) {
       let u = this.nodesByDepth[o];
       for (let l of u) {
@@ -12740,7 +12740,7 @@ async function s0(e, t, n, s) {
         return lr(e);
       if (e.shape.length === 2) {
         if (e.shape[1] > 1)
-          return Hu(e, 1);
+          return Gu(e, 1);
         if (e.shape[1] === 1)
           return G(e, [e.shape[0]]);
         throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
@@ -12764,7 +12764,7 @@ var uB = 32;
 function r0(e, t) {
   let n, s, r = t;
   n = r.xs, s = r.ys, w.assert(n != null && s != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);
-  let a = Fx("input", e.inputNames, n), i = Fx("output", e.outputNames, s), o = a[0].shape[0];
+  let a = Dx("input", e.inputNames, n), i = Dx("output", e.outputNames, s), o = a[0].shape[0];
   w.assert(a.length === e.inputs.length, () => `LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`), w.assert(i.length === e.outputs.length, () => `LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);
   for (let u = 0; u < a.length; u++)
     w.assert(a[u].shape[0] === o, () => `Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
@@ -12772,7 +12772,7 @@ function r0(e, t) {
     w.assert(i[u].shape[0] === o, () => `Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);
   return { xs: a, ys: i };
 }
-function Fx(e, t, n) {
+function Dx(e, t, n) {
   if (n instanceof et)
     return [n];
   if (Array.isArray(n))
@@ -12800,7 +12800,7 @@ async function cB(e, t, n) {
   try {
     let r = n.validationData != null, a, i;
     if (r)
-      if (Ox(n.validationData))
+      if (Fx(n.validationData))
         w.assert(n.validationBatches == null || n.validationBatches > 0 && Number.isInteger(n.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);
       else {
         let g = lB(n.validationData);
@@ -12815,7 +12815,7 @@ async function cB(e, t, n) {
       let g = {};
       await d.onEpochBegin(f);
       let b = 0, y = 0;
-      for (s || (m = await t.iterator()); s ? b < n.batchesPerEpoch : true; ) {
+      for (s || (m = await t.iterator()); !s || b < n.batchesPerEpoch; ) {
         let v = await m.next();
         if (s && v.done) {
           console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${b} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch * n.epochs} batches). You may need to use the repeat() function when building your dataset.`);
@@ -12841,7 +12841,7 @@ async function cB(e, t, n) {
         if (s ? b >= n.batchesPerEpoch : v.done) {
           if (r) {
             let x;
-            Ox(n.validationData) ? x = dt(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = dt(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? uB : n.validationBatchSize, verbose: 0 }));
+            Fx(n.validationData) ? x = dt(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = dt(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? uB : n.validationBatchSize, verbose: 0 }));
             for (let k = 0; k < e.metricsNames.length; ++k)
               g[`val_${e.metricsNames[k]}`] = x[k];
           }
@@ -12862,7 +12862,7 @@ function dB(e, t) {
   let n = null;
   return t.batchesPerEpoch != null ? n = t.batchesPerEpoch : Number.isFinite(e.size) && (n = e.size), n;
 }
-function Ox(e) {
+function Fx(e) {
   return typeof e.iterator == "function";
 }
 function pB(e) {
@@ -12875,7 +12875,7 @@ async function hB(e, t, n) {
     throw new Fe("Verbose mode is not implemented yet.");
   w.assert(!s || n.batches > 0 && Number.isInteger(n.batches), () => `Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);
   let i = pB(t) ? t : await t.iterator(), o = 0, u = 0;
-  for (; s ? u < n.batches : true; ) {
+  for (; !s || u < n.batches; ) {
     let l = await i.next();
     if (a = j(() => {
       if (l.value) {
@@ -12902,16 +12902,16 @@ async function hB(e, t, n) {
   }
   return gn(a);
 }
-function wm(e) {
+function xm(e) {
   w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
 }
-function Eu(e, t, n) {
+function Au(e, t, n) {
   return e == null ? [null] : Array.isArray(e) ? e.map((s) => Jr(s, t, n - t)) : Jr(e, t, n - t);
 }
-function Vb(e, t) {
-  return j(() => e == null ? null : Array.isArray(e) ? e.map((n) => Vb(n, t)) : VI(e, t.dtype === "int32" ? t : ce(t, "int32")));
+function Bb(e, t) {
+  return j(() => e == null ? null : Array.isArray(e) ? e.map((n) => Bb(n, t)) : VI(e, t.dtype === "int32" ? t : ce(t, "int32")));
 }
-function km(e, t) {
+function wm(e, t) {
   let n = [], s = 0, r = null;
   for (; s < e; )
     r = s + t, r >= e && (r = e), n.push([s, r]), s = r;
@@ -12935,13 +12935,13 @@ async function fB(e, t, n, s, r, a, i, o, u, l, c, p, d, h, f) {
       if (c === "batch")
         throw new Fe("batch shuffling is not implemneted yet");
       c && w.shuffle(b);
-      let C = Qt(b), T = km(g, r);
+      let C = Qt(b), T = wm(g, r);
       for (let E = 0; E < T.length; ++E) {
         let A = {};
         if (await y.onBatchBegin(E, A), j(() => {
           let P = T[E][0], R = T[E][1], F = Jr(C, P, R - P);
           A.batch = E, A.size = R - P;
-          let $ = Vb(n, F), z = t($);
+          let $ = Bb(n, F), z = t($);
           for (let W = 0; W < s.length; ++W) {
             let q = s[W], K = z[W];
             A[q] = K, Ht(K);
@@ -12970,7 +12970,7 @@ async function mB(e, t, n, s = {}) {
   let r, a, i, o, u, l, c, p, d;
   try {
     let h = s.batchSize == null ? 32 : s.batchSize;
-    wm(h);
+    xm(h);
     let f = false, m = await e.standardizeUserData(t, n, s.sampleWeight, s.classWeight, f, h);
     r = m[0], a = m[1], d = m[2];
     let g = false, b;
@@ -12984,7 +12984,7 @@ async function mB(e, t, n, s = {}) {
     } else if (s.validationSplit != null && s.validationSplit > 0 && s.validationSplit < 1) {
       g = true;
       let A = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
-      c = Eu(r, A, P), i = r, r = Eu(r, 0, A), p = Eu(a, A, P), o = a, a = Eu(a, 0, A), b = c.concat(p);
+      c = Au(r, A, P), i = r, r = Au(r, 0, A), p = Au(a, A, P), o = a, a = Au(a, 0, A), b = c.concat(p);
     } else
       s.validationSteps != null && (g = true);
     let y = r.concat(a).concat(d);
@@ -13003,7 +13003,7 @@ function a0(e) {
   for (let n = 0; n < e.length; ++n) {
     let s = e[n];
     if (s.rank === 1)
-      t.push(Bl(s, 1));
+      t.push(Ll(s, 1));
     else {
       if (s.rank === 0)
         throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
@@ -13044,19 +13044,19 @@ function ls(e, t) {
 function gB(e) {
   return e instanceof et;
 }
-function Im(e) {
+function km(e) {
   return Array.isArray(e);
 }
-function Px(e) {
-  return !gB(e) && !Im(e);
+function Ox(e) {
+  return !gB(e) && !km(e);
 }
-function zx(e, t, n, s = true, r = "") {
+function Px(e, t, n, s = true, r = "") {
   if (t == null || t.length === 0) {
     if (e != null) {
       let i = false;
-      if (Im(e) && e.length > 0)
+      if (km(e) && e.length > 0)
         i = true;
-      else if (Px(e)) {
+      else if (Ox(e)) {
         for (let o in e)
           if (e.hasOwnProperty(o)) {
             i = true;
@@ -13072,14 +13072,14 @@ function zx(e, t, n, s = true, r = "") {
   if (e == null)
     return t.map((i) => null);
   let a;
-  if (Px(e)) {
+  if (Ox(e)) {
     e = e, a = [];
     for (let i of t) {
       if (e[i] == null)
         throw new U(`No data provided for "${i}". Need data for each key in: ${t}`);
       a.push(e[i]);
     }
-  } else if (Im(e)) {
+  } else if (km(e)) {
     if (e = e, e.length !== t.length)
       throw new U(`Error when checking model ${r}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${t.length} Tensor(s), but instead got the following list of Tensor(s): ${e}`);
     a = e;
@@ -13117,11 +13117,11 @@ function bB(e, t, n) {
     throw new U(`Input Tensors should have the same number of samples as target Tensors. Found ${s[0]} input sample(s) and ${r[0]} target sample(s).`);
 }
 function yB(e, t, n) {
-  let s = [fi, Pp, Qu];
+  let s = [fi, Op, Yu];
   for (let r = 0; r < e.length; ++r) {
     let a = e[r], i = t[r], o = n[r];
     if (i != null) {
-      if (i === Qu && a.shape[a.shape.length - 1] === 1)
+      if (i === Yu && a.shape[a.shape.length - 1] === 1)
         throw new U(`You are passing a target array of shape ${a.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);
       if (s.indexOf(i) !== -1) {
         let u = a.shape.slice(1), l = o.slice(1);
@@ -13134,7 +13134,7 @@ function yB(e, t, n) {
     }
   }
 }
-function Mx(e, t, n, s = true, r = "") {
+function zx(e, t, n, s = true, r = "") {
   let a;
   if (Array.isArray(e)) {
     if (e.length !== t.length)
@@ -13208,13 +13208,13 @@ var pr = class extends Is {
         if (this.outputNames.indexOf(a) === -1)
           throw new U(`Unknown entry in loss dictionary: "${a}". Only expected the following keys: ${this.outputNames}`);
       for (let a of this.outputNames)
-        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(Vf(e.loss[a]));
+        e.loss[a] == null && console.warn(`Output "${a}" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${a} during training`), t.push(Bf(e.loss[a]));
     } else if (Array.isArray(e.loss)) {
       if (e.loss.length !== this.outputs.length)
         throw new U(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${e.loss}.`);
-      t = e.loss.map((i) => Vf(i));
+      t = e.loss.map((i) => Bf(i));
     } else {
-      let a = Vf(e.loss);
+      let a = Bf(e.loss);
       this.outputs.forEach((i) => {
         t.push(a);
       });
@@ -13246,11 +13246,11 @@ var pr = class extends Is {
           for (let h of u) {
             if (typeof h == "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(h) !== -1) {
               let m = this.internalOutputShapes[a];
-              m[m.length - 1] === 1 || this.lossFunctions[a] === Pp ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = Mb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = QI) : this.lossFunctions[a] === Td ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = ZI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = JI) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Lb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Bb);
+              m[m.length - 1] === 1 || this.lossFunctions[a] === Op ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = zb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = QI) : this.lossFunctions[a] === Nd ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = ZI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = JI) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Mb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Lb);
               let g;
               ["accuracy", "acc"].indexOf(h) !== -1 ? g = "acc" : ["crossentropy", "ce"].indexOf(h) !== -1 && (g = "ce"), d = p, c = l + g;
             } else
-              d = XL(h), c = l + qc(h);
+              d = XL(h), c = l + Hc(h);
             let f;
             Zr(c, () => {
               f = d;
@@ -13265,7 +13265,7 @@ var pr = class extends Is {
   }
   evaluate(e, t, n = {}) {
     let s = n.batchSize == null ? 32 : n.batchSize;
-    wm(s);
+    xm(s);
     let r = true, a = this.standardizeUserDataXY(e, t, r, s);
     try {
       let i = a[0].concat(a[1]);
@@ -13306,7 +13306,7 @@ var pr = class extends Is {
           throw new U(`No value is provided for the model's input ${o.name}`);
         a.add(o, u);
       }
-    let i = Au(r, a);
+    let i = _u(r, a);
     return n ? i : i[0];
   }
   retrieveSymbolicTensors(e) {
@@ -13334,33 +13334,33 @@ var pr = class extends Is {
       let s = this.checkNumSamples(e);
       if (n)
         throw new Fe("Verbose predictLoop() is not implemented yet.");
-      let r = km(s, t), a = this.outputs.map((i) => []);
+      let r = wm(s, t), a = this.outputs.map((i) => []);
       for (let i = 0; i < r.length; ++i)
         j(() => {
-          let u = r[i][0], l = r[i][1], c = Eu(e, u, l), p = [];
+          let u = r[i][0], l = r[i][1], c = Au(e, u, l), p = [];
           if (Array.isArray(c))
             for (let h = 0; h < c.length; ++h)
               p.push({ key: this.inputs[h], value: c[h] });
           else
             p.push({ key: this.inputs[0], value: c });
           let d = new Xr(p);
-          return Au(this.outputs, d);
+          return _u(this.outputs, d);
         }).forEach((u, l) => a[l].push(u));
       return gn(a.map((i) => Ft(i, 0)));
     });
   }
   predict(e, t = {}) {
     let n = a0(e);
-    Mx(n, this.inputNames, this.feedInputShapes, false);
+    zx(n, this.inputNames, this.feedInputShapes, false);
     try {
       let s = t.batchSize == null ? 32 : t.batchSize;
-      return wm(s), this.predictLoop(n, s);
+      return xm(s), this.predictLoop(n, s);
     } finally {
       ls(n, e);
     }
   }
   predictOnBatch(e) {
-    Mx(e, this.inputNames, this.feedInputShapes, true);
+    zx(e, this.inputNames, this.feedInputShapes, true);
     let t = (Array.isArray(e) ? e[0] : e).shape[0];
     return this.predictLoop(e, t);
   }
@@ -13370,9 +13370,9 @@ var pr = class extends Is {
     let r = [];
     for (let a = 0; a < this.feedOutputShapes.length; ++a) {
       let i = this.feedOutputShapes[a];
-      this.feedLossFns[a] === Td ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
+      this.feedLossFns[a] === Nd ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
     }
-    if (e = zx(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = zx(t, this.feedOutputNames, r, false, "target"), bB(e, t, null), yB(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
+    if (e = Px(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Px(t, this.feedOutputNames, r, false, "target"), bB(e, t, null), yB(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
       throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);
     return [e, t];
   }
@@ -13397,9 +13397,9 @@ var pr = class extends Is {
       if (r != null)
         throw new Fe("steps mode in testLoop() is not implemented yet");
       {
-        let o = km(a, n), u = Qt(ys(0, a));
+        let o = wm(a, n), u = Qt(ys(0, a));
         for (let l = 0; l < o.length; ++l) {
-          let c = o[l][0], p = o[l][1], d = Jr(u, c, p - c), h = Vb(t, d), f = e(h);
+          let c = o[l][0], p = o[l][1], d = Jr(u, c, p - c), h = Bb(t, d), f = e(h);
           if (l === 0)
             for (let m = 0; m < f.length; ++m)
               i.push(Ie(0));
@@ -13418,7 +13418,7 @@ var pr = class extends Is {
     let e = this.metricsNames, t = [];
     for (let n = 0; n < e.length; ++n) {
       let s = e[n], r = s;
-      kx(e, s) > 1 && (r += `_${kx(e.slice(0, n), s)}`), t.push(r);
+      wx(e, s) > 1 && (r += `_${wx(e.slice(0, n), s)}`), t.push(r);
     }
     return t;
   }
@@ -13428,7 +13428,7 @@ var pr = class extends Is {
         let c = [];
         for (let f = 0; f < this.inputs.length; ++f)
           c.push({ key: this.inputs[f], value: n[f] });
-        let p = new Xr(c), d = Au(this.outputs, p, { training: true }), h;
+        let p = new Xr(c), d = _u(this.outputs, p, { training: true }), h;
         for (let f = 0; f < this.lossFunctions.length; ++f) {
           let g = this.lossFunctions[f](s[f], d[f]);
           r[f] != null && (g = oB(g, r[f]));
@@ -13457,7 +13457,7 @@ var pr = class extends Is {
       let t = [], n, s = e.slice(0, this.inputs.length), r = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), a = [];
       for (let u = 0; u < this.inputs.length; ++u)
         a.push({ key: this.inputs[u], value: s[u] });
-      let i = new Xr(a), o = Au(this.outputs, i);
+      let i = new Xr(a), o = _u(this.outputs, i);
       for (let u = 0; u < this.lossFunctions.length; ++u) {
         let l = this.lossFunctions[u], c = It(l(r[u], o[u]));
         u === 0 ? n = c : n = ie(n, c), t.push(n);
@@ -13504,8 +13504,8 @@ var pr = class extends Is {
   dispose() {
     let e = super.dispose();
     if (e.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {
-      let t = cm().numTensors;
-      this.optimizer_.dispose(), e.numDisposedVariables += t - cm().numTensors;
+      let t = lm().numTensors;
+      this.optimizer_.dispose(), e.numDisposedVariables += t - lm().numTensors;
     }
     return e;
   }
@@ -13532,13 +13532,13 @@ var pr = class extends Is {
   }
   getMetricIdentifiers() {
     if (typeof this.metrics == "string" || typeof this.metrics == "function")
-      return [Ws(qc(this.metrics))];
+      return [Ws(Hc(this.metrics))];
     if (Array.isArray(this.metrics))
-      return this.metrics.map((e) => Ws(qc(e)));
+      return this.metrics.map((e) => Ws(Hc(e)));
     {
       let e = {};
       for (let t in this.metrics)
-        e[t] = Ws(qc(this.metrics[t]));
+        e[t] = Ws(Hc(this.metrics[t]));
       return e;
     }
   }
@@ -13552,7 +13552,7 @@ var pr = class extends Is {
       throw new Error("Loading loss_weights is not supported yet.");
     if (e.sample_weight_mode != null)
       throw new Error("Loading sample_weight_mode is not supported yet.");
-    let t = Zu(e.optimizer_config), n = fs(t), s;
+    let t = Qu(e.optimizer_config), n = fs(t), s;
     if (typeof e.loss == "string")
       s = qr(e.loss);
     else if (Array.isArray(e.loss))
@@ -13589,10 +13589,10 @@ var pr = class extends Is {
       let u = "optimizer", { data: l, specs: c } = await _n.encodeWeights(await this.optimizer.getWeights(), u);
       n.specs.push(...c), n.data = _n.concatenateArrayBuffers([n.data, l]);
     }
-    return this.userDefinedMetadata != null && (Ex(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
+    return this.userDefinedMetadata != null && (Ax(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
   }
   setUserDefinedMetadata(e) {
-    Ex(e, this.name), this.userDefinedMetadata = e;
+    Ax(e, this.name), this.userDefinedMetadata = e;
   }
   getUserDefinedMetadata() {
     return this.userDefinedMetadata;
@@ -13608,7 +13608,7 @@ async function wB(e, t) {
   "modelTopology" in e || (e = { modelTopology: e }), e = e;
   let n = e.modelTopology;
   n.model_config != null && (n = n.model_config);
-  let s = Zu(n), r = fs(s, t);
+  let s = Qu(n), r = fs(s, t);
   if (e.weightsManifest != null) {
     let a = await _n.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((o) => o.originalName)), i = {};
     for (let o of r.weights)
@@ -13633,7 +13633,7 @@ async function IB(e, t, n) {
     throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
   let s = await e.load(), r = s.modelTopology;
   r.model_config != null && (r = r.model_config);
-  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = fs(Zu(r), t, i), u = s.trainingConfig;
+  let a = n.strict == null ? true : n.strict, i = s.weightData != null && s.weightSpecs != null && a, o = fs(Qu(r), t, i), u = s.trainingConfig;
   if (u != null && o.loadTrainingConfig(u), s.userDefinedMetadata != null && o.setUserDefinedMetadata(s.userDefinedMetadata), s.weightData != null) {
     if (s.weightSpecs == null)
       throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
@@ -13648,10 +13648,10 @@ function SB(e, t) {
     a.group === "optimizer" ? r.push({ name: a.name, tensor: n[a.name] }) : s[a.name] = n[a.name];
   }), { modelWeights: s, optimizerWeights: r };
 }
-var Sm = class extends pr {
+var Im = class extends pr {
   constructor(e) {
     super({ inputs: [], outputs: [] });
-    if (e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Dp("sequential_"), e.layers != null)
+    if (e = e || {}, this.trainable = true, this.built = false, this.name = e.name != null ? e.name : Rp("sequential_"), e.layers != null)
       for (let t of e.layers)
         this.add(t);
   }
@@ -13660,7 +13660,7 @@ var Sm = class extends pr {
       throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`);
   }
   add(e) {
-    let t = e instanceof Sm || e instanceof pr, n;
+    let t = e instanceof Im || e instanceof pr, n;
     if (t) {
       if (n = e, n.outputs.length !== 1)
         throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
@@ -13683,7 +13683,7 @@ var Sm = class extends pr {
           throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
         this.checkShape(e), this.outputs = [e.inboundNodes[0].outputTensors[0]], this.inputs = GI(this.outputs[0]);
       }
-      this.inboundNodes = [], new Fp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: pa(null, this.inputs.length), outputMasks: [null], inputShapes: this.inputs.map((s) => s.shape), outputShapes: this.outputs[0].shape });
+      this.inboundNodes = [], new Dp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: pa(null, this.inputs.length), outputMasks: [null], inputShapes: this.inputs.map((s) => s.shape), outputShapes: this.outputs[0].shape });
     } else {
       let s = e.apply(this.outputs[0]);
       if (Array.isArray(s))
@@ -13766,7 +13766,7 @@ var Sm = class extends pr {
     } else
       w.assert(t.layers != null, () => "When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."), r = t.layers, delete t.layers, a = t;
     let i = new e(a);
-    if (!(i instanceof Sm))
+    if (!(i instanceof Im))
       throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);
     for (let o of r) {
       let l = fs(o, void 0, s);
@@ -13793,14 +13793,14 @@ var Sm = class extends pr {
     return { name: this.name, layers: e };
   }
 };
-var Wb = Sm;
-Wb.className = "Sequential";
-ae.registerClass(Wb);
+var Vb = Im;
+Vb.className = "Sequential";
+ae.registerClass(Vb);
 function cpe(e) {
   return new pr(e);
 }
 function dpe(e) {
-  return new Wb(e);
+  return new Vb(e);
 }
 function ppe(e, t) {
   return t == null && (t = {}), kB(e, t);
@@ -13809,7 +13809,7 @@ function CB(e) {
   return HI(e);
 }
 function hpe(e, t) {
-  Pb.registerCallbackConstructor(e, t);
+  Ob.registerCallbackConstructor(e, t);
 }
 var wn = class extends ae.Serializable {
   getConfig() {
@@ -13839,7 +13839,7 @@ l0.className = "relu";
 ae.registerClass(l0);
 var c0 = class extends wn {
   apply(e) {
-    return j(() => bp(6, Xs(e)));
+    return j(() => gp(6, Xs(e)));
   }
 };
 c0.className = "relu6";
@@ -13867,7 +13867,7 @@ h0.className = "hardSigmoid";
 ae.registerClass(h0);
 var f0 = class extends wn {
   apply(e) {
-    return Pl(e);
+    return Ol(e);
   }
 };
 f0.className = "softplus";
@@ -13881,18 +13881,18 @@ m0.className = "softsign";
 ae.registerClass(m0);
 var g0 = class extends wn {
   apply(e) {
-    return qu(e);
+    return Hu(e);
   }
 };
 g0.className = "tanh";
 ae.registerClass(g0);
-var Ub = class extends wn {
+var Wb = class extends wn {
   apply(e, t = -1) {
-    return ob(e, t);
+    return ib(e, t);
   }
 };
-Ub.className = "softmax";
-ae.registerClass(Ub);
+Wb.className = "softmax";
+ae.registerClass(Wb);
 var b0 = class extends wn {
   apply(e, t = -1) {
     return eI(e, t);
@@ -13909,7 +13909,7 @@ y0.className = "swish";
 ae.registerClass(y0);
 var v0 = class extends wn {
   apply(e) {
-    return j(() => V(e, qu(Pl(e))));
+    return j(() => V(e, Hu(Ol(e))));
   }
 };
 v0.className = "mish";
@@ -13917,35 +13917,35 @@ ae.registerClass(v0);
 function br(e) {
   return e.getClassName();
 }
-function Uf(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "activation");
+function Wf(e, t = {}) {
+  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "activation");
 }
 function yr(e) {
   if (e == null) {
     let t = {};
-    return t.className = "linear", t.config = {}, Uf(t);
+    return t.className = "linear", t.config = {}, Wf(t);
   }
   if (typeof e == "string") {
     let t = {};
-    return t.className = e, t.config = {}, Uf(t);
+    return t.className = e, t.config = {}, Wf(t);
   } else
-    return e instanceof wn ? e : Uf(e);
+    return e instanceof wn ? e : Wf(e);
 }
-function Gb(e) {
+function Ub(e) {
   if (e != null && typeof e != "object")
     throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`);
 }
 var x0 = class extends ae.Serializable {
 };
-var Ul = class extends x0 {
+var Wl = class extends x0 {
   constructor(e) {
     super();
-    Gb(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
+    Ub(e), this.l1 = e == null || e.l1 == null ? 0.01 : e.l1, this.l2 = e == null || e.l2 == null ? 0.01 : e.l2, this.hasL1 = this.l1 !== 0, this.hasL2 = this.l2 !== 0;
   }
   apply(e) {
     return j(() => {
       let t = $t([1]);
-      return this.hasL1 && (t = ie(t, ye(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ye(V(this.l2, Vl(e))))), G(t, []);
+      return this.hasL1 && (t = ie(t, ye(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ye(V(this.l2, Bl(e))))), G(t, []);
     });
   }
   getConfig() {
@@ -13955,31 +13955,31 @@ var Ul = class extends x0 {
     return new e({ l1: t.l1, l2: t.l2 });
   }
 };
-Ul.className = "L1L2";
-ae.registerClass(Ul);
+Wl.className = "L1L2";
+ae.registerClass(Wl);
 function NB(e) {
-  return Gb(e), new Ul({ l1: e != null ? e.l1 : null, l2: 0 });
+  return Ub(e), new Wl({ l1: e != null ? e.l1 : null, l2: 0 });
 }
 function TB(e) {
-  return Gb(e), new Ul({ l2: e != null ? e.l2 : null, l1: 0 });
+  return Ub(e), new Wl({ l2: e != null ? e.l2 : null, l1: 0 });
 }
-var Lx = { l1l2: "L1L2" };
+var Mx = { l1l2: "L1L2" };
 function at(e) {
-  return xb(e);
+  return vb(e);
 }
-function Bx(e, t = {}) {
-  return Ml(e, ae.SerializationMap.getMap().classNameMap, t, "regularizer");
+function Lx(e, t = {}) {
+  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "regularizer");
 }
 function ft(e) {
   if (e == null)
     return null;
   if (typeof e == "string") {
-    let n = { className: e in Lx ? Lx[e] : e, config: {} };
-    return Bx(n);
+    let n = { className: e in Mx ? Mx[e] : e, config: {} };
+    return Lx(n);
   } else
-    return e instanceof x0 ? e : Bx(e);
+    return e instanceof x0 ? e : Lx(e);
 }
-var Hb = class extends Ge {
+var Gb = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
     this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
@@ -13997,16 +13997,16 @@ var Hb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Hb.className = "ReLU";
-ae.registerClass(Hb);
-var qb = class extends Ge {
+Gb.className = "ReLU";
+ae.registerClass(Gb);
+var Hb = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
     this.DEFAULT_ALPHA = 0.3, e == null && (e = {}), this.alpha = e.alpha == null ? this.DEFAULT_ALPHA : e.alpha;
   }
   call(e, t) {
     let n = Oe(e);
-    return Kg(n, this.alpha);
+    return jg(n, this.alpha);
   }
   computeOutputShape(e) {
     return e;
@@ -14016,9 +14016,9 @@ var qb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-qb.className = "LeakyReLU";
-ae.registerClass(qb);
-var jb = class extends Ge {
+Hb.className = "LeakyReLU";
+ae.registerClass(Hb);
+var qb = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
     if (this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ht(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = ft(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
@@ -14044,16 +14044,16 @@ var jb = class extends Ge {
     this.inputSpec = [new Dt({ ndim: e.length, axes: n })], this.built = true;
   }
   call(e, t) {
-    return e = Oe(e), nb(e, this.alpha.read());
+    return e = Oe(e), tb(e, this.alpha.read());
   }
   getConfig() {
     let e = { alphaInitializer: yt(this.alphaInitializer), alphaRegularizer: at(this.alphaRegularizer), alphaConstraint: Ot(this.alphaConstraint), sharedAxes: this.sharedAxes }, t = super.getConfig();
     return Object.assign(e, t), e;
   }
 };
-jb.className = "PReLU";
-ae.registerClass(jb);
-var Kb = class extends Ge {
+qb.className = "PReLU";
+ae.registerClass(qb);
+var jb = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
     if (this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
@@ -14062,7 +14062,7 @@ var Kb = class extends Ge {
   }
   call(e, t) {
     let n = Oe(e);
-    return fp(n);
+    return hp(n);
   }
   computeOutputShape(e) {
     return e;
@@ -14072,9 +14072,9 @@ var Kb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Kb.className = "ELU";
-ae.registerClass(Kb);
-var Xb = class extends Ge {
+jb.className = "ELU";
+ae.registerClass(jb);
+var Kb = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
     this.DEFAULT_THETA = 1, e == null && (e = {}), this.theta = e.theta == null ? this.DEFAULT_THETA : e.theta;
@@ -14091,12 +14091,12 @@ var Xb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Xb.className = "ThresholdedReLU";
-ae.registerClass(Xb);
-var Yb = class extends Ge {
+Kb.className = "ThresholdedReLU";
+ae.registerClass(Kb);
+var Xb = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
-    this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Ub().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
+    this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Wb().apply, this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis;
   }
   call(e, t) {
     let n = Oe(e);
@@ -14110,9 +14110,9 @@ var Yb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Yb.className = "Softmax";
-ae.registerClass(Yb);
-function Xi(e, t, n) {
+Xb.className = "Softmax";
+ae.registerClass(Xb);
+function Ki(e, t, n) {
   if (typeof e == "number")
     return pa(e, t);
   if (e.length !== t)
@@ -14141,7 +14141,7 @@ function Ns(e, t, n, s) {
     throw new U(`Unsupport padding mode: ${s}.`);
   return e;
 }
-function Qb(e, t) {
+function Yb(e, t) {
   return j(() => (St(t), t === "channelsFirst" ? qe(e, [0, 2, 3, 1]) : e));
 }
 function w0(e, t) {
@@ -14161,13 +14161,13 @@ function $B(e, t, n, s = 1, r = "valid", a, i = 1) {
     return n != null && (o = ws(o, n)), o;
   });
 }
-function Vx(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
+function Bx(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
   return j(() => {
     if (a == null && (a = bs()), St(a), e.rank !== 3 && e.rank !== 4)
       throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);
     if (t.rank !== 3 && t.rank !== 4)
       throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);
-    let u = Qb(e, a);
+    let u = Yb(e, a);
     if (r === "causal")
       throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
     return u = da.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: i, dataFormat: "NHWC", bias: n, activation: o }), a === "channelsFirst" && (u = qe(u, [0, 3, 1, 2])), u;
@@ -14185,12 +14185,12 @@ function _B(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
     return o = Kk(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ws(o, n)), a === "channelsFirst" && (o = qe(o, [0, 4, 1, 2, 3])), o;
   });
 }
-var Zb = class extends Ge {
+var Qb = class extends Ge {
   constructor(e, t) {
     super(t);
-    if (this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", Zb.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
+    if (this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", Qb.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
       throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
-    if (this.kernelSize = Xi(t.kernelSize, e, "kernelSize"), this.strides = Xi(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Un(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, St(this.dataFormat), this.activation = yr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Xi(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
+    if (this.kernelSize = Ki(t.kernelSize, e, "kernelSize"), this.strides = Ki(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Un(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, St(this.dataFormat), this.activation = yr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Ki(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
       throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);
     if (this.rank === 2) {
       if (typeof this.dilationRate == "number")
@@ -14205,7 +14205,7 @@ var Zb = class extends Ge {
     }
   }
   static verifyArgs(e) {
-    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !wb(e.kernelSize, "number", 1, 3))
+    if (Cs("kernelSize" in e, "required key 'kernelSize' not in config"), typeof e.kernelSize != "number" && !xb(e.kernelSize, "number", 1, 3))
       throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`);
   }
   getConfig() {
@@ -14213,10 +14213,10 @@ var Zb = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var Gl = class extends Zb {
+var Ul = class extends Qb {
   constructor(e, t) {
     super(e, t);
-    this.kernel = null, Gl.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
+    this.kernel = null, Ul.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
   }
   build(e) {
     e = nt(e);
@@ -14231,12 +14231,12 @@ var Gl = class extends Zb {
       e = Oe(e);
       let n, s = this.bias == null ? null : this.bias.read(), r = PI(this.activation.getClassName());
       if (r != null && this.rank === 2)
-        n = Vx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
+        n = Bx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
       else {
         if (this.rank === 1)
           n = $B(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
         else if (this.rank === 2)
-          n = Vx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
+          n = Bx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
         else if (this.rank === 3)
           n = _B(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
         else
@@ -14265,7 +14265,7 @@ var Gl = class extends Zb {
       throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`);
   }
 };
-var k0 = class extends Gl {
+var k0 = class extends Ul {
   constructor(e) {
     super(2, e);
     k0.verifyArgs(e);
@@ -14275,14 +14275,14 @@ var k0 = class extends Gl {
     return delete e.rank, e;
   }
   static verifyArgs(e) {
-    if (typeof e.kernelSize != "number" && !wb(e.kernelSize, "number", 1, 2))
+    if (typeof e.kernelSize != "number" && !xb(e.kernelSize, "number", 1, 2))
       throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var zp = k0;
-zp.className = "Conv2D";
-ae.registerClass(zp);
-var I0 = class extends Gl {
+var Pp = k0;
+Pp.className = "Conv2D";
+ae.registerClass(Pp);
+var I0 = class extends Ul {
   constructor(e) {
     super(3, e);
     I0.verifyArgs(e);
@@ -14296,10 +14296,10 @@ var I0 = class extends Gl {
       throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var Mp = I0;
-Mp.className = "Conv3D";
-ae.registerClass(Mp);
-var Jb = class extends zp {
+var zp = I0;
+zp.className = "Conv3D";
+ae.registerClass(zp);
+var Zb = class extends Pp {
   constructor(e) {
     super(e);
     if (this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
@@ -14339,9 +14339,9 @@ var Jb = class extends zp {
     return delete e.dilationRate, e;
   }
 };
-Jb.className = "Conv2DTranspose";
-ae.registerClass(Jb);
-var ey = class extends Mp {
+Zb.className = "Conv2DTranspose";
+ae.registerClass(Zb);
+var Jb = class extends zp {
   constructor(e) {
     super(e);
     if (this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
@@ -14381,9 +14381,9 @@ var ey = class extends Mp {
     return delete e.dilationRate, e;
   }
 };
-ey.className = "Conv3DTranspose";
-ae.registerClass(ey);
-var S0 = class extends Gl {
+Jb.className = "Conv3DTranspose";
+ae.registerClass(Jb);
+var S0 = class extends Ul {
   constructor(e, t) {
     super(e, t);
     if (this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
@@ -14422,14 +14422,14 @@ var S0 = class extends Gl {
   }
 };
 S0.className = "SeparableConv";
-var ty = class extends S0 {
+var ey = class extends S0 {
   constructor(e) {
     super(2, e);
   }
 };
-ty.className = "SeparableConv2D";
-ae.registerClass(ty);
-var C0 = class extends Gl {
+ey.className = "SeparableConv2D";
+ae.registerClass(ey);
+var C0 = class extends Ul {
   constructor(e) {
     super(1, e);
     C0.verifyArgs(e), this.inputSpec = [{ ndim: 3 }];
@@ -14439,14 +14439,14 @@ var C0 = class extends Gl {
     return delete e.rank, delete e.dataFormat, e;
   }
   static verifyArgs(e) {
-    if (typeof e.kernelSize != "number" && !wb(e.kernelSize, "number", 1, 1))
+    if (typeof e.kernelSize != "number" && !xb(e.kernelSize, "number", 1, 1))
       throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`);
   }
 };
-var ny = C0;
-ny.className = "Conv1D";
-ae.registerClass(ny);
-var sy = class extends Ge {
+var ty = C0;
+ty.className = "Conv1D";
+ae.registerClass(ty);
+var ny = class extends Ge {
   constructor(e) {
     super(e);
     typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
@@ -14457,11 +14457,11 @@ var sy = class extends Ge {
   call(e, t) {
     return j(() => {
       if (e = Oe(e), this.dataFormat === "channelsLast") {
-        let n = Gc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
-        return Gc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
+        let n = Uc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
+        return Uc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
       } else {
-        let n = Gc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
-        return Gc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
+        let n = Uc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
+        return Uc(n, this.cropping[1][0], e.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);
       }
     });
   }
@@ -14470,9 +14470,9 @@ var sy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-sy.className = "Cropping2D";
-ae.registerClass(sy);
-var ry = class extends Ge {
+ny.className = "Cropping2D";
+ae.registerClass(ny);
+var sy = class extends Ge {
   constructor(e) {
     super(e);
     this.DEFAULT_SIZE = [2, 2], this.inputSpec = [{ ndim: 4 }], this.size = e.size == null ? this.DEFAULT_SIZE : e.size, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, St(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, WM(this.interpolation);
@@ -14504,20 +14504,20 @@ var ry = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-ry.className = "UpSampling2D";
-ae.registerClass(ry);
+sy.className = "UpSampling2D";
+ae.registerClass(sy);
 function AB(e, t, n = [1, 1], s = "valid", r, a) {
   return j(() => {
     r == null && (r = bs()), St(r);
-    let i = Qb(e, r);
+    let i = Yb(e, r);
     if (e.rank !== 4)
       throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);
     if (t.rank !== 4)
       throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);
-    return i = hp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
+    return i = pp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
   });
 }
-var ay = class extends Zb {
+var ry = class extends Qb {
   constructor(e) {
     super(2, e);
     this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ht(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = ft(e.depthwiseRegularizer);
@@ -14548,8 +14548,8 @@ var ay = class extends Zb {
     return e.depthMultiplier = this.depthMultiplier, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.depthwiseRegularizer = at(this.depthwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseRegularizer), e;
   }
 };
-ay.className = "DepthwiseConv2D";
-ae.registerClass(ay);
+ry.className = "DepthwiseConv2D";
+ae.registerClass(ry);
 function N0(e, t, n, s) {
   if (Array.isArray(e)) {
     if (t != null || n != null)
@@ -14595,7 +14595,7 @@ var $0 = class extends Ge {
     let t;
     if (e.cell == null)
       throw new U("cell property is missing for the constructor of RNN.");
-    if (Array.isArray(e.cell) ? t = new Vp({ cells: e.cell }) : t = e.cell, t.stateSize == null)
+    if (Array.isArray(e.cell) ? t = new Bp({ cells: e.cell }) : t = e.cell, t.stateSize == null)
       throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
     this.cell = t, this.returnSequences = e.returnSequences == null ? false : e.returnSequences, this.returnState = e.returnState == null ? false : e.returnState, this.goBackwards = e.goBackwards == null ? false : e.goBackwards, this._stateful = e.stateful == null ? false : e.stateful, this.unroll = e.unroll == null ? false : e.unroll, this.supportsMasking = true, this.inputSpec = [new Dt({ ndim: 3 })], this.stateSpec = null, this.states_ = null, this.numConstants = null, this.keptStates = [];
   }
@@ -14610,7 +14610,7 @@ var $0 = class extends Ge {
     this.states_ = e;
   }
   computeOutputShape(e) {
-    bm(e) && (e = e[0]), e = e;
+    gm(e) && (e = e[0]), e = e;
     let t = this.cell.stateSize;
     Array.isArray(t) || (t = [t]);
     let n = t[0], s;
@@ -14648,7 +14648,7 @@ var $0 = class extends Ge {
   build(e) {
     if (this.numConstants != null)
       throw new Fe("Constants support is not implemented in RNN yet.");
-    bm(e) && (e = e[0]), e = e;
+    gm(e) && (e = e[0]), e = e;
     let n = this.stateful ? e[0] : null, s = e.slice(2);
     this.inputSpec[0] = new Dt({ shape: [n, null, ...s] });
     let r = [e[0]].concat(e.slice(2));
@@ -14726,7 +14726,7 @@ var $0 = class extends Ge {
   getInitialState(e) {
     return j(() => {
       let t = $t(e.shape);
-      return t = ye(t, [1, 2]), t = Bl(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? mm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [mm(t, [1, this.cell.stateSize])] : [t];
+      return t = ye(t, [1, 2]), t = Ll(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? fm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [fm(t, [1, this.cell.stateSize])] : [t];
     });
   }
   get trainableWeights() {
@@ -14752,12 +14752,12 @@ var $0 = class extends Ge {
 var _r = $0;
 _r.className = "RNN";
 ae.registerClass(_r);
-var Hl = class extends Ge {
+var Gl = class extends Ge {
 };
-var Lp = class extends Hl {
+var Mp = class extends Gl {
   constructor(e) {
     super(e);
-    this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Qi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Qi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e), this.kernel = this.addWeight("kernel", [e[e.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
@@ -14781,11 +14781,11 @@ var Lp = class extends Hl {
     return { ...e, ...t };
   }
 };
-Lp.className = "SimpleRNNCell";
-ae.registerClass(Lp);
-var iy = class extends _r {
+Mp.className = "SimpleRNNCell";
+ae.registerClass(Mp);
+var ay = class extends _r {
   constructor(e) {
-    e.cell = new Lp(e);
+    e.cell = new Mp(e);
     super(e);
   }
   call(e, t) {
@@ -14799,14 +14799,14 @@ var iy = class extends _r {
     return new e(t);
   }
 };
-iy.className = "SimpleRNN";
-ae.registerClass(iy);
-var Bp = class extends Hl {
+ay.className = "SimpleRNN";
+ae.registerClass(ay);
+var Lp = class extends Gl {
   constructor(e) {
     super(e);
     if (this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
       throw new U("GRUCell does not support reset_after parameter set to true.");
-    this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
+    this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Qi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Qi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     e = nt(e);
@@ -14836,11 +14836,11 @@ var Bp = class extends Hl {
     return { ...e, ...t };
   }
 };
-Bp.className = "GRUCell";
-ae.registerClass(Bp);
-var oy = class extends _r {
+Lp.className = "GRUCell";
+ae.registerClass(Lp);
+var iy = class extends _r {
   constructor(e) {
-    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Bp(e);
+    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Lp(e);
     super(e);
   }
   call(e, t) {
@@ -14854,12 +14854,12 @@ var oy = class extends _r {
     return t.implmentation === 0 && (t.implementation = 1), new e(t);
   }
 };
-oy.className = "GRU";
-ae.registerClass(oy);
-var ql = class extends Hl {
+iy.className = "GRU";
+ae.registerClass(iy);
+var Hl = class extends Gl {
   constructor(e) {
     super(e);
-    this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Zi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Zi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
+    this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Qi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Qi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
   }
   build(e) {
     var t;
@@ -14872,8 +14872,8 @@ var ql = class extends Hl {
         let r = this.biasInitializer, a = this.units;
         s = new (t = class extends es {
           apply(i, o) {
-            let u = r.apply([a]), l = new Np().apply([a]), c = r.apply([a * 2]);
-            return Nx(Nx(u, l), c);
+            let u = r.apply([a]), l = new Cp().apply([a]), c = r.apply([a * 2]);
+            return Cx(Cx(u, l), c);
           }
         }, t.className = "CustomInit", t)();
       } else
@@ -14905,11 +14905,11 @@ var ql = class extends Hl {
     return { ...e, ...t };
   }
 };
-ql.className = "LSTMCell";
-ae.registerClass(ql);
-var uy = class extends _r {
+Hl.className = "LSTMCell";
+ae.registerClass(Hl);
+var oy = class extends _r {
   constructor(e) {
-    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new ql(e);
+    e.implementation === 0 && console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."), e.cell = new Hl(e);
     super(e);
   }
   call(e, t) {
@@ -14923,9 +14923,9 @@ var uy = class extends _r {
     return t.implmentation === 0 && (t.implementation = 1), new e(t);
   }
 };
-uy.className = "LSTM";
-ae.registerClass(uy);
-var Vp = class extends Hl {
+oy.className = "LSTM";
+ae.registerClass(oy);
+var Bp = class extends Gl {
   constructor(e) {
     super(e);
     this.cells = e.cells;
@@ -14955,7 +14955,7 @@ var Vp = class extends Hl {
     });
   }
   build(e) {
-    bm(e) && (e = e[0]), e = e;
+    gm(e) && (e = e[0]), e = e;
     let t;
     this.cells.forEach((n, s) => {
       Zr(`RNNCell_${s}`, () => {
@@ -14997,7 +14997,7 @@ var Vp = class extends Hl {
     let e = [];
     for (let t of this.cells)
       e.push(...t.weights);
-    return ym(e);
+    return bm(e);
   }
   setWeights(e) {
     let t = [];
@@ -15006,13 +15006,13 @@ var Vp = class extends Hl {
       for (let a = 0; a < n.weights.length; ++a)
         t.push([n.weights[a], r[a]]);
     }
-    Ob(t);
+    Fb(t);
   }
 };
-Vp.className = "StackedRNNCells";
-ae.registerClass(Vp);
+Bp.className = "StackedRNNCells";
+ae.registerClass(Bp);
 function vr(e) {
-  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : WI(t(), n), o = () => Wl(i, t, s);
+  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : WI(t(), n), o = () => Vl(i, t, s);
   return !r || r <= 1 ? Ht(o().clone()) : Array(r).fill(void 0).map(o).map((l) => Ht(l.clone()));
 }
 var _0 = class extends _r {
@@ -15073,11 +15073,11 @@ var _0 = class extends _r {
   }
 };
 _0.className = "ConvRNN2D";
-var Wp = class extends ql {
+var Vp = class extends Hl {
   constructor(e) {
     let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: i } = e;
     super({ ...e, units: t });
-    this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Xi(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Xi(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Un(this.padding), this.dataFormat = a || "channelsLast", St(this.dataFormat), this.dilationRate = Xi(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
+    this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Ki(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Ki(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Un(this.padding), this.dataFormat = a || "channelsLast", St(this.dataFormat), this.dilationRate = Ki(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
   }
   build(e) {
     var t;
@@ -15095,7 +15095,7 @@ var Wp = class extends ql {
         o = new (t = class extends es {
           apply(c, p) {
             let d = u.apply([l]), h = zn([l]), f = u.apply([l * 2]);
-            return Tb([d, h, f]);
+            return Nb([d, h, f]);
           }
         }, t.className = "CustomInit", t)();
       } else
@@ -15132,20 +15132,20 @@ var Wp = class extends ql {
     return ua(e, t, 1, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
   }
 };
-Wp.className = "ConvLSTM2DCell";
-ae.registerClass(Wp);
-var ly = class extends _0 {
+Vp.className = "ConvLSTM2DCell";
+ae.registerClass(Vp);
+var uy = class extends _0 {
   constructor(e) {
-    let t = new Wp(e);
+    let t = new Vp(e);
     super({ ...e, cell: t });
   }
   static fromConfig(e, t) {
     return new e(t);
   }
 };
-ly.className = "ConvLSTM2D";
-ae.registerClass(ly);
-var Up = class extends Ge {
+uy.className = "ConvLSTM2D";
+ae.registerClass(uy);
+var Wp = class extends Ge {
   constructor(e) {
     super(e);
     this.rate = Math.max(Math.min(e.rate, 1), 0), this.noiseShape = e.noiseShape, this.seed = e.seed, this.supportsMasking = true;
@@ -15164,7 +15164,7 @@ var Up = class extends Ge {
       let n = Oe(e);
       if (0 < this.rate && this.rate < 1) {
         let s = t.training == null ? false : t.training, r = this.getNoiseShape(n);
-        return Wl(() => WI(n, this.rate, r, this.seed), () => n, s);
+        return Vl(() => WI(n, this.rate, r, this.seed), () => n, s);
       }
       return e;
     });
@@ -15177,9 +15177,9 @@ var Up = class extends Ge {
     return super.dispose();
   }
 };
-Up.className = "Dropout";
-ae.registerClass(Up);
-var cy = class extends Up {
+Wp.className = "Dropout";
+ae.registerClass(Wp);
+var ly = class extends Wp {
   constructor(e) {
     super(e);
     this.inputSpec = [{ ndim: 3 }];
@@ -15189,9 +15189,9 @@ var cy = class extends Up {
     return [t[0], 1, t[2]];
   }
 };
-cy.className = "SpatialDropout1D";
-ae.registerClass(cy);
-var dy = class extends Ge {
+ly.className = "SpatialDropout1D";
+ae.registerClass(ly);
+var cy = class extends Ge {
   constructor(e) {
     super(e);
     if (this.activation = null, this.useBias = true, this.kernel = null, this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.batchInputShape == null && e.inputShape == null && e.inputDim != null) {
@@ -15222,9 +15222,9 @@ var dy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-dy.className = "Dense";
-ae.registerClass(dy);
-var py = class extends Ge {
+cy.className = "Dense";
+ae.registerClass(cy);
+var dy = class extends Ge {
   constructor(e) {
     e = e || {};
     super(e);
@@ -15257,9 +15257,9 @@ var py = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-py.className = "Flatten";
-ae.registerClass(py);
-var hy = class extends Ge {
+dy.className = "Flatten";
+ae.registerClass(dy);
+var py = class extends Ge {
   constructor(e) {
     super(e);
     this.supportsMasking = true, this.activation = yr(e.activation);
@@ -15276,9 +15276,9 @@ var hy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-hy.className = "Activation";
-ae.registerClass(hy);
-var fy = class extends Ge {
+py.className = "Activation";
+ae.registerClass(py);
+var hy = class extends Ge {
   constructor(e) {
     super(e);
     this.n = e.n, this.inputSpec = [{ ndim: 2 }];
@@ -15294,9 +15294,9 @@ var fy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-fy.className = "RepeatVector";
-ae.registerClass(fy);
-var my = class extends Ge {
+hy.className = "RepeatVector";
+ae.registerClass(hy);
+var fy = class extends Ge {
   constructor(e) {
     super(e);
     this.targetShape = e.targetShape;
@@ -15348,9 +15348,9 @@ var my = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-my.className = "Reshape";
-ae.registerClass(my);
-var gy = class extends Ge {
+fy.className = "Reshape";
+ae.registerClass(fy);
+var my = class extends Ge {
   constructor(e) {
     super(e);
     if (e.dims == null)
@@ -15377,9 +15377,9 @@ var gy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-gy.className = "Permute";
-ae.registerClass(gy);
-var by = class extends Ge {
+my.className = "Permute";
+ae.registerClass(my);
+var gy = class extends Ge {
   constructor(e) {
     super(e == null ? {} : e);
     this.supportsMasking = true, e != null ? this.maskValue = e.maskValue == null ? 0 : e.maskValue : this.maskValue = 0;
@@ -15393,19 +15393,19 @@ var by = class extends Ge {
   }
   computeMask(e, t) {
     let n = Oe(e), s = -1;
-    return dm(Xu(n, this.maskValue), s);
+    return cm(Ku(n, this.maskValue), s);
   }
   call(e, t) {
     return j(() => {
       this.invokeCallHook(e, t);
-      let n = Oe(e), s = -1, r = true, a = dm(Xu(n, this.maskValue), s, r);
+      let n = Oe(e), s = -1, r = true, a = cm(Ku(n, this.maskValue), s, r);
       return V(n, ce(a, n.dtype));
     });
   }
 };
-by.className = "Masking";
-ae.registerClass(by);
-var yy = class extends Ge {
+gy.className = "Masking";
+ae.registerClass(gy);
+var by = class extends Ge {
   constructor(e) {
     super(e);
     if (this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
@@ -15420,7 +15420,7 @@ var yy = class extends Ge {
   warnOnIncompatibleInputShape(e) {
   }
   computeMask(e, t) {
-    return j(() => this.maskZero ? (e = Oe(e), Xu(e, je(e))) : null);
+    return j(() => this.maskZero ? (e = Oe(e), Ku(e, je(e))) : null);
   }
   computeOutputShape(e) {
     if (e = nt(e), this.inputLength == null)
@@ -15443,7 +15443,7 @@ var yy = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      n.dtype !== "int32" && (n = Sp(n, "int32"));
+      n.dtype !== "int32" && (n = Ip(n, "int32"));
       let s = VI(this.embeddings.read(), G(n, [n.size]));
       return G(s, nt(this.computeOutputShape(n.shape)));
     });
@@ -15453,8 +15453,8 @@ var yy = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-yy.className = "Embedding";
-ae.registerClass(yy);
+by.className = "Embedding";
+ae.registerClass(by);
 var mi = class extends Ge {
   constructor(e) {
     super(e || {});
@@ -15512,7 +15512,7 @@ var mi = class extends Ge {
           for (let a of e) {
             let i = a.rank;
             for (let o = 0; o < r - i; ++o)
-              a = Bl(a, 1);
+              a = Ll(a, 1);
             n.push(a);
           }
           return this.mergeFunction(n);
@@ -15578,7 +15578,7 @@ var mi = class extends Ge {
     });
   }
 };
-var vy = class extends mi {
+var yy = class extends mi {
   constructor(e) {
     super(e);
   }
@@ -15591,9 +15591,9 @@ var vy = class extends mi {
     });
   }
 };
-vy.className = "Add";
-ae.registerClass(vy);
-var xy = class extends mi {
+yy.className = "Add";
+ae.registerClass(yy);
+var vy = class extends mi {
   constructor(e) {
     super(e);
   }
@@ -15606,9 +15606,9 @@ var xy = class extends mi {
     });
   }
 };
-xy.className = "Multiply";
-ae.registerClass(xy);
-var wy = class extends mi {
+vy.className = "Multiply";
+ae.registerClass(vy);
+var xy = class extends mi {
   constructor(e) {
     super(e);
   }
@@ -15621,9 +15621,9 @@ var wy = class extends mi {
     });
   }
 };
-wy.className = "Average";
-ae.registerClass(wy);
-var ky = class extends mi {
+xy.className = "Average";
+ae.registerClass(xy);
+var wy = class extends mi {
   constructor(e) {
     super(e);
   }
@@ -15636,9 +15636,9 @@ var ky = class extends mi {
     });
   }
 };
-ky.className = "Maximum";
-ae.registerClass(ky);
-var Iy = class extends mi {
+wy.className = "Maximum";
+ae.registerClass(wy);
+var ky = class extends mi {
   constructor(e) {
     super(e);
   }
@@ -15646,14 +15646,14 @@ var Iy = class extends mi {
     return j(() => {
       let t = e[0];
       for (let n = 1; n < e.length; ++n)
-        t = bp(t, e[n]);
+        t = gp(t, e[n]);
       return t;
     });
   }
 };
-Iy.className = "Minimum";
-ae.registerClass(Iy);
-var Sy = class extends mi {
+ky.className = "Minimum";
+ae.registerClass(ky);
+var Iy = class extends mi {
   constructor(e) {
     super(e);
     this.DEFAULT_AXIS = -1, e == null && (e = {}), this.axis = e.axis == null ? this.DEFAULT_AXIS : e.axis, this.supportsMasking = true, this.reshapeRequired = false;
@@ -15686,7 +15686,7 @@ var Sy = class extends mi {
       throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(e));
   }
   mergeFunction(e) {
-    return j(() => Tb(e, this.axis));
+    return j(() => Nb(e, this.axis));
   }
   computeOutputShape(e) {
     if (!(Array.isArray(e) && Array.isArray(e[0])))
@@ -15731,9 +15731,9 @@ var Sy = class extends mi {
     return Object.assign(e, t), e;
   }
 };
-Sy.className = "Concatenate";
-ae.registerClass(Sy);
-function Cu(e, t) {
+Iy.className = "Concatenate";
+ae.registerClass(Iy);
+function Su(e, t) {
   for (; e < 0; )
     e += t;
   return e;
@@ -15780,7 +15780,7 @@ function EB(e, t, n) {
     return o.shape.length === 1 && (o = On(o, 1)), o;
   });
 }
-var Cy = class extends mi {
+var Sy = class extends mi {
   constructor(e) {
     super(e);
     this.axes = e.axes, this.normalize = e.normalize == null ? false : e.normalize, this.supportsMasking = true, this.reshapeRequired = false;
@@ -15798,11 +15798,11 @@ var Cy = class extends mi {
     if (e.length !== 2)
       throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);
     let t = e[0], n = e[1], s;
-    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Cu(r, e[a].shape.length)) : s = [Cu(this.axes, t.shape.length), Cu(this.axes, n.shape.length)], this.normalize && (t = Nd(t, s[0]), n = Nd(n, s[1])), EB(t, n, s);
+    return Array.isArray(this.axes) ? s = this.axes.map((r, a) => Su(r, e[a].shape.length)) : s = [Su(this.axes, t.shape.length), Su(this.axes, n.shape.length)], this.normalize && (t = Cd(t, s[0]), n = Cd(n, s[1])), EB(t, n, s);
   }
   interpretAxes(e, t) {
     let n;
-    return Array.isArray(this.axes) ? n = this.axes : n = [Cu(this.axes, e.length), Cu(this.axes, t.length)], n;
+    return Array.isArray(this.axes) ? n = this.axes : n = [Su(this.axes, e.length), Su(this.axes, t.length)], n;
   }
   computeOutputShape(e) {
     w.assert(Array.isArray(e) && e.length === 2 && Array.isArray(e[0]) && Array.isArray(e[1]), () => "A `Dot` layer should be called on a list of exactly 2 inputs.");
@@ -15822,9 +15822,9 @@ var Cy = class extends mi {
     return Object.assign(e, t), e;
   }
 };
-Cy.className = "Dot";
-ae.registerClass(Cy);
-var Ny = class extends Ge {
+Sy.className = "Dot";
+ae.registerClass(Sy);
+var Cy = class extends Ge {
   constructor(e) {
     super(e);
     this.supportsMasking = true, this.stddev = e.stddev;
@@ -15840,13 +15840,13 @@ var Ny = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return Wl(() => ie(Cp(n.shape, 0, this.stddev), n), () => n, t.training || false);
+      return Vl(() => ie(Sp(n.shape, 0, this.stddev), n), () => n, t.training || false);
     });
   }
 };
-Ny.className = "GaussianNoise";
-ae.registerClass(Ny);
-var Ty = class extends Ge {
+Cy.className = "GaussianNoise";
+ae.registerClass(Cy);
+var Ny = class extends Ge {
   constructor(e) {
     super(e);
     this.supportsMasking = true, this.rate = e.rate;
@@ -15862,16 +15862,16 @@ var Ty = class extends Ge {
     return j(() => {
       this.invokeCallHook(e, t);
       let n = Oe(e);
-      return this.rate > 0 && this.rate < 1 ? Wl(() => {
+      return this.rate > 0 && this.rate < 1 ? Vl(() => {
         let r = Math.sqrt(this.rate / (1 - this.rate));
-        return V(n, Cp(n.shape, 1, r));
+        return V(n, Sp(n.shape, 1, r));
       }, () => n, t.training || false) : n;
     });
   }
 };
-Ty.className = "GaussianDropout";
-ae.registerClass(Ty);
-var $y = class extends Ge {
+Ny.className = "GaussianDropout";
+ae.registerClass(Ny);
+var Ty = class extends Ge {
   constructor(e) {
     super(e);
     this.supportsMasking = true, this.rate = e.rate, this.noiseShape = e.noiseShape;
@@ -15890,9 +15890,9 @@ var $y = class extends Ge {
     return j(() => {
       if (this.rate < 1 && this.rate > 0) {
         let n = this._getNoiseShape(e);
-        return Wl(() => {
-          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = Ko(zl(n), this.rate);
-          u = Sp(u, "float32");
+        return Vl(() => {
+          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = jo(Pl(n), this.rate);
+          u = Ip(u, "float32");
           let l = ((1 - this.rate) * (1 + this.rate * o ** 2)) ** -0.5, c = -l * o * this.rate, p = ie(V(r, u), V(ie(u, -1), o));
           return ie(V(p, l), c);
         }, () => Oe(e), t.training || false);
@@ -15901,9 +15901,9 @@ var $y = class extends Ge {
     });
   }
 };
-$y.className = "AlphaDropout";
-ae.registerClass($y);
-function Ju(e, t, n, s, r, a = 1e-3) {
+Ty.className = "AlphaDropout";
+ae.registerClass(Ty);
+function Zu(e, t, n, s, r, a = 1e-3) {
   let i;
   if (e.rank === 2)
     i = vE(e, t, n, s, r, a);
@@ -15917,23 +15917,23 @@ function Ju(e, t, n, s, r, a = 1e-3) {
 }
 function RB(e, t, n, s, r = 1e-3) {
   return j(() => {
-    let a = eb(e, s), i = a.mean, o = a.variance;
-    return [Ju(e, i, o, n, t, r), i, o];
+    let a = Jg(e, s), i = a.mean, o = a.variance;
+    return [Zu(e, i, o, n, t, r), i, o];
   });
 }
 function DB(e, t, n, s, r = 1e-3) {
   return j(() => {
-    let a = eb(e, s), i = a.mean, o = a.variance, u = [];
+    let a = Jg(e, s), i = a.mean, o = a.variance, u = [];
     for (let f of ys(0, e.rank))
       s.indexOf(f) !== -1 ? u.push(1) : u.push(e.shape[f]);
     let l = G(i, u), c = G(o, u), p = t == null ? null : G(t, u), d = n == null ? null : G(n, u);
-    return [Ju(e, l, c, d, p, r), i, o];
+    return [Zu(e, l, c, d, p, r), i, o];
   });
 }
 function FB(e, t, n, s, r = 1e-3) {
   return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? RB(e, t, n, s, r) : DB(e, t, n, s, r);
 }
-var _y = class extends Ge {
+var $y = class extends Ge {
   constructor(e) {
     e == null && (e = {});
     super(e);
@@ -15959,9 +15959,9 @@ var _y = class extends Ge {
       let c = !w.arraysEqual(l, ys(0, a).slice(0, a - 1)), p = () => {
         if (c) {
           let b = G(this.movingMean.read(), u), y = G(this.movingVariance.read(), u), v = this.center ? G(this.beta.read(), u) : null, x = this.scale ? G(this.gamma.read(), u) : null;
-          return Ju(s, b, y, v, x, this.epsilon);
+          return Zu(s, b, y, v, x, this.epsilon);
         } else
-          return Ju(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
+          return Zu(s, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);
       };
       if (!n)
         return p();
@@ -15981,9 +15981,9 @@ var _y = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-_y.className = "BatchNormalization";
-ae.registerClass(_y);
-var Ay = class extends Ge {
+$y.className = "BatchNormalization";
+ae.registerClass($y);
+var _y = class extends Ge {
   constructor(e) {
     e == null && (e = {});
     super(e);
@@ -16015,13 +16015,13 @@ var Ay = class extends Ge {
   call(e, t) {
     let n = Oe(e), s = n.shape, r = s.length;
     return j(() => {
-      let { mean: i, variance: o } = eb(n, this.axis, true), u = pa(1, r);
+      let { mean: i, variance: o } = Jg(n, this.axis, true), u = pa(1, r);
       for (let f of this.axis)
         u[f] = s[f];
       let l = (f) => f != null && f.shape.length !== r ? G(f, u) : f, c = l(this.gamma.read()), p = l(this.beta.read()), d = [], h = [];
       for (let f = 0; f < r; ++f)
         this.axis.indexOf(f) !== -1 ? (d.push(s[f]), h.push(1)) : (d.push(1), h.push(s[f]));
-      return i = cs(i, d), o = cs(o, d), c = cs(c, h), p = cs(p, h), Ju(n, i, o, p, c, this.epsilon);
+      return i = cs(i, d), o = cs(o, d), c = cs(c, h), p = cs(p, h), Zu(n, i, o, p, c, this.epsilon);
     });
   }
   getConfig() {
@@ -16029,8 +16029,8 @@ var Ay = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Ay.className = "LayerNormalization";
-ae.registerClass(Ay);
+_y.className = "LayerNormalization";
+ae.registerClass(_y);
 function OB(e, t, n) {
   return j(() => {
     if (e.rank !== 4)
@@ -16043,7 +16043,7 @@ function OB(e, t, n) {
     return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], pi(e, s);
   });
 }
-var Ey = class extends Ge {
+var Ay = class extends Ge {
   constructor(e) {
     e == null && (e = {});
     super(e);
@@ -16081,13 +16081,13 @@ var Ey = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-Ey.className = "ZeroPadding2D";
-ae.registerClass(Ey);
-function Gp(e, t, n, s, r, a) {
+Ay.className = "ZeroPadding2D";
+ae.registerClass(Ay);
+function Up(e, t, n, s, r, a) {
   return j(() => {
-    St(r), zI(a), Un(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = bs()), a == null && (a = "max"), e = Qb(e, r);
+    St(r), zI(a), Un(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = bs()), a == null && (a = "max"), e = Yb(e, r);
     let i, o = s === "same" ? "same" : "valid";
-    return a === "max" ? i = Jg(e, t, n, o) : i = Ug(e, t, n, o), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
+    return a === "max" ? i = Zg(e, t, n, o) : i = Wg(e, t, n, o), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
   });
 }
 function A0(e, t, n, s, r, a) {
@@ -16124,7 +16124,7 @@ var E0 = class extends Ge {
   }
   call(e, t) {
     return j(() => {
-      this.invokeCallHook(e, t), e = Bl(Oe(e), 2);
+      this.invokeCallHook(e, t), e = Ll(Oe(e), 2);
       let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
       return mr(n, [2]);
     });
@@ -16134,26 +16134,26 @@ var E0 = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
+var Ey = class extends E0 {
+  constructor(e) {
+    super(e);
+  }
+  poolingFunction(e, t, n, s, r) {
+    return St(r), Un(s), Up(e, t, n, s, r, "max");
+  }
+};
+Ey.className = "MaxPooling1D";
+ae.registerClass(Ey);
 var Ry = class extends E0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return St(r), Un(s), Gp(e, t, n, s, r, "max");
+    return St(r), Un(s), Up(e, t, n, s, r, "avg");
   }
 };
-Ry.className = "MaxPooling1D";
+Ry.className = "AveragePooling1D";
 ae.registerClass(Ry);
-var Dy = class extends E0 {
-  constructor(e) {
-    super(e);
-  }
-  poolingFunction(e, t, n, s, r) {
-    return St(r), Un(s), Gp(e, t, n, s, r, "avg");
-  }
-};
-Dy.className = "AveragePooling1D";
-ae.registerClass(Dy);
 var R0 = class extends Ge {
   constructor(e) {
     e.poolSize == null && (e.poolSize = [2, 2]);
@@ -16181,26 +16181,26 @@ var R0 = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
+var Dy = class extends R0 {
+  constructor(e) {
+    super(e);
+  }
+  poolingFunction(e, t, n, s, r) {
+    return St(r), Un(s), Up(e, t, n, s, r, "max");
+  }
+};
+Dy.className = "MaxPooling2D";
+ae.registerClass(Dy);
 var Fy = class extends R0 {
   constructor(e) {
     super(e);
   }
   poolingFunction(e, t, n, s, r) {
-    return St(r), Un(s), Gp(e, t, n, s, r, "max");
+    return St(r), Un(s), Up(e, t, n, s, r, "avg");
   }
 };
-Fy.className = "MaxPooling2D";
+Fy.className = "AveragePooling2D";
 ae.registerClass(Fy);
-var Oy = class extends R0 {
-  constructor(e) {
-    super(e);
-  }
-  poolingFunction(e, t, n, s, r) {
-    return St(r), Un(s), Gp(e, t, n, s, r, "avg");
-  }
-};
-Oy.className = "AveragePooling2D";
-ae.registerClass(Oy);
 var D0 = class extends Ge {
   constructor(e) {
     e.poolSize == null && (e.poolSize = [2, 2, 2]);
@@ -16228,7 +16228,7 @@ var D0 = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var Py = class extends D0 {
+var Oy = class extends D0 {
   constructor(e) {
     super(e);
   }
@@ -16236,9 +16236,9 @@ var Py = class extends D0 {
     return St(r), Un(s), A0(e, t, n, s, r, "max");
   }
 };
-Py.className = "MaxPooling3D";
-ae.registerClass(Py);
-var zy = class extends D0 {
+Oy.className = "MaxPooling3D";
+ae.registerClass(Oy);
+var Py = class extends D0 {
   constructor(e) {
     super(e);
   }
@@ -16246,8 +16246,8 @@ var zy = class extends D0 {
     return St(r), Un(s), A0(e, t, n, s, r, "avg");
   }
 };
-zy.className = "AveragePooling3D";
-ae.registerClass(zy);
+Py.className = "AveragePooling3D";
+ae.registerClass(Py);
 var F0 = class extends Ge {
   constructor(e) {
     super(e);
@@ -16260,7 +16260,7 @@ var F0 = class extends Ge {
     throw new Fe();
   }
 };
-var My = class extends F0 {
+var zy = class extends F0 {
   constructor(e) {
     super(e || {});
   }
@@ -16271,9 +16271,9 @@ var My = class extends F0 {
     });
   }
 };
-My.className = "GlobalAveragePooling1D";
-ae.registerClass(My);
-var Ly = class extends F0 {
+zy.className = "GlobalAveragePooling1D";
+ae.registerClass(zy);
+var My = class extends F0 {
   constructor(e) {
     super(e || {});
   }
@@ -16284,8 +16284,8 @@ var Ly = class extends F0 {
     });
   }
 };
-Ly.className = "GlobalMaxPooling1D";
-ae.registerClass(Ly);
+My.className = "GlobalMaxPooling1D";
+ae.registerClass(My);
 var O0 = class extends Ge {
   constructor(e) {
     super(e);
@@ -16302,7 +16302,7 @@ var O0 = class extends Ge {
     return Object.assign(e, t), e;
   }
 };
-var By = class extends O0 {
+var Ly = class extends O0 {
   call(e, t) {
     return j(() => {
       let n = Oe(e);
@@ -16310,9 +16310,9 @@ var By = class extends O0 {
     });
   }
 };
-By.className = "GlobalAveragePooling2D";
-ae.registerClass(By);
-var Vy = class extends O0 {
+Ly.className = "GlobalAveragePooling2D";
+ae.registerClass(Ly);
+var By = class extends O0 {
   call(e, t) {
     return j(() => {
       let n = Oe(e);
@@ -16320,8 +16320,8 @@ var Vy = class extends O0 {
     });
   }
 };
-Vy.className = "GlobalMaxPooling2D";
-ae.registerClass(Vy);
+By.className = "GlobalMaxPooling2D";
+ae.registerClass(By);
 var P0 = class extends Ge {
   constructor(e) {
     super(e);
@@ -16368,7 +16368,7 @@ var P0 = class extends Ge {
     return Object.assign(a, t), new e(a);
   }
 };
-var Wy = class extends P0 {
+var Vy = class extends P0 {
   constructor(e) {
     super(e);
     this.supportsMasking = true;
@@ -16389,13 +16389,13 @@ var Wy = class extends P0 {
     return j(() => (e = Oe(e), T0((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
   }
 };
-Wy.className = "TimeDistributed";
-ae.registerClass(Wy);
+Vy.className = "TimeDistributed";
+ae.registerClass(Vy);
 function PB(e) {
   hi(VM, "BidirectionalMergeMode", e);
 }
 var zB = "concat";
-var Uy = class extends P0 {
+var Wy = class extends P0 {
   constructor(e) {
     super(e);
     let t = e.layer.getConfig(), n = {};
@@ -16465,7 +16465,7 @@ var Uy = class extends P0 {
       let a;
       this.returnState && (Array.isArray(s) && (a = s.slice(1).concat(r.slice(1))), s = s[0], r = r[0]), this.returnSequences && (r = Yn(r, 1));
       let i;
-      return this.mergeMode === "concat" ? i = Tb([s, r]) : this.mergeMode === "sum" ? i = ie(s, r) : this.mergeMode === "ave" ? i = V(0.5, ie(s, r)) : this.mergeMode === "mul" ? i = V(s, r) : this.mergeMode == null && (i = [s, r]), this.returnState ? this.mergeMode == null ? i.concat(a) : [i].concat(a) : i;
+      return this.mergeMode === "concat" ? i = Nb([s, r]) : this.mergeMode === "sum" ? i = ie(s, r) : this.mergeMode === "ave" ? i = V(0.5, ie(s, r)) : this.mergeMode === "mul" ? i = V(s, r) : this.mergeMode == null && (i = [s, r]), this.returnState ? this.mergeMode == null ? i.concat(a) : [i].concat(a) : i;
     });
   }
   resetStates(e) {
@@ -16508,217 +16508,217 @@ var Uy = class extends P0 {
     return s.layer = n, new e(s);
   }
 };
-Uy.className = "Bidirectional";
-ae.registerClass(Uy);
+Wy.className = "Bidirectional";
+ae.registerClass(Wy);
 function MB(e) {
-  return new Qo(e);
+  return new Yo(e);
 }
 function LB(e) {
-  return new Kb(e);
-}
-function BB(e) {
-  return new Hb(e);
-}
-function VB(e) {
-  return new qb(e);
-}
-function WB(e) {
   return new jb(e);
 }
-function UB(e) {
-  return new Yb(e);
+function BB(e) {
+  return new Gb(e);
 }
-function GB(e) {
+function VB(e) {
+  return new Hb(e);
+}
+function WB(e) {
+  return new qb(e);
+}
+function UB(e) {
   return new Xb(e);
 }
+function GB(e) {
+  return new Kb(e);
+}
 function HB(e) {
-  return new ny(e);
-}
-function qB(e) {
-  return new zp(e);
-}
-function jB(e) {
-  return new Jb(e);
-}
-function KB(e) {
-  return new Mp(e);
-}
-function XB(e) {
-  return new ey(e);
-}
-function YB(e) {
   return new ty(e);
 }
+function qB(e) {
+  return new Pp(e);
+}
+function jB(e) {
+  return new Zb(e);
+}
+function KB(e) {
+  return new zp(e);
+}
+function XB(e) {
+  return new Jb(e);
+}
+function YB(e) {
+  return new ey(e);
+}
 function QB(e) {
-  return new sy(e);
+  return new ny(e);
 }
 function ZB(e) {
-  return new ry(e);
+  return new sy(e);
 }
 function JB(e) {
-  return new ay(e);
+  return new ry(e);
 }
 function eV(e) {
-  return new hy(e);
-}
-function tV(e) {
-  return new dy(e);
-}
-function nV(e) {
-  return new Up(e);
-}
-function sV(e) {
-  return new cy(e);
-}
-function rV(e) {
   return new py(e);
 }
+function tV(e) {
+  return new cy(e);
+}
+function nV(e) {
+  return new Wp(e);
+}
+function sV(e) {
+  return new ly(e);
+}
+function rV(e) {
+  return new dy(e);
+}
 function aV(e) {
-  return new fy(e);
+  return new hy(e);
 }
 function iV(e) {
-  return new my(e);
+  return new fy(e);
 }
 function oV(e) {
-  return new gy(e);
+  return new my(e);
 }
 function uV(e) {
-  return new yy(e);
+  return new by(e);
 }
 function lV(e) {
-  return new vy(e);
+  return new yy(e);
 }
 function cV(e) {
-  return new wy(e);
-}
-function dV(e) {
-  return new Sy(e);
-}
-function pV(e) {
-  return new ky(e);
-}
-function hV(e) {
-  return new Iy(e);
-}
-function fV(e) {
   return new xy(e);
 }
+function dV(e) {
+  return new Iy(e);
+}
+function pV(e) {
+  return new wy(e);
+}
+function hV(e) {
+  return new ky(e);
+}
+function fV(e) {
+  return new vy(e);
+}
 function mV(e) {
-  return new Cy(e);
+  return new Sy(e);
 }
 function gV(e) {
-  return new _y(e);
+  return new $y(e);
 }
 function bV(e) {
-  return new Ay(e);
+  return new _y(e);
 }
 function yV(e) {
-  return new Ey(e);
+  return new Ay(e);
 }
-function Gy(e) {
-  return new Dy(e);
+function Uy(e) {
+  return new Ry(e);
 }
 function vV(e) {
-  return Gy(e);
+  return Uy(e);
 }
 function xV(e) {
+  return Uy(e);
+}
+function Gy(e) {
+  return new Fy(e);
+}
+function wV(e) {
+  return Gy(e);
+}
+function kV(e) {
   return Gy(e);
 }
 function Hy(e) {
-  return new Oy(e);
-}
-function wV(e) {
-  return Hy(e);
-}
-function kV(e) {
-  return Hy(e);
-}
-function qy(e) {
-  return new zy(e);
-}
-function IV(e) {
-  return qy(e);
-}
-function SV(e) {
-  return qy(e);
-}
-function CV(e) {
-  return new My(e);
-}
-function NV(e) {
-  return new By(e);
-}
-function z0(e) {
-  return new Ly(e);
-}
-function M0(e) {
-  return new Vy(e);
-}
-function L0(e) {
-  return new Ry(e);
-}
-function B0(e) {
-  return new Fy(e);
-}
-function TV(e) {
   return new Py(e);
 }
+function IV(e) {
+  return Hy(e);
+}
+function SV(e) {
+  return Hy(e);
+}
+function CV(e) {
+  return new zy(e);
+}
+function NV(e) {
+  return new Ly(e);
+}
+function z0(e) {
+  return new My(e);
+}
+function M0(e) {
+  return new By(e);
+}
+function L0(e) {
+  return new Ey(e);
+}
+function B0(e) {
+  return new Dy(e);
+}
+function TV(e) {
+  return new Oy(e);
+}
 function $V(e) {
-  return new oy(e);
-}
-function _V(e) {
-  return new Bp(e);
-}
-function AV(e) {
-  return new uy(e);
-}
-function EV(e) {
-  return new ql(e);
-}
-function RV(e) {
   return new iy(e);
 }
-function DV(e) {
+function _V(e) {
   return new Lp(e);
 }
+function AV(e) {
+  return new oy(e);
+}
+function EV(e) {
+  return new Hl(e);
+}
+function RV(e) {
+  return new ay(e);
+}
+function DV(e) {
+  return new Mp(e);
+}
 function FV(e) {
-  return new ly(e);
+  return new uy(e);
 }
 function OV(e) {
-  return new Wp(e);
+  return new Vp(e);
 }
 function PV(e) {
   return new _r(e);
 }
 function zV(e) {
-  return new Vp(e);
+  return new Bp(e);
 }
 function MV(e) {
-  return new Uy(e);
+  return new Wy(e);
 }
 function LV(e) {
-  return new Wy(e);
+  return new Vy(e);
 }
 var BV = z0;
 var VV = M0;
 var WV = L0;
 var UV = B0;
 function GV(e) {
-  return new Ny(e);
+  return new Cy(e);
 }
 function HV(e) {
-  return new Ty(e);
+  return new Ny(e);
 }
 function qV(e) {
-  return new $y(e);
+  return new Ty(e);
 }
 function jV(e) {
-  return new by(e);
+  return new gy(e);
 }
 var KV = {};
 Ae(KV, { MAPE: () => aW, MSE: () => uW, binaryAccuracy: () => XV, binaryCrossentropy: () => YV, categoricalAccuracy: () => ZV, categoricalCrossentropy: () => JV, cosineProximity: () => nW, mape: () => iW, meanAbsoluteError: () => sW, meanAbsolutePercentageError: () => rW, meanSquaredError: () => oW, mse: () => lW, precision: () => eW, recall: () => tW, sparseCategoricalAccuracy: () => QV });
 function XV(e, t) {
-  return Mb(e, t);
+  return zb(e, t);
 }
 function YV(e, t) {
   return QI(e, t);
@@ -16727,10 +16727,10 @@ function QV(e, t) {
   return ZI(e, t);
 }
 function ZV(e, t) {
-  return Lb(e, t);
+  return Mb(e, t);
 }
 function JV(e, t) {
-  return Bb(e, t);
+  return Lb(e, t);
 }
 function eW(e, t) {
   return YI(e, t);
@@ -16739,19 +16739,19 @@ function tW(e, t) {
   return VL(e, t);
 }
 function nW(e, t) {
-  return zb(e, t);
+  return Pb(e, t);
 }
 function sW(e, t) {
-  return Op(e, t);
+  return Fp(e, t);
 }
 function rW(e, t) {
-  return Zo(e, t);
+  return Qo(e, t);
 }
 function aW(e, t) {
-  return Zo(e, t);
+  return Qo(e, t);
 }
 function iW(e, t) {
-  return Zo(e, t);
+  return Qo(e, t);
 }
 function oW(e, t) {
   return fi(e, t);
@@ -16767,7 +16767,7 @@ Ae(cW, { modelFromJSON: () => wB });
 var dW = {};
 Ae(dW, { l1: () => hW, l1l2: () => pW, l2: () => fW });
 function pW(e) {
-  return new Ul(e);
+  return new Wl(e);
 }
 function hW(e) {
   return NB(e);
@@ -16775,7 +16775,7 @@ function hW(e) {
 function fW(e) {
   return TB(e);
 }
-var mW = class extends Ji {
+var mW = class extends Zi {
   constructor() {
     super(...arguments);
     this.model = null;
@@ -16786,10 +16786,10 @@ var mW = class extends Ji {
     this.model = e;
   }
 };
-function jc(e, t) {
+function qc(e, t) {
   return e < t;
 }
-function Wx(e, t) {
+function Vx(e, t) {
   return e > t;
 }
 var gW = class extends mW {
@@ -16797,10 +16797,10 @@ var gW = class extends mW {
     super();
     if (e == null && (e = {}), e.restoreBestWeights)
       throw new Fe("restoreBestWeights = True is not implemented in EarlyStopping yet.");
-    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = jc : this.mode === "max" ? this.monitorFunc = Wx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Wx : this.monitorFunc = jc, this.monitorFunc === jc && (this.minDelta *= -1);
+    this.monitor = e.monitor || "val_loss", this.minDelta = Math.abs(e.minDelta || 0), this.patience = e.patience || 0, this.verbose = e.verbose || 0, this.mode = e.mode || "auto", this.baseline = e.baseline, ["auto", "min", "max"].indexOf(this.mode) === -1 && (console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`), this.mode = "auto"), this.mode === "min" ? this.monitorFunc = qc : this.mode === "max" ? this.monitorFunc = Vx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Vx : this.monitorFunc = qc, this.monitorFunc === qc && (this.minDelta *= -1);
   }
   async onTrainBegin(e) {
-    this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === jc ? 1 / 0 : -1 / 0;
+    this.wait = 0, this.stoppedEpoch = 0, this.baseline != null ? this.best = this.baseline : this.best = this.monitorFunc === qc ? 1 / 0 : -1 / 0;
   }
   async onEpochEnd(e, t) {
     await rr(t);
@@ -16825,23 +16825,23 @@ yW.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (e) => {
   e && console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
 });
 var V0 = ((e) => (e[e.DT_INVALID = 0] = "DT_INVALID", e[e.DT_FLOAT = 1] = "DT_FLOAT", e[e.DT_DOUBLE = 2] = "DT_DOUBLE", e[e.DT_INT32 = 3] = "DT_INT32", e[e.DT_UINT8 = 4] = "DT_UINT8", e[e.DT_INT16 = 5] = "DT_INT16", e[e.DT_INT8 = 6] = "DT_INT8", e[e.DT_STRING = 7] = "DT_STRING", e[e.DT_COMPLEX64 = 8] = "DT_COMPLEX64", e[e.DT_INT64 = 9] = "DT_INT64", e[e.DT_BOOL = 10] = "DT_BOOL", e[e.DT_QINT8 = 11] = "DT_QINT8", e[e.DT_QUINT8 = 12] = "DT_QUINT8", e[e.DT_QINT32 = 13] = "DT_QINT32", e[e.DT_BFLOAT16 = 14] = "DT_BFLOAT16", e[e.DT_QINT16 = 15] = "DT_QINT16", e[e.DT_QUINT16 = 16] = "DT_QUINT16", e[e.DT_UINT16 = 17] = "DT_UINT16", e[e.DT_COMPLEX128 = 18] = "DT_COMPLEX128", e[e.DT_HALF = 19] = "DT_HALF", e[e.DT_RESOURCE = 20] = "DT_RESOURCE", e[e.DT_VARIANT = 21] = "DT_VARIANT", e[e.DT_UINT32 = 22] = "DT_UINT32", e[e.DT_UINT64 = 23] = "DT_UINT64", e[e.DT_FLOAT_REF = 101] = "DT_FLOAT_REF", e[e.DT_DOUBLE_REF = 102] = "DT_DOUBLE_REF", e[e.DT_INT32_REF = 103] = "DT_INT32_REF", e[e.DT_UINT8_REF = 104] = "DT_UINT8_REF", e[e.DT_INT16_REF = 105] = "DT_INT16_REF", e[e.DT_INT8_REF = 106] = "DT_INT8_REF", e[e.DT_STRING_REF = 107] = "DT_STRING_REF", e[e.DT_COMPLEX64_REF = 108] = "DT_COMPLEX64_REF", e[e.DT_INT64_REF = 109] = "DT_INT64_REF", e[e.DT_BOOL_REF = 110] = "DT_BOOL_REF", e[e.DT_QINT8_REF = 111] = "DT_QINT8_REF", e[e.DT_QUINT8_REF = 112] = "DT_QUINT8_REF", e[e.DT_QINT32_REF = 113] = "DT_QINT32_REF", e[e.DT_BFLOAT16_REF = 114] = "DT_BFLOAT16_REF", e[e.DT_QINT16_REF = 115] = "DT_QINT16_REF", e[e.DT_QUINT16_REF = 116] = "DT_QUINT16_REF", e[e.DT_UINT16_REF = 117] = "DT_UINT16_REF", e[e.DT_COMPLEX128_REF = 118] = "DT_COMPLEX128_REF", e[e.DT_HALF_REF = 119] = "DT_HALF_REF", e[e.DT_RESOURCE_REF = 120] = "DT_RESOURCE_REF", e[e.DT_VARIANT_REF = 121] = "DT_VARIANT_REF", e[e.DT_UINT32_REF = 122] = "DT_UINT32_REF", e[e.DT_UINT64_REF = 123] = "DT_UINT64_REF", e))(V0 || {});
-var Ux;
+var Wx;
 ((e) => {
   let t;
   ((n) => {
     n[n.LEGACY = 0] = "LEGACY", n[n.V1 = 1] = "V1", n[n.V2 = 2] = "V2";
   })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
-})(Ux || (Ux = {}));
-var jy = {};
+})(Wx || (Wx = {}));
+var qy = {};
 function mpe(e, t) {
   let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
-  jy[e] = n;
+  qy[e] = n;
 }
 function W0(e) {
-  return jy[e];
+  return qy[e];
 }
 function gpe(e) {
-  delete jy[e];
+  delete qy[e];
 }
 function I(e, t, n, s, r) {
   let a = t.inputParams[e];
@@ -16864,17 +16864,17 @@ function on(e, t, n, s) {
     if (o != null)
       return o;
   }
-  let i = n.currentContextIds.find((o) => !!t[Ed(r, o)]);
-  return i !== void 0 ? t[Ed(r, i)][a] : void 0;
+  let i = n.currentContextIds.find((o) => !!t[Ad(r, o)]);
+  return i !== void 0 ? t[Ad(r, i)][a] : void 0;
 }
 function vW(e, t, n) {
-  return t[Ed(e, n.currentContextId)];
+  return t[Ad(e, n.currentContextId)];
 }
 function Ts(e, t) {
   let [n, s, r] = $n(e);
-  return [Ed(n, t && t.currentContextId), s, r];
+  return [Ad(n, t && t.currentContextId), s, r];
 }
-function Ed(e, t) {
+function Ad(e, t) {
   return t ? `${e}-${t}` : e;
 }
 function $n(e) {
@@ -16884,7 +16884,7 @@ function $n(e) {
   let n = t[0], s = t.length === 3 ? t[1] : void 0, r = Number(t[t.length - 1]);
   return [n, r, s];
 }
-function sd(e, t, n) {
+function nd(e, t, n) {
   let s = I("pad", e, t, n);
   if (s === "explicit") {
     s = I("explicitPaddings", e, t, n);
@@ -16955,7 +16955,7 @@ var zW = [{ tfOpName: "StringNGrams", category: "string", inputs: [{ start: 0, n
 var oS = {};
 Ae(oS, { json: () => MW });
 var MW = [{ tfOpName: "Cast", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "SrcT", name: "sdtype", type: "dtype", notSupported: true }, { tfName: "DstT", name: "dtype", type: "dtype" }] }, { tfOpName: "ExpandDims", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "MirrorPad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "mode", name: "mode", type: "string" }] }, { tfOpName: "Pad", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }], attrs: [{ tfName: "constant_value", name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "PadV2", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "padding", type: "number[]" }, { start: 2, name: "constantValue", type: "number", defaultValue: 0 }] }, { tfOpName: "Reshape", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }] }, { tfOpName: "Squeeze", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "axis", tfDeprecatedName: "squeeze_dims", name: "axis", type: "number[]" }] }, { tfOpName: "SpaceToBatchND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "paddings", type: "number[]" }] }, { tfOpName: "BatchToSpaceND", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "blockShape", type: "number[]" }, { start: 2, name: "crops", type: "number[]" }] }, { tfOpName: "DepthToSpace", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }], attrs: [{ tfName: "block_size", name: "blockSize", type: "number" }, { tfName: "data_format", name: "dataFormat", type: "string" }] }, { tfOpName: "BroadcastTo", category: "transformation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "shape", type: "number[]" }], attrs: [] }, { tfOpName: "BroadcastArgs", category: "transformation", inputs: [{ start: 0, name: "s0", type: "tensor" }, { start: 1, name: "s1", type: "tensor" }], attrs: [] }];
-var Gx = class {
+var Ux = class {
   static get Instance() {
     return this._instance || (this._instance = new this());
   }
@@ -17006,37 +17006,37 @@ var Gx = class {
       let a = r.type, i;
       switch (r.type) {
         case "string":
-          i = Cm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Cm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          i = Sm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Sm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "string[]":
-          i = Rm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Rm(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "number":
-          i = Tm(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = Tm(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "number[]":
           i = Em(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Em(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "bool":
-          i = Nm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Nm(e.attr, r.tfDeprecatedName, r.defaultValue));
+        case "number":
+          i = Nm(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = Nm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "bool[]":
-          i = Fm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Fm(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "shape":
+        case "number[]":
           i = Am(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Am(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "shape[]":
+        case "bool":
+          i = Cm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Cm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
+        case "bool[]":
           i = Dm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Dm(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
-        case "dtype":
-          i = $m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = $m(e.attr, r.tfDeprecatedName, r.defaultValue));
-          break;
-        case "dtype[]":
+        case "shape":
           i = _m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = _m(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
+        case "shape[]":
+          i = Rm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Rm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
+        case "dtype":
+          i = Tm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Tm(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
+        case "dtype[]":
+          i = $m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = $m(e.attr, r.tfDeprecatedName, r.defaultValue));
+          break;
         case "func":
-          i = Hx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Hx(e.attr, r.tfDeprecatedName, r.defaultValue));
+          i = Gx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Gx(e.attr, r.tfDeprecatedName, r.defaultValue));
           break;
         case "tensor":
         case "tensors":
@@ -17052,7 +17052,7 @@ var Gx = class {
     t != null && (r = t.reduce((c, p) => (c[p.name] = this.mapNode(p), p.op === "Const" && s.push(c[p.name]), c), {}));
     let a = [], i = [];
     e.signature.inputArg.forEach((c) => {
-      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: Ky(c.type), type: "dtype" } }, children: [] };
+      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: jy(c.type), type: "dtype" } }, children: [] };
       d.signatureKey = c.name, a.push(d), r[p] = d;
     }), Object.keys(r).forEach((c) => {
       let p = r[c];
@@ -17096,19 +17096,19 @@ function uS(e, t) {
   let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : LW(e);
   return t ? n : n.toLowerCase();
 }
-function Cm(e, t, n, s = false) {
+function Sm(e, t, n, s = false) {
   let r = e[t];
   return r != null ? uS(r.s, s) : n;
 }
-function Nm(e, t, n) {
+function Cm(e, t, n) {
   let s = e[t];
   return s ? s.b : n;
 }
-function Tm(e, t, n) {
+function Nm(e, t, n) {
   let s = e[t] || {}, r = s.i != null ? s.i : s.f != null ? s.f : n;
   return typeof r == "number" ? r : parseInt(r, 10);
 }
-function Ky(e) {
+function jy(e) {
   switch (typeof e == "string" && (e = V0[e]), e) {
     case 1:
     case 19:
@@ -17128,39 +17128,39 @@ function Ky(e) {
       return null;
   }
 }
-function Hx(e, t, n) {
+function Gx(e, t, n) {
   let s = e[t];
   return s && s.func ? s.func.name : n;
 }
+function Tm(e, t, n) {
+  let s = e[t];
+  return s && s.type ? jy(s.type) : n;
+}
 function $m(e, t, n) {
   let s = e[t];
-  return s && s.type ? Ky(s.type) : n;
-}
-function _m(e, t, n) {
-  let s = e[t];
-  return s && s.list && s.list.type ? s.list.type.map((r) => Ky(r)) : n;
+  return s && s.list && s.list.type ? s.list.type.map((r) => jy(r)) : n;
 }
 function lS(e) {
   if (!e.unknownRank)
     return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
 }
-function Am(e, t, n) {
+function _m(e, t, n) {
   let s = e[t];
   return s && s.shape ? lS(s.shape) : n;
 }
-function Em(e, t, n) {
+function Am(e, t, n) {
   let s = e[t];
   return s ? ((s.list.f && s.list.f.length ? s.list.f : s.list.i) || []).map((r) => typeof r == "number" ? r : parseInt(r, 10)) : n;
 }
-function Rm(e, t, n, s = false) {
+function Em(e, t, n, s = false) {
   let r = e[t];
   return r && r.list && r.list.s ? r.list.s.map((a) => uS(a, s)) : n;
 }
-function Dm(e, t, n) {
+function Rm(e, t, n) {
   let s = e[t];
   return s && s.list && s.list.shape ? s.list.shape.map((r) => lS(r)) : n;
 }
-function Fm(e, t, n) {
+function Dm(e, t, n) {
   let s = e[t];
   return s && s.list && s.list.b ? s.list.b : n;
 }
@@ -17176,26 +17176,26 @@ var BW = class {
     if (n.tensor != null)
       return on(e, this.tensorMap, this.context);
     if (n.i != null || n.f != null)
-      return Tm(this.node.rawAttrs, e, t);
-    if (n.s != null)
-      return Cm(this.node.rawAttrs, e, t);
-    if (n.b != null)
       return Nm(this.node.rawAttrs, e, t);
+    if (n.s != null)
+      return Sm(this.node.rawAttrs, e, t);
+    if (n.b != null)
+      return Cm(this.node.rawAttrs, e, t);
     if (n.shape != null)
-      return Am(this.node.rawAttrs, e, t);
+      return _m(this.node.rawAttrs, e, t);
     if (n.type != null)
-      return $m(this.node.rawAttrs, e, t);
+      return Tm(this.node.rawAttrs, e, t);
     if (n.list != null) {
       if (n.list.i != null || n.list.f != null)
-        return Em(this.node.rawAttrs, e, t);
+        return Am(this.node.rawAttrs, e, t);
       if (n.list.s != null)
-        return Rm(this.node.rawAttrs, e, t);
+        return Em(this.node.rawAttrs, e, t);
       if (n.list.shape != null)
-        return Dm(this.node.rawAttrs, e, t);
+        return Rm(this.node.rawAttrs, e, t);
       if (n.list.b != null)
-        return Fm(this.node.rawAttrs, e, t);
+        return Dm(this.node.rawAttrs, e, t);
       if (n.list.type != null)
-        return _m(this.node.rawAttrs, e, t);
+        return $m(this.node.rawAttrs, e, t);
     }
     return t;
   }
@@ -17223,7 +17223,7 @@ var VW = (e, t, n) => {
     case "Sub":
       return [ge(I("a", e, t, n), I("b", e, t, n))];
     case "Minimum":
-      return [bp(I("a", e, t, n), I("b", e, t, n))];
+      return [gp(I("a", e, t, n), I("b", e, t, n))];
     case "Maximum":
       return [Tr(I("a", e, t, n), I("b", e, t, n))];
     case "Pow":
@@ -17258,11 +17258,11 @@ var WW = (e, t, n) => {
     case "Complex":
       return [aa(I("real", e, t, n), I("imag", e, t, n))];
     case "Cos":
-      return [qg(I("x", e, t, n))];
+      return [Hg(I("x", e, t, n))];
     case "Cosh":
       return [Yk(I("x", e, t, n))];
     case "Elu":
-      return [fp(I("x", e, t, n))];
+      return [hp(I("x", e, t, n))];
     case "Erf":
       return [fR(I("x", e, t, n))];
     case "Exp":
@@ -17270,19 +17270,19 @@ var WW = (e, t, n) => {
     case "Expm1":
       return [yR(I("x", e, t, n))];
     case "Floor":
-      return [mp(I("x", e, t, n))];
+      return [fp(I("x", e, t, n))];
     case "Log":
       return [Kn(I("x", e, t, n))];
     case "Log1p":
-      return [Xg(I("x", e, t, n))];
+      return [Kg(I("x", e, t, n))];
     case "Imag":
-      return [jg(I("x", e, t, n))];
+      return [qg(I("x", e, t, n))];
     case "Neg":
       return [kt(I("x", e, t, n))];
     case "Reciprocal":
       return [VD(I("x", e, t, n))];
     case "Real":
-      return [wd(I("x", e, t, n))];
+      return [xd(I("x", e, t, n))];
     case "Relu":
       return [Xs(I("x", e, t, n))];
     case "Round":
@@ -17298,13 +17298,13 @@ var WW = (e, t, n) => {
     case "Sinh":
       return [pI(I("x", e, t, n))];
     case "Softplus":
-      return [Pl(I("x", e, t, n))];
+      return [Ol(I("x", e, t, n))];
     case "Sqrt":
       return [ln(I("x", e, t, n))];
     case "Square":
       return [ct(I("x", e, t, n))];
     case "Tanh":
-      return [qu(I("x", e, t, n))];
+      return [Hu(I("x", e, t, n))];
     case "Tan":
       return [S3(I("x", e, t, n))];
     case "ClipByValue":
@@ -17316,9 +17316,9 @@ var WW = (e, t, n) => {
     case "Prod":
       return [iI(I("x", e, t, n), I("axes", e, t, n))];
     case "LeakyRelu":
-      return [Kg(I("x", e, t, n), I("alpha", e, t, n))];
+      return [jg(I("x", e, t, n), I("alpha", e, t, n))];
     case "Prelu":
-      return [nb(I("x", e, t, n), I("alpha", e, t, n))];
+      return [tb(I("x", e, t, n), I("alpha", e, t, n))];
     case "IsNan":
       return [_R(on(e.inputNames[0], t, n))];
     default:
@@ -17334,20 +17334,20 @@ function Hn(e, t, n = "") {
     }
   }
 }
-function qx(e) {
+function Hx(e) {
   return !(typeof e == "number" || e.some((t) => t < 0));
 }
-function Nu(e, t, n) {
-  let s = Om(e, n), r = !qx(s);
+function Cu(e, t, n) {
+  let s = Fm(e, n), r = !Hx(s);
   if (r && t.length === 0)
     throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);
   if (r && t.forEach((a) => {
-    s = Om(a.shape, s);
-  }), !qx(s))
+    s = Fm(a.shape, s);
+  }), !Hx(s))
     throw new Error(`Non-fully-defined elementShape: ${s}`);
   return s;
 }
-function Om(e, t) {
+function Fm(e, t) {
   if (typeof e == "number")
     return t;
   if (typeof t == "number")
@@ -17475,7 +17475,7 @@ var UW = class {
     this.writeMany(i, a);
   }
 };
-var jl = class {
+var ql = class {
   constructor(e, t, n, s = -1) {
     this.tensors = e, this.elementShape = t, this.elementDtype = n, e != null && e.forEach((r) => {
       if (n !== r.dtype)
@@ -17487,7 +17487,7 @@ var jl = class {
     return this.idTensor.id;
   }
   copy() {
-    return new jl([...this.tensors], this.elementShape, this.elementDtype);
+    return new ql([...this.tensors], this.elementShape, this.elementDtype);
   }
   clearAndClose(e) {
     this.tensors.forEach((t) => {
@@ -17503,7 +17503,7 @@ var jl = class {
     if (n !== -1 && this.tensors.length !== n)
       throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);
     Hn(e, this.elementShape, "TensorList shape mismatch: ");
-    let s = Nu(this.elementShape, this.tensors, e);
+    let s = Cu(this.elementShape, this.tensors, e);
     return j(() => {
       let r = this.tensors.map((a) => G(a, s));
       return Qn(r, 0);
@@ -17514,7 +17514,7 @@ var jl = class {
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     if (this.size() === 0)
       throw new Error("Trying to pop from an empty list.");
-    let n = Nu(this.elementShape, this.tensors, e), s = this.tensors.pop();
+    let n = Cu(this.elementShape, this.tensors, e), s = this.tensors.pop();
     return Hn(s.shape, e, "TensorList shape mismatch: "), G(s, n);
   }
   pushBack(e) {
@@ -17539,7 +17539,7 @@ var jl = class {
     if (this.tensors[e] == null)
       throw new Error(`element at index ${e} is null.`);
     Hn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
-    let s = Nu(this.elementShape, this.tensors, t);
+    let s = Cu(this.elementShape, this.tensors, t);
     return G(this.tensors[e], s);
   }
   setItem(e, t) {
@@ -17553,7 +17553,7 @@ var jl = class {
     if (t !== this.elementDtype)
       throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);
     Hn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
-    let s = Nu(this.elementShape, this.tensors, n);
+    let s = Cu(this.elementShape, this.tensors, n);
     return e.length === 0 ? hs([], [0].concat(s)) : j(() => {
       let r = e.map((a) => G(this.tensors[a], s));
       return Qn(r, 0);
@@ -17563,7 +17563,7 @@ var jl = class {
     if (!!e && e !== this.elementDtype)
       throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
     Hn(this.elementShape, t, "TensorList shape mismatch: ");
-    let n = Nu(this.elementShape, this.tensors, t);
+    let n = Cu(this.elementShape, this.tensors, t);
     return this.size() === 0 ? hs([], [0].concat(n)) : j(() => {
       let s = this.tensors.map((r) => G(r, n));
       return Ft(s, 0);
@@ -17579,10 +17579,10 @@ function GW(e, t, n) {
   let r = e.shape.slice(1);
   Hn(r, t, "TensorList shape mismatch: ");
   let a = Fs(e);
-  return new jl(a, t, s);
+  return new ql(a, t, s);
 }
 function HW(e, t, n) {
-  return new jl([], e, t, n);
+  return new ql([], e, t, n);
 }
 function qW(e, t, n, s) {
   if (t.length !== e.shape[0])
@@ -17590,7 +17590,7 @@ function qW(e, t, n, s) {
   let r = Math.max(...t);
   if (s != null && s !== -1 && r >= s)
     throw new Error(`Max index must be < array size (${r}  vs. ${s})`);
-  let a = new jl([], n, e.dtype, s), i = Fs(e, 0);
+  let a = new ql([], n, e.dtype, s), i = Fs(e, 0);
   return t.forEach((o, u) => {
     a.setItem(o, i[u]);
   }), a;
@@ -17601,7 +17601,7 @@ function jW(e, t, n) {
     throw new Error(`Expected sum of lengths to be equal to
           tensor.shape[0], but sum of lengths is
         ${s}, and tensor's shape is: ${e.shape}`);
-  let a = e.shape.slice(1), i = Om(a, n), o = s === 0 ? 0 : e.size / s, u = j(() => {
+  let a = e.shape.slice(1), i = Fm(a, n), o = s === 0 ? 0 : e.size / s, u = j(() => {
     let c = [];
     e = G(e, [1, s, o]);
     for (let p = 0; p < t.length; ++p) {
@@ -17609,7 +17609,7 @@ function jW(e, t, n) {
       c[p] = G(He(e, h, f), i);
     }
     return e.dispose(), c;
-  }), l = new jl([], n, e.dtype, t.length);
+  }), l = new ql([], n, e.dtype, t.length);
   for (let c = 0; c < u.length; c++)
     l.setItem(c, u[c]);
   return l;
@@ -17758,7 +17758,7 @@ var KW = async (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function jx(e, t, n) {
+function qx(e, t, n) {
   let [s, r] = I("fusedOps", e, t, n), a = s === "biasadd", i = !a, o = r === "prelu", u = s === "fusedbatchnorm", l = I("numArgs", e, t, n);
   if (a) {
     if (o && l !== 2)
@@ -17768,7 +17768,7 @@ function jx(e, t, n) {
   }
   if (u)
     throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
-  let c = I("strides", e, t, n), p = sd(e, t, n), d = I("dataFormat", e, t, n).toUpperCase(), h = I("dilations", e, t, n), [f, m] = I("args", e, t, n);
+  let c = I("strides", e, t, n), p = nd(e, t, n), d = I("dataFormat", e, t, n).toUpperCase(), h = I("dilations", e, t, n), [f, m] = I("args", e, t, n);
   i && (m = f, f = void 0);
   let g = I("leakyreluAlpha", e, t, n);
   return { stride: c, pad: p, dataFormat: d, dilations: h, biasArg: f, preluArg: m, activationFunc: r, leakyreluAlpha: g };
@@ -17780,26 +17780,26 @@ var XW = (e, t, n) => {
       return [qk(I("x", e, t, n), I("filter", e, t, n), s, r, a, i)];
     }
     case "Conv2D": {
-      let s = I("strides", e, t, n), r = sd(e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
+      let s = I("strides", e, t, n), r = nd(e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
       return [ua(I("x", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, a, [i[1], i[2]])];
     }
     case "_FusedConv2D": {
-      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = jx(e, t, n);
+      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = qx(e, t, n);
       return [da.conv2d({ x: I("x", e, t, n), filter: I("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
     }
     case "FusedDepthwiseConv2dNative": {
-      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = jx(e, t, n);
+      let { stride: s, pad: r, dataFormat: a, dilations: i, biasArg: o, preluArg: u, activationFunc: l, leakyreluAlpha: c } = qx(e, t, n);
       return [da.depthwiseConv2d({ x: I("x", e, t, n), filter: I("filter", e, t, n), strides: [s[1], s[2]], pad: r, dataFormat: a, dilations: [i[1], i[2]], bias: o, activation: l, preluActivationWeights: u, leakyreluAlpha: c })];
     }
     case "Conv2DBackpropInput":
     case "Conv2dTranspose": {
-      let s = I("outputShape", e, t, n), r = I("strides", e, t, n), a = sd(e, t, n);
+      let s = I("outputShape", e, t, n), r = I("strides", e, t, n), a = nd(e, t, n);
       return [jk(I("x", e, t, n), I("filter", e, t, n), s, [r[1], r[2]], a)];
     }
     case "DepthwiseConv2dNative":
     case "DepthwiseConv2d": {
-      let s = I("strides", e, t, n), r = sd(e, t, n), a = I("dilations", e, t, n), i = I("dataFormat", e, t, n).toUpperCase();
-      return [hp(I("input", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
+      let s = I("strides", e, t, n), r = nd(e, t, n), a = I("dilations", e, t, n), i = I("dataFormat", e, t, n).toUpperCase();
+      return [pp(I("input", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
     }
     case "Conv3D": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
@@ -17807,11 +17807,11 @@ var XW = (e, t, n) => {
     }
     case "AvgPool": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
-      return [Ug(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
+      return [Wg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
     }
     case "MaxPool": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
-      return [Jg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
+      return [Zg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
     }
     case "MaxPoolWithArgmax": {
       let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n), i = I("includeBatchInIndex", e, t, n), { result: o, indexes: u } = sD(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, i);
@@ -17837,7 +17837,7 @@ var YW = (e, t, n) => {
   switch (e.op) {
     case "Fill": {
       let s = I("shape", e, t, n), r = I("dtype", e, t, n), a = I("value", e, t, n);
-      return [Ol(s, a, r)];
+      return [Fl(s, a, r)];
     }
     case "LinSpace": {
       let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("num", e, t, n);
@@ -17849,21 +17849,21 @@ var YW = (e, t, n) => {
     }
     case "OneHot": {
       let s = I("indices", e, t, n), r = I("depth", e, t, n), a = I("onValue", e, t, n), i = I("offValue", e, t, n);
-      return [vd(s, r, a, i)];
+      return [yd(s, r, a, i)];
     }
     case "Ones":
       return [zn(I("shape", e, t, n), I("dtype", e, t, n))];
     case "OnesLike":
       return [Xn(I("x", e, t, n))];
     case "RandomUniform":
-      return [zl(I("shape", e, t, n), I("minval", e, t, n), I("maxval", e, t, n), I("dtype", e, t, n))];
+      return [Pl(I("shape", e, t, n), I("minval", e, t, n), I("maxval", e, t, n), I("dtype", e, t, n))];
     case "Range": {
       let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("step", e, t, n);
-      return [Yu(s, r, a, I("dtype", e, t, n))];
+      return [Xu(s, r, a, I("dtype", e, t, n))];
     }
     case "TruncatedNormal": {
       let s = I("shape", e, t, n), r = I("mean", e, t, n), a = I("stdDev", e, t, n), i = I("seed", e, t, n);
-      return [cb(s, r, a, I("dtype", e, t, n), i)];
+      return [lb(s, r, a, I("dtype", e, t, n), i)];
     }
     case "Zeros":
       return [$t(I("shape", e, t, n), I("dtype", e, t, n))];
@@ -17873,23 +17873,23 @@ var YW = (e, t, n) => {
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Gf(e, t, n) {
+function Uf(e, t, n) {
   let s = I("boxes", e, t, n), r = I("scores", e, t, n), a = I("maxOutputSize", e, t, n), i = I("iouThreshold", e, t, n), o = I("scoreThreshold", e, t, n), u = I("softNmsSigma", e, t, n);
   return { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u };
 }
 var QW = async (e, t, n) => {
   switch (e.op) {
     case "NonMaxSuppressionV5": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Gf(e, t, n), l = await ds.nonMaxSuppressionWithScoreAsync(s, r, a, i, o, u);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Uf(e, t, n), l = await ds.nonMaxSuppressionWithScoreAsync(s, r, a, i, o, u);
       return [l.selectedIndices, l.selectedScores];
     }
     case "NonMaxSuppressionV4": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Gf(e, t, n), u = I("padToMaxOutputSize", e, t, n), l = await ds.nonMaxSuppressionPaddedAsync(s, r, a, i, o, u);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Uf(e, t, n), u = I("padToMaxOutputSize", e, t, n), l = await ds.nonMaxSuppressionPaddedAsync(s, r, a, i, o, u);
       return [l.selectedIndices, l.validOutputs];
     }
     case "NonMaxSuppressionV3":
     case "NonMaxSuppressionV2": {
-      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Gf(e, t, n);
+      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o } = Uf(e, t, n);
       return [await ds.nonMaxSuppressionAsync(s, r, a, i, o)];
     }
     case "Where": {
@@ -17909,11 +17909,11 @@ var ZW = (e, t, n) => {
       return [i.values, i.indices];
     }
     case "Unique": {
-      let s = I("x", e, t, n), r = dx(s);
+      let s = I("x", e, t, n), r = cx(s);
       return [r.values, r.indices];
     }
     case "UniqueV2": {
-      let s = I("x", e, t, n), r = I("axis", e, t, n), a = dx(s, r);
+      let s = I("x", e, t, n), r = I("axis", e, t, n), a = cx(s, r);
       return [a.values, a.indices];
     }
     default:
@@ -18061,19 +18061,19 @@ var s4 = (e, t, n) => {
     case "Equal":
       return [qn(I("a", e, t, n), I("b", e, t, n))];
     case "NotEqual":
-      return [Xu(I("a", e, t, n), I("b", e, t, n))];
+      return [Ku(I("a", e, t, n), I("b", e, t, n))];
     case "Greater":
       return [Wn(I("a", e, t, n), I("b", e, t, n))];
     case "GreaterEqual":
-      return [Ko(I("a", e, t, n), I("b", e, t, n))];
+      return [jo(I("a", e, t, n), I("b", e, t, n))];
     case "Less":
       return [Jk(I("a", e, t, n), I("b", e, t, n))];
     case "LessEqual":
-      return [Xo(I("a", e, t, n), I("b", e, t, n))];
+      return [Ko(I("a", e, t, n), I("b", e, t, n))];
     case "LogicalAnd":
       return [Ds(I("a", e, t, n), I("b", e, t, n))];
     case "LogicalNot":
-      return [Zg(I("a", e, t, n))];
+      return [Qg(I("a", e, t, n))];
     case "LogicalOr":
       return [rI(I("a", e, t, n), I("b", e, t, n))];
     case "Select":
@@ -18111,13 +18111,13 @@ var a4 = (e, t, n) => {
   switch (e.op) {
     case "FusedBatchNorm":
     case "FusedBatchNormV2":
-      return [ju(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
+      return [qu(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
     case "FusedBatchNormV3":
-      return [ju(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
+      return [qu(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
     case "LRN":
       return [OR(I("x", e, t, n), I("radius", e, t, n), I("bias", e, t, n), I("alpha", e, t, n), I("beta", e, t, n))];
     case "Softmax":
-      return [ob(I("x", e, t, n))];
+      return [ib(I("x", e, t, n))];
     case "LogSoftmax":
       return [eI(I("x", e, t, n))];
     case "SparseToDense":
@@ -18138,7 +18138,7 @@ var i4 = (e, t, n) => {
     }
     case "Min": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
-      return [hm(I("x", e, t, n), i, o)];
+      return [pm(I("x", e, t, n), i, o)];
     }
     case "Sum": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
@@ -18150,11 +18150,11 @@ var i4 = (e, t, n) => {
     }
     case "Any": {
       let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
-      return [dm(I("x", e, t, n), i, o)];
+      return [cm(I("x", e, t, n), i, o)];
     }
     case "ArgMax": {
       let i = I("axis", e, t, n);
-      return [Hu(I("x", e, t, n), i)];
+      return [Gu(I("x", e, t, n), i)];
     }
     case "ArgMin": {
       let i = I("axis", e, t, n);
@@ -18188,11 +18188,11 @@ var o4 = (e, t, n) => {
     }
     case "Gather": {
       let s = I("x", e, t, n), r = I("indices", e, t, n);
-      return [Ku(s, ce(r, "int32"), 0)];
+      return [ju(s, ce(r, "int32"), 0)];
     }
     case "GatherV2": {
       let s = I("axis", e, t, n), r = I("batchDims", e, t, n), a = I("x", e, t, n), i = I("indices", e, t, n);
-      return [Ku(a, ce(i, "int32"), s, r)];
+      return [ju(a, ce(i, "int32"), s, r)];
     }
     case "Reverse": {
       let s = I("dims", e, t, n), r = [];
@@ -18255,17 +18255,17 @@ var o4 = (e, t, n) => {
 var u4 = (e, t, n) => {
   switch (e.op) {
     case "SparseFillEmptyRows": {
-      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = Wc.sparseFillEmptyRows(I("indices", e, t, n), I("values", e, t, n), I("denseShape", e, t, n), I("defaultValue", e, t, n));
+      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = Vc.sparseFillEmptyRows(I("indices", e, t, n), I("values", e, t, n), I("denseShape", e, t, n), I("defaultValue", e, t, n));
       return [s, r, a, i];
     }
     case "SparseReshape": {
-      let { outputIndices: s, outputShape: r } = Wc.sparseReshape(I("inputIndices", e, t, n), I("inputShape", e, t, n), I("newShape", e, t, n));
+      let { outputIndices: s, outputShape: r } = Vc.sparseReshape(I("inputIndices", e, t, n), I("inputShape", e, t, n), I("newShape", e, t, n));
       return [s, r];
     }
     case "SparseSegmentMean":
-      return [Wc.sparseSegmentMean(I("data", e, t, n), I("indices", e, t, n), I("segmentIds", e, t, n))];
+      return [Vc.sparseSegmentMean(I("data", e, t, n), I("indices", e, t, n), I("segmentIds", e, t, n))];
     case "SparseSegmentSum":
-      return [Wc.sparseSegmentSum(I("data", e, t, n), I("indices", e, t, n), I("segmentIds", e, t, n))];
+      return [Vc.sparseSegmentSum(I("data", e, t, n), I("indices", e, t, n), I("segmentIds", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
@@ -18273,11 +18273,11 @@ var u4 = (e, t, n) => {
 var l4 = (e, t, n) => {
   switch (e.op) {
     case "FFT":
-      return [ub(I("x", e, t, n))];
+      return [ob(I("x", e, t, n))];
     case "IFFT":
-      return [Id(I("x", e, t, n))];
+      return [kd(I("x", e, t, n))];
     case "RFFT":
-      return [lb(I("x", e, t, n))];
+      return [ub(I("x", e, t, n))];
     case "IRFFT":
       return [fI(I("x", e, t, n))];
     default:
@@ -18287,15 +18287,15 @@ var l4 = (e, t, n) => {
 var c4 = (e, t, n) => {
   switch (e.op) {
     case "StringNGrams": {
-      let { nGrams: s, nGramsSplits: r } = zf.stringNGrams(I("data", e, t, n), I("dataSplits", e, t, n), I("separator", e, t, n), I("nGramWidths", e, t, n), I("leftPad", e, t, n), I("rightPad", e, t, n), I("padWidth", e, t, n), I("preserveShortSequences", e, t, n));
+      let { nGrams: s, nGramsSplits: r } = Pf.stringNGrams(I("data", e, t, n), I("dataSplits", e, t, n), I("separator", e, t, n), I("nGramWidths", e, t, n), I("leftPad", e, t, n), I("rightPad", e, t, n), I("padWidth", e, t, n), I("preserveShortSequences", e, t, n));
       return [s, r];
     }
     case "StringSplit": {
-      let { indices: s, values: r, shape: a } = zf.stringSplit(I("input", e, t, n), I("delimiter", e, t, n), I("skipEmpty", e, t, n));
+      let { indices: s, values: r, shape: a } = Pf.stringSplit(I("input", e, t, n), I("delimiter", e, t, n), I("skipEmpty", e, t, n));
       return [s, r, a];
     }
     case "StringToHashBucketFast":
-      return [zf.stringToHashBucketFast(I("input", e, t, n), I("numBuckets", e, t, n))];
+      return [Pf.stringToHashBucketFast(I("input", e, t, n), I("numBuckets", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
@@ -18321,25 +18321,25 @@ var d4 = (e, t, n) => {
       return [pi(I("x", e, t, n), I("padding", e, t, n), I("constantValue", e, t, n))];
     case "SpaceToBatchND": {
       let s = I("blockShape", e, t, n), r = I("paddings", e, t, n);
-      return [tb(I("x", e, t, n), s, r)];
+      return [eb(I("x", e, t, n), s, r)];
     }
     case "BatchToSpaceND": {
       let s = I("blockShape", e, t, n), r = I("crops", e, t, n);
-      return [Gg(I("x", e, t, n), s, r)];
+      return [Ug(I("x", e, t, n), s, r)];
     }
     case "DepthToSpace": {
       let s = I("blockSize", e, t, n), r = I("dataFormat", e, t, n).toUpperCase();
       return [JE(I("x", e, t, n), s, r)];
     }
     case "BroadcastTo":
-      return [nd(I("x", e, t, n), I("shape", e, t, n))];
+      return [td(I("x", e, t, n), I("shape", e, t, n))];
     case "BroadcastArgs":
       return [NE(I("s0", e, t, n), I("s1", e, t, n))];
     default:
       throw TypeError(`Node type ${e.op} is not implemented`);
   }
 };
-function Kx(e, t, n, s) {
+function jx(e, t, n, s) {
   let r = ((a, i, o) => {
     switch (a.category) {
       case "arithmetic":
@@ -18391,7 +18391,7 @@ function Kx(e, t, n, s) {
   })(e, t, n);
   return w.isPromise(r) ? r.then((a) => [].concat(a)) : [].concat(r);
 }
-var Xx = class {
+var Kx = class {
   constructor(e = {}, t = {}, n = {}, s = {}) {
     this.weightMap = e, this.tensorArrayMap = t, this.tensorListMap = n, this.functionMap = s, this.rootContext = { id: 0, frameName: "", iterationId: 0 }, this.contexts = [this.rootContext], this.lastId = 0, this.generateCurrentContextIds();
   }
@@ -18460,7 +18460,7 @@ var Xx = class {
       this.tensorListMap[t].clearAndClose(e);
   }
 };
-function Yx(e, t, n, s) {
+function Xx(e, t, n, s) {
   let r = /* @__PURE__ */ new Set(), a = [], i = null, o = null, u = /* @__PURE__ */ new Set(), l = Object.keys(e).map((d) => $n(d)[0]), c = [];
   s != null && (c = s.map((d) => $n(d.name)[0]));
   let p = [...t];
@@ -18508,10 +18508,10 @@ function g4(e) {
 function b4(e) {
   return m4.indexOf(e.op) >= 0;
 }
-var Pm = class {
+var Om = class {
   constructor(e, t) {
     this.graph = e, this.parent = t, this.compiledMap = /* @__PURE__ */ new Map(), this._weightMap = {}, this.SEPERATOR = ",", this._functions = {}, this._functionExecutorMap = {}, this.intermediateTensors = {}, this.keepTensorForDebug = false, this._outputs = e.outputs, this._inputs = e.inputs, this._initNodes = e.initNodes, this._signature = e.signature, this._functions = e.functions, e.functions != null && Object.keys(e.functions).forEach((n) => {
-      this._functionExecutorMap[n] = new Pm(e.functions[n], this);
+      this._functionExecutorMap[n] = new Om(e.functions[n], this);
     });
   }
   get weightIds() {
@@ -18553,7 +18553,7 @@ var Pm = class {
     return n.join(this.SEPERATOR) + "--" + s.join(this.SEPERATOR);
   }
   compile(e, t) {
-    let n = Yx(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
+    let n = Xx(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
     if (r != null)
       throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);
     if (s.length > 0) {
@@ -18572,7 +18572,7 @@ var Pm = class {
     o == null && (o = this.compile(e, a), this.compiledMap.set(i, o));
     let u = {}, l = {};
     return j(() => {
-      let c = new Xx(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
+      let c = new Kx(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
       Object.keys(e).forEach((f) => {
         let [m, g] = $n(f), b = [];
         b[g] = e[f], p[m] = b;
@@ -18581,7 +18581,7 @@ var Pm = class {
       for (let f = 0; f < o.length; f++) {
         let m = o[f];
         if (!p[m.name]) {
-          let g = Kx(m, p, c, this._resourceManager);
+          let g = jx(m, p, c, this._resourceManager);
           if (w.isPromise(g))
             throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);
           p[m.name] = g, this.checkTensorForDisposal(m.name, m, p, c, d, r, h);
@@ -18646,7 +18646,7 @@ var Pm = class {
       console.warn(l.message);
     }
     this.resetIntermediateTensors();
-    let a = new Xx(this.weightMap, s, r, this.functionExecutorMap);
+    let a = new Kx(this.weightMap, s, r, this.functionExecutorMap);
     this.tensorsMap = await this.executeWithControlFlow(e, a, t, n);
     let i = t.map((l) => on(l, this.tensorsMap, a)), o = i.map((l) => l.id), u = Object.keys(e).map((l) => e[l].id);
     return this.keepIds = /* @__PURE__ */ new Set([...o, ...u, ...this.weightIds]), this.keepTensorForDebug || this.disposeTensorsMap(), this.parent == null && a.dispose(this.keepIds), i;
@@ -18658,7 +18658,7 @@ var Pm = class {
   async executeWithControlFlow(e, t, n, s) {
     let r = Object.keys(e), a = r.map((y) => this.graph.nodes[$n(y)[0]]), i = n.map((y) => $n(y)[0]), o = i.map((y) => this.graph.nodes[y]);
     o.length === 0 && (o = this._outputs);
-    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = Yx(e, o, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
+    let { usedNodes: u, missingInputs: l, dynamicNode: c, syncInputs: p } = Xx(e, o, this.weightMap, this._initNodes), d = [...a, ...this.graph.weights, ...this._initNodes || []].map((y) => ({ node: y, contexts: t.currentContext })), h = { ...this.weightMap };
     Object.keys(e).forEach((y) => {
       let [v, x] = $n(y), k = [];
       k[x] = e[y], h[v] = k;
@@ -18683,7 +18683,7 @@ var Pm = class {
       n.currentContext = c.contexts;
       let p = "";
       if (c.node.op === "Enter" && I("isConstant", c.node, s, n) && ([p] = Ts(c.node.name, n)), s[c.node.name] == null) {
-        let d = Kx(c.node, s, n, this._resourceManager);
+        let d = jx(c.node, s, n, this._resourceManager);
         p || ([p] = Ts(c.node.name, n));
         let h = n.currentContext;
         w.isPromise(d) ? l.push(d.then((f) => (s[p] = f, n.currentContext = h, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u), f))) : (s[p] = d, this.checkTensorForDisposal(p, c.node, s, n, a, i, o), this.processChildNodes(c.node, t, n, s, r, u));
@@ -18816,9 +18816,9 @@ var w4 = class {
     let t = this.artifacts.modelTopology, n;
     this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null ? n = this.artifacts.userDefinedMetadata.signature : n = this.artifacts.signature, this.signature = n, this.version = `${t.versions.producer}.${t.versions.minConsumer}`;
     let s = _n.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
-    if (this.executor = new Pm(Gx.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
-      let r = Gx.Instance.transformGraph(e.modelInitializer);
-      this.initializer = new Pm(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
+    if (this.executor = new Om(Ux.Instance.transformGraph(t, this.signature)), this.executor.weightMap = this.convertTensorMapToTensorsMap(s), this.executor.resourceManager = this.resourceManager, e.modelInitializer != null && e.modelInitializer.node != null) {
+      let r = Ux.Instance.transformGraph(e.modelInitializer);
+      this.initializer = new Om(r), this.initializer.weightMap = this.executor.weightMap, this.initializer.resourceManager = this.resourceManager, this.initializer.executeAsync({}, []);
     }
     return true;
   }
@@ -18880,13 +18880,13 @@ async function bpe(e, t = {}) {
 }
 var ype = "0.0.0";
 var k4 = {};
-Ae(k4, { CSVDataset: () => kS, Dataset: () => Jo, FileDataSource: () => _S, TextLineDataset: () => wS, URLDataSource: () => AS, array: () => H4, csv: () => nU, func: () => sU, generator: () => rU, microphone: () => iU, version_data: () => oU, webcam: () => aU, zip: () => q4 });
-var I4 = ya(Wd());
-var S4 = ya(Wd());
+Ae(k4, { CSVDataset: () => kS, Dataset: () => Zo, FileDataSource: () => _S, TextLineDataset: () => wS, URLDataSource: () => AS, array: () => H4, csv: () => nU, func: () => sU, generator: () => rU, microphone: () => iU, version_data: () => oU, webcam: () => aU, zip: () => q4 });
+var I4 = ya(Vd());
+var S4 = ya(Vd());
 function C4(e, t) {
-  return Rd(e, t);
+  return Ed(e, t);
 }
-function Rd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set()) {
+function Ed(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set()) {
   if (e == null)
     return null;
   if (typeof Blob == "function" && e instanceof Blob)
@@ -18899,11 +18899,11 @@ function Rd(e, t, n = /* @__PURE__ */ new Map(), s = /* @__PURE__ */ new Set())
   if (r.recurse && r.value !== null)
     throw new Error("A deep map function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (eo(e)) {
+    if (Ji(e)) {
       let a = Array.isArray(e) ? [] : {};
       s.add(e);
       for (let i in e) {
-        let o = e[i], u = Rd(o, t, n, s);
+        let o = e[i], u = Ed(o, t, n, s);
         a[i] = u;
       }
       return s.delete(e), e.__proto__ && (a.__proto__ = e.__proto__), a;
@@ -18923,7 +18923,7 @@ function dS(e, t, n = /* @__PURE__ */ new Set()) {
   if (r.recurse && r.value !== null)
     throw new Error("A deep zip function may not return both a value and recurse=true.");
   if (r.recurse)
-    if (eo(s)) {
+    if (Ji(s)) {
       let a = Array.isArray(s) ? [] : {};
       n.add(s);
       for (let i in s) {
@@ -18937,11 +18937,11 @@ function dS(e, t, n = /* @__PURE__ */ new Set()) {
     return r.value;
 }
 function pS(e) {
-  return e === null ? null : eo(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
+  return e === null ? null : Ji(e[0]) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
 async function hS(e, t) {
   let n = /* @__PURE__ */ new Map();
-  Rd(e, t, n);
+  Ed(e, t, n);
   for (let r of Array.from(n.keys())) {
     let a = n.get(r);
     if (w.isPromise(a)) {
@@ -18949,9 +18949,9 @@ async function hS(e, t) {
       n.set(r, i);
     }
   }
-  return Rd(e, t, n);
+  return Ed(e, t, n);
 }
-function eo(e) {
+function Ji(e) {
   let t = false;
   if (X().get("IS_BROWSER"))
     t = e instanceof TextDecoder;
@@ -18971,7 +18971,7 @@ function _4(e) {
   return C4(e, A4);
 }
 function A4(e) {
-  return e instanceof et ? { value: e.clone(), recurse: false } : eo(e) ? { value: null, recurse: true } : { value: e, recurse: false };
+  return e instanceof et ? { value: e.clone(), recurse: false } : Ji(e) ? { value: null, recurse: true } : { value: e, recurse: false };
 }
 var fS = class {
   constructor(e) {
@@ -19065,7 +19065,7 @@ gS.INITIAL_CAPACITY = 32;
 function bS(e) {
   return new D4(e);
 }
-function Xy(e) {
+function Ky(e) {
   return new F4(e);
 }
 function E4(e, t) {
@@ -19107,10 +19107,10 @@ var Ut = class {
     return new B4(this, e);
   }
   mapAsync(e) {
-    return new Qx(this, e);
+    return new Yx(this, e);
   }
   serialMapAsync(e) {
-    return new Qx(this, e).serial();
+    return new Yx(this, e).serial();
   }
   flatmap(e) {
     return new W4(this, e);
@@ -19306,7 +19306,7 @@ var V4 = class extends Ut {
       }
   }
 };
-var Qx = class extends Ut {
+var Yx = class extends Ut {
   constructor(e, t) {
     super();
     this.upstream = e, this.transform = t;
@@ -19324,7 +19324,7 @@ var Qx = class extends Ut {
     return { value: n, done: false };
   }
 };
-var Yy = class extends Ut {
+var Xy = class extends Ut {
   constructor() {
     super();
     this.outputQueue = new gS(), this.lastRead = Promise.resolve({ value: null, done: false });
@@ -19339,7 +19339,7 @@ var Yy = class extends Ut {
     return { value: this.outputQueue.shift(), done: false };
   }
 };
-var W4 = class extends Yy {
+var W4 = class extends Xy {
   constructor(e, t) {
     super();
     this.upstream = e, this.transform = t;
@@ -19456,7 +19456,7 @@ var G4 = class extends xS {
     return { value: null, done: true };
   }
 };
-var Jo = class {
+var Zo = class {
   constructor() {
     this.size = null;
   }
@@ -19495,7 +19495,7 @@ var Jo = class {
   repeat(e) {
     let t = this, n;
     return this.size != null && e > 0 ? n = this.size * e : e === 0 ? n = 0 : this.size != null && (e === void 0 || e < 0) ? n = 1 / 0 : n = null, Tn(async () => {
-      let s = Xy(async () => ({ value: await t.iterator(), done: false }));
+      let s = Ky(async () => ({ value: await t.iterator(), done: false }));
       return E4(s.take(e));
     }, n);
   }
@@ -19527,9 +19527,9 @@ var Jo = class {
     return (await this.iterator()).toArrayForTest();
   }
 };
-Jo.MAX_BUFFER_SIZE = 1e4;
+Zo.MAX_BUFFER_SIZE = 1e4;
 function Tn(e, t = null) {
-  return new class extends Jo {
+  return new class extends Zo {
     constructor() {
       super(...arguments);
       this.size = t;
@@ -19543,7 +19543,7 @@ function H4(e) {
   return Tn(async () => bS(e), e.length);
 }
 function q4(e) {
-  if (!eo(e))
+  if (!Ji(e))
     throw new Error("The argument to zip() must be an object or array.");
   let t;
   if (Array.isArray(e))
@@ -19554,9 +19554,9 @@ function q4(e) {
       t = t == null ? e[n].size : Math.min(t, e[n].size);
   return Tn(async () => {
     let n = await hS(e, (s) => {
-      if (s instanceof Jo)
+      if (s instanceof Zo)
         return { value: s.iterator(), recurse: false };
-      if (eo(s))
+      if (Ji(s))
         return { value: null, recurse: true };
       throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
     });
@@ -19574,7 +19574,7 @@ function K4(e) {
     throw new Error("Can't make a batch of zero elements.");
   return e[0] instanceof et ? Qn(e) : hs(e);
 }
-var wS = class extends Jo {
+var wS = class extends Zo {
   constructor(e) {
     super();
     this.input = e;
@@ -19584,13 +19584,13 @@ var wS = class extends Jo {
 `).map((s) => (s.endsWith("\r") && (s = s.slice(0, -1)), s));
   }
 };
-var Kc = '"';
-var Tu = Symbol("out");
-var Zx = Symbol("field");
-var Xc = Symbol("quote");
-var Hf = Symbol("quoteafterquote");
-var Jx = Symbol("quoteinquote");
-var kS = class extends Jo {
+var jc = '"';
+var Nu = Symbol("out");
+var Qx = Symbol("field");
+var Kc = Symbol("quote");
+var Gf = Symbol("quoteafterquote");
+var Zx = Symbol("quoteinquote");
+var kS = class extends Zo {
   constructor(e, t) {
     super();
     this.input = e, this.hasHeader = true, this.fullColumnNames = null, this.columnNamesValidated = false, this.columnConfigs = null, this.configuredColumnsOnly = false, this.delimiter = ",", this.delimWhitespace = false, this.base = new wS(e), t || (t = {}), this.hasHeader = t.hasHeader !== false, this.fullColumnNames = t.columnNames, this.columnConfigs = t.columnConfigs, this.configuredColumnsOnly = t.configuredColumnsOnly, t.delimWhitespace ? (w.assert(t.delimiter == null, () => "Delimiter should not be provided when delimWhitespace is true."), this.delimWhitespace = true, this.delimiter = " ") : this.delimiter = t.delimiter ? t.delimiter : ",";
@@ -19670,64 +19670,64 @@ var kS = class extends Jo {
     return e === "1" || e.toLowerCase() === "true" ? 1 : 0;
   }
   parseRow(e, t = true) {
-    let n = [], s = 0, r = e.length, a = Tu;
+    let n = [], s = 0, r = e.length, a = Nu;
     for (let i = 0; i < r; i++)
       switch (a) {
-        case Tu:
+        case Nu:
           switch (e.charAt(i)) {
-            case Kc:
-              s = i + 1, a = Xc;
+            case jc:
+              s = i + 1, a = Kc;
               break;
             case this.delimiter:
               if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
                 break;
-              n.push(""), a = Tu;
+              n.push(""), a = Nu;
               break;
             default:
-              a = Zx, s = i;
+              a = Qx, s = i;
+              break;
+          }
+          break;
+        case Qx:
+          switch (e.charAt(i)) {
+            case this.delimiter:
+              n.push(e.substring(s, i)), a = Nu, s = i + 1;
+              break;
+            default:
+          }
+          break;
+        case Kc:
+          switch (e.charAt(i)) {
+            case jc:
+              a = Gf;
+              break;
+            default:
+          }
+          break;
+        case Gf:
+          switch (e.charAt(i)) {
+            case this.delimiter:
+              n.push(e.substring(s, i - 1)), a = Nu, s = i + 1;
+              break;
+            case jc:
+              a = Kc;
+              break;
+            default:
+              a = Zx;
               break;
           }
           break;
         case Zx:
           switch (e.charAt(i)) {
-            case this.delimiter:
-              n.push(e.substring(s, i)), a = Tu, s = i + 1;
-              break;
-            default:
-          }
-          break;
-        case Xc:
-          switch (e.charAt(i)) {
-            case Kc:
-              a = Hf;
-              break;
-            default:
-          }
-          break;
-        case Hf:
-          switch (e.charAt(i)) {
-            case this.delimiter:
-              n.push(e.substring(s, i - 1)), a = Tu, s = i + 1;
-              break;
-            case Kc:
-              a = Xc;
-              break;
-            default:
-              a = Jx;
-              break;
-          }
-          break;
-        case Jx:
-          switch (e.charAt(i)) {
-            case Kc:
-              a = Xc;
+            case jc:
+              a = Kc;
               break;
             default:
           }
           break;
         default:
       }
-    if (a === Hf ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
+    if (a === Gf ? n.push(e.substring(s, r - 1)) : n.push(e.substring(s)), t && n.length !== this.fullColumnNames.length)
       throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);
     return n;
   }
@@ -19816,9 +19816,9 @@ var SS = class extends Ut {
     if (this.webcamVideoElement = e, this.webcamConfig = t, this.isClosed = true, this.resize = false, this.needToResize())
       if (this.resize = true, this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth], this.cropBoxInd = Qt([0], "int32"), this.webcamConfig.centerCrop) {
         let n = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width, s = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height, r = (1 - n) / 2, a = (1 - s) / 2, i = r + n, o = s + a;
-        this.cropBox = Ki([a, r, o, i], [1, 4]);
+        this.cropBox = ji([a, r, o, i], [1, 4]);
       } else
-        this.cropBox = Ki([0, 0, 1, 1], [1, 4]);
+        this.cropBox = ji([0, 0, 1, 1], [1, 4]);
   }
   summary() {
     return "webcam";
@@ -19920,7 +19920,7 @@ var X4 = class extends NS {
     return this.impl.next();
   }
 };
-var Y4 = class extends Yy {
+var Y4 = class extends Xy {
   constructor(e, t) {
     super();
     this.upstream = e, this.separator = t, this.carryover = "";
@@ -19956,7 +19956,7 @@ var Z4 = class extends NS {
     return this.impl.next();
   }
 };
-var J4 = class extends Yy {
+var J4 = class extends Xy {
   constructor(e) {
     super();
     if (this.upstream = e, X().get("IS_BROWSER"))
@@ -20027,7 +20027,7 @@ var _S = class extends CS {
   }
   async iterator() {
     if ($S(this.input) && X().get("IS_NODE")) {
-      let e = eg();
+      let e = Jm();
       this.input = e.readFileSync(this.input.substr(7));
     }
     return new TS(this.input, this.options);
@@ -20046,13 +20046,13 @@ function nU(e, t = {}) {
   return new kS(new AS(e), t);
 }
 function sU(e) {
-  let t = Xy(e);
+  let t = Ky(e);
   return Tn(async () => t);
 }
 function rU(e) {
   return Tn(async () => {
     let t = await e();
-    return Xy(() => t.next());
+    return Ky(() => t.next());
   });
 }
 async function aU(e, t) {
@@ -20068,10 +20068,10 @@ function ve(e, t) {
   });
 }
 var uU = xs.whereImpl;
-var ES = class extends nl {
+var ES = class extends tl {
   constructor() {
     super();
-    this.blockSize = 48, this.firstUse = true, this.data = new Ud(this, Ss());
+    this.blockSize = 48, this.firstUse = true, this.data = new Wd(this, Ss());
   }
   nextDataId() {
     return ES.nextDataId++;
@@ -20172,8 +20172,8 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 };
 var RS = ES;
 RS.nextDataId = 0;
-var Qy = {};
-Ae(Qy, { addImpl: () => FS, bincountImpl: () => Jy, bincountReduceImpl: () => OS, ceilImpl: () => PS, concatImpl: () => ev, equalImpl: () => zS, expImpl: () => LS, expm1Impl: () => VS, floorImpl: () => WS, gatherNdImpl: () => US, gatherV2Impl: () => GS, greaterEqualImpl: () => qS, greaterImpl: () => HS, lessEqualImpl: () => KS, lessImpl: () => jS, linSpaceImpl: () => XS, logImpl: () => YS, maxImpl: () => QS, maximumImpl: () => ZS, minimumImpl: () => JS, multiplyImpl: () => tv, negImpl: () => eC, notEqualImpl: () => tC, prodImpl: () => nC, rangeImpl: () => sv, rsqrtImpl: () => sC, sigmoidImpl: () => YU, simpleAbsImpl: () => DS, sliceImpl: () => Fd, sparseFillEmptyRowsImpl: () => aC, sparseReshapeImpl: () => iC, sparseSegmentReductionImpl: () => rv, sqrtImpl: () => JU, squaredDifferenceImpl: () => oC, stridedSliceImpl: () => uC, stringNGramsImpl: () => lC, stringSplitImpl: () => cC, stringToHashBucketFastImpl: () => dC, subImpl: () => pC, tileImpl: () => hC, topKImpl: () => mC, transposeImpl: () => nv, uniqueImpl: () => gC });
+var Yy = {};
+Ae(Yy, { addImpl: () => FS, bincountImpl: () => Zy, bincountReduceImpl: () => OS, ceilImpl: () => PS, concatImpl: () => Jy, equalImpl: () => zS, expImpl: () => LS, expm1Impl: () => VS, floorImpl: () => WS, gatherNdImpl: () => US, gatherV2Impl: () => GS, greaterEqualImpl: () => qS, greaterImpl: () => HS, lessEqualImpl: () => KS, lessImpl: () => jS, linSpaceImpl: () => XS, logImpl: () => YS, maxImpl: () => QS, maximumImpl: () => ZS, minimumImpl: () => JS, multiplyImpl: () => ev, negImpl: () => eC, notEqualImpl: () => tC, prodImpl: () => nC, rangeImpl: () => nv, rsqrtImpl: () => sC, sigmoidImpl: () => YU, simpleAbsImpl: () => DS, sliceImpl: () => Dd, sparseFillEmptyRowsImpl: () => aC, sparseReshapeImpl: () => iC, sparseSegmentReductionImpl: () => sv, sqrtImpl: () => JU, squaredDifferenceImpl: () => oC, stridedSliceImpl: () => uC, stringNGramsImpl: () => lC, stringSplitImpl: () => cC, stringToHashBucketFastImpl: () => dC, subImpl: () => pC, tileImpl: () => hC, topKImpl: () => mC, transposeImpl: () => tv, uniqueImpl: () => gC });
 function DS(e) {
   let t = new Float32Array(e.length);
   for (let n = 0; n < e.length; ++n)
@@ -20186,7 +20186,7 @@ var lU = (e) => {
   let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
   return s = DS(r), n.makeOutput(s, t.shape, t.dtype);
 };
-var cU = { kernelName: io, backendName: "cpu", kernelFunc: lU };
+var cU = { kernelName: ao, backendName: "cpu", kernelFunc: lU };
 function At(e) {
   return (t, n, s, r, a) => {
     let i = N.assertAndGetBroadcastShape(t, n), o = i.length, u = w.computeStrides(i), l = w.sizeFromShape(i), c = w.getTypedArrayFromDType(a, l), p = t.length, d = n.length, h = w.computeStrides(t), f = w.computeStrides(n), m = N.getBroadcastDims(t, i), g = N.getBroadcastDims(n, i);
@@ -20209,10 +20209,10 @@ function An(e) {
   let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = n.makeTensorInfo(s.shape, "complex64"), u = n.data.get(o.dataId);
   return u.complexTensorInfos = { real: n.makeTensorInfo(s.shape, "float32", a), imag: n.makeTensorInfo(r.shape, "float32", i) }, o;
 }
-var dU = { kernelName: jd, backendName: "cpu", kernelFunc: An };
-function Dd(e, t, n = "float32") {
+var dU = { kernelName: qd, backendName: "cpu", kernelFunc: An };
+function Rd(e, t, n = "float32") {
   if (n === "complex64") {
-    let r = Dd(e, t, "float32"), a = Dd(e, t, "float32");
+    let r = Rd(e, t, "float32"), a = Rd(e, t, "float32");
     return An({ inputs: { real: r, imag: a }, backend: e });
   }
   let s = w.makeZerosTypedArray(w.sizeFromShape(t), n);
@@ -20227,13 +20227,13 @@ function ha(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.real, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var hU = { kernelName: np, backendName: "cpu", kernelFunc: ha };
+var hU = { kernelName: tp, backendName: "cpu", kernelFunc: ha };
 function xr(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return Os({ inputs: { x: r }, backend: n });
-    let i = Dd(n, r.shape, r.dtype), o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = An({ inputs: { real: o, imag: i }, backend: n });
+    let i = Rd(n, r.shape, r.dtype), o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = An({ inputs: { real: o, imag: i }, backend: n });
     return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   }
   if (r.dtype === "complex64") {
@@ -20272,7 +20272,7 @@ function Gt(e, t, n, s) {
     }
   };
 }
-function Zy(e) {
+function Qy(e) {
   return (t, n, s, r, a, i) => {
     let o = N.assertAndGetBroadcastShape(t, n), u = w.sizeFromShape(o), l = o.length, c = w.computeStrides(o), p = w.getTypedArrayFromDType("float32", u), d = w.getTypedArrayFromDType("float32", u), h = N.getBroadcastDims(t, o), f = N.getBroadcastDims(n, o), m = N.mergeRealAndImagArrays(s, r), g = N.mergeRealAndImagArrays(a, i), b = t.length, y = w.computeStrides(t), v = n.length, x = w.computeStrides(n);
     if (h.length + f.length === 0)
@@ -20293,10 +20293,10 @@ function Zy(e) {
   };
 }
 var FS = At((e, t) => e + t);
-var mU = Zy((e, t, n, s) => ({ real: e + n, imag: t + s }));
-var Kl = Gt(kr, FS, mU);
-var gU = { kernelName: kr, backendName: "cpu", kernelFunc: Kl };
-function Jy(e, t, n, s, r) {
+var mU = Qy((e, t, n, s) => ({ real: e + n, imag: t + s }));
+var jl = Gt(kr, FS, mU);
+var gU = { kernelName: kr, backendName: "cpu", kernelFunc: jl };
+function Zy(e, t, n, s, r) {
   let a = w.sizeFromShape(s), i = w.makeZerosTypedArray(r, n);
   for (let o = 0; o < e.length; o++) {
     let u = e[o];
@@ -20336,7 +20336,7 @@ function st(e, t, n) {
     return o.makeTensorInfo(i.shape, c, p);
   };
 }
-function eu(e, t, n) {
+function Jo(e, t, n) {
   return ({ inputs: s, attrs: r, backend: a }) => {
     let { x: i } = s;
     if (ve(i, e), i.dtype === "string" || n === "string")
@@ -20346,9 +20346,9 @@ function eu(e, t, n) {
   };
 }
 var PS = Ar((e) => Math.ceil(e));
-var bU = eu(Ca, PS);
+var bU = Jo(Ca, PS);
 var yU = { kernelName: Ca, backendName: "cpu", kernelFunc: bU };
-function ev(e, t, n, s) {
+function Jy(e, t, n, s) {
   let r = w.getArrayFromDType(n, w.sizeFromShape(t));
   if (s && n !== "string") {
     let a = 0;
@@ -20371,16 +20371,16 @@ function ev(e, t, n, s) {
   return r;
 }
 var zS = At((e, t) => e === t ? 1 : 0);
-var MS = Gt(ho, zS, null, "bool");
-var vU = { kernelName: ho, backendName: "cpu", kernelFunc: MS };
+var MS = Gt(po, zS, null, "bool");
+var vU = { kernelName: po, backendName: "cpu", kernelFunc: MS };
 var LS = Ar((e) => Math.exp(e));
-var BS = eu(Da, LS, "float32");
+var BS = Jo(Da, LS, "float32");
 var xU = { kernelName: Da, backendName: "cpu", kernelFunc: BS };
 var VS = Ar((e) => Math.expm1(e));
-var wU = eu(mo, VS);
-var kU = { kernelName: mo, backendName: "cpu", kernelFunc: wU };
+var wU = Jo(fo, VS);
+var kU = { kernelName: fo, backendName: "cpu", kernelFunc: wU };
 var WS = Ar((e) => Math.floor(e));
-var IU = eu(Fa, WS);
+var IU = Jo(Fa, WS);
 var SU = { kernelName: Fa, backendName: "cpu", kernelFunc: IU };
 function US(e, t, n, s, r, a, i, o, u) {
   let l = De([s, a], n);
@@ -20408,17 +20408,17 @@ function GS(e, t, n) {
   return s;
 }
 var HS = At((e, t) => e > t ? 1 : 0);
-var CU = Gt(vo, HS, null, "bool");
-var NU = { kernelName: vo, backendName: "cpu", kernelFunc: CU };
+var CU = Gt(yo, HS, null, "bool");
+var NU = { kernelName: yo, backendName: "cpu", kernelFunc: CU };
 var qS = At((e, t) => e >= t ? 1 : 0);
 var TU = Gt(za, qS, null, "bool");
 var $U = { kernelName: za, backendName: "cpu", kernelFunc: TU };
 var jS = At((e, t) => e < t ? 1 : 0);
-var _U = Gt(xo, jS, null, "bool");
-var AU = { kernelName: xo, backendName: "cpu", kernelFunc: _U };
+var _U = Gt(vo, jS, null, "bool");
+var AU = { kernelName: vo, backendName: "cpu", kernelFunc: _U };
 var KS = At((e, t) => e <= t ? 1 : 0);
-var EU = Gt(wo, KS, null, "bool");
-var RU = { kernelName: wo, backendName: "cpu", kernelFunc: EU };
+var EU = Gt(xo, KS, null, "bool");
+var RU = { kernelName: xo, backendName: "cpu", kernelFunc: EU };
 function XS(e, t, n) {
   let s = (t - e) / (n - 1), r = w.makeZerosTypedArray(n, "float32");
   r[0] = e;
@@ -20427,7 +20427,7 @@ function XS(e, t, n) {
   return r;
 }
 var YS = Ar((e) => Math.log(e));
-var DU = eu(Ba, YS);
+var DU = Jo(Ba, YS);
 var FU = { kernelName: Ba, backendName: "cpu", kernelFunc: DU };
 function QS(e, t, n, s) {
   let r = w.getTypedArrayFromDType(s, w.sizeFromShape(n));
@@ -20447,13 +20447,13 @@ var PU = { kernelName: Wa, backendName: "cpu", kernelFunc: OU };
 var JS = At((e, t) => Math.min(e, t));
 var zU = Gt(qa, JS);
 var MU = { kernelName: qa, backendName: "cpu", kernelFunc: zU };
-var tv = At((e, t) => e * t);
-var LU = Zy((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
-var Hp = Gt(Ka, tv, LU);
-var BU = { kernelName: Ka, backendName: "cpu", kernelFunc: Hp };
+var ev = At((e, t) => e * t);
+var LU = Qy((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
+var Gp = Gt(Ka, ev, LU);
+var BU = { kernelName: Ka, backendName: "cpu", kernelFunc: Gp };
 function eC(e, t, n) {
   let s = w.createScalarValue(-1, n);
-  return tv([], t, s, e, n);
+  return ev([], t, s, e, n);
 }
 function VU(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
@@ -20461,11 +20461,11 @@ function VU(e) {
   let r = n.data.get(s.dataId).values, [a, i] = eC(r, s.shape, s.dtype);
   return n.makeTensorInfo(i, s.dtype, a);
 }
-var WU = { kernelName: Io, backendName: "cpu", kernelFunc: VU };
+var WU = { kernelName: ko, backendName: "cpu", kernelFunc: VU };
 var tC = At((e, t) => e !== t ? 1 : 0);
-var UU = Gt(So, tC, null, "bool");
-var GU = { kernelName: So, backendName: "cpu", kernelFunc: UU };
-function nv(e, t, n, s, r) {
+var UU = Gt(Io, tC, null, "bool");
+var GU = { kernelName: Io, backendName: "cpu", kernelFunc: UU };
+function tv(e, t, n, s, r) {
   let a = t.length, i = w.sizeFromShape(t), o = w.computeStrides(t), u = w.computeStrides(r), l = w.getTypedArrayFromDType(n, w.sizeFromShape(r));
   for (let c = 0; c < i; ++c) {
     let p = w.indexToLoc(c, a, o), d = new Array(p.length);
@@ -20482,7 +20482,7 @@ function Vn(e) {
   let i = r.shape.length, o = new Array(i);
   for (let p = 0; p < o.length; p++)
     o[p] = r.shape[a[p]];
-  let u = s.data.get(r.dataId).values, l = nv(u, r.shape, r.dtype, a, o);
+  let u = s.data.get(r.dataId).values, l = tv(u, r.shape, r.dtype, a, o);
   return { dataId: s.write(l, o, r.dtype), shape: o, dtype: r.dtype };
 }
 var HU = { kernelName: ci, backendName: "cpu", kernelFunc: Vn };
@@ -20504,8 +20504,8 @@ function qU(e) {
   let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = nC(p.shape, p.dtype, h, c), b = m;
   return i && (b = N.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
 }
-var jU = { kernelName: Ao, backendName: "cpu", kernelFunc: qU };
-function sv(e, t, n, s) {
+var jU = { kernelName: _o, backendName: "cpu", kernelFunc: qU };
+function nv(e, t, n, s) {
   let r = e === t, a = e < t && n < 0, i = t < e && n > 1;
   if (r || a || i)
     return w.makeZerosTypedArray(0, s);
@@ -20516,12 +20516,12 @@ function sv(e, t, n, s) {
   return u;
 }
 var sC = Ar((e) => 1 / Math.sqrt(e));
-var KU = eu(ti, sC);
+var KU = Jo(ti, sC);
 var XU = { kernelName: ti, backendName: "cpu", kernelFunc: KU };
 var YU = Ar((e) => 1 / (1 + Math.exp(-e)));
 var rC = st(si, (e) => 1 / (1 + Math.exp(-e)));
 var QU = { kernelName: si, backendName: "cpu", kernelFunc: rC };
-function Fd(e, t, n, s, r) {
+function Dd(e, t, n, s, r) {
   let a = wt.isSliceContinous(s, t, n), i = w.sizeFromShape(n), o = w.computeStrides(s);
   if (a) {
     let p = wt.computeFlatOffset(t, o);
@@ -20539,10 +20539,10 @@ function fa(e) {
   ve(r, "slice");
   let [o, u] = wt.parseSliceParams(r, a, i);
   wt.assertParamsValid(r, o, u);
-  let l = n.data.get(r.dataId).values, c = Fd(l, o, u, r.shape, r.dtype);
+  let l = n.data.get(r.dataId).values, c = Dd(l, o, u, r.shape, r.dtype);
   return n.makeTensorInfo(u, r.dtype, c);
 }
-var ZU = { kernelName: Po, backendName: "cpu", kernelFunc: fa };
+var ZU = { kernelName: Oo, backendName: "cpu", kernelFunc: fa };
 function aC(e, t, n, s, r, a, i) {
   let o = t[0], u = a[0], l = new Array(u), c = new Array(o), p = t[1];
   if (u === 0) {
@@ -20636,7 +20636,7 @@ function iC(e, t, n, s, r) {
   }
   return [m, [i, o], u];
 }
-function rv(e, t, n, s, r, a = false, i = 0) {
+function sv(e, t, n, s, r, a = false, i = 0) {
   let o = s.length, u = [t[0], e.length / t[0]], l = u[1], p = o > 0 ? r[o - 1] + 1 : 0;
   if (p < 0)
     throw new Error(N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
@@ -20823,9 +20823,9 @@ function dC(e, t) {
   return n;
 }
 var pC = At((e, t) => e - t);
-var iG = Zy((e, t, n, s) => ({ real: e - n, imag: t - s }));
-var av = Gt(ui, pC, iG);
-var oG = { kernelName: ui, backendName: "cpu", kernelFunc: av };
+var iG = Qy((e, t, n, s) => ({ real: e - n, imag: t - s }));
+var rv = Gt(ui, pC, iG);
+var oG = { kernelName: ui, backendName: "cpu", kernelFunc: rv };
 function hC(e, t) {
   let n = new Array(e.rank);
   for (let r = 0; r < n.length; r++)
@@ -20840,7 +20840,7 @@ function hC(e, t) {
   }
   return s;
 }
-var Ru = (e, t) => {
+var Eu = (e, t) => {
   let n = t.value - e.value;
   return n === 0 ? e.index - t.index : n;
 };
@@ -20851,20 +20851,20 @@ function fC(e, t, n = 0, s = e.length - 1) {
       fC(e, t, d, h);
     }
     let r = e[t], a = n, i = s;
-    for (w.swap(e, n, t), Ru(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
-      for (w.swap(e, a, i), a++, i--; Ru(e[a], r) < 0; )
+    for (w.swap(e, n, t), Eu(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
+      for (w.swap(e, a, i), a++, i--; Eu(e[a], r) < 0; )
         a = a + 1;
-      for (; Ru(e[i], r) > 0; )
+      for (; Eu(e[i], r) > 0; )
         i = i - 1;
     }
-    Ru(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
+    Eu(e[n], r) === 0 ? w.swap(e, n, i) : (i = i + 1, w.swap(e, i, s)), i <= t && (n = i + 1), t <= i && (s = i - 1);
   }
 }
 function mC(e, t, n, s, r) {
   let a = t[t.length - 1], [i, o] = [e.length / a, a], u = w.getTypedArrayFromDType(n, i * s), l = w.getTypedArrayFromDType("int32", i * s);
   for (let p = 0; p < i; p++) {
     let d = p * o, h = e.subarray(d, d + o), f = new Array(h.length);
-    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (fC(f, s), f = f.slice(0, s)), r && f.sort(Ru);
+    h.forEach((y, v) => f[v] = { value: y, index: v }), s < f.length && (fC(f, s), f = f.slice(0, s)), r && f.sort(Eu);
     let m = p * s, g = u.subarray(m, m + s), b = l.subarray(m, m + s);
     for (let y = 0; y < s; y++)
       g[y] = f[y].value, b[y] = f[y].index;
@@ -20910,7 +20910,7 @@ function gC(e, t, n, s) {
   return h[r] = p[1], { outputValues: d.values, outputShape: h, indices: o };
 }
 var vpe = "0.0.0";
-pp("cpu", () => new RS(), 1);
+dp("cpu", () => new RS(), 1);
 var bC = st(Ra, (e) => e >= 0 ? e : Math.exp(e) - 1);
 var uG = { kernelName: Ra, backendName: "cpu", kernelFunc: bC };
 function yC(e) {
@@ -20934,7 +20934,7 @@ var xC = st(Za, (e) => Math.max(0, e));
 var pG = { kernelName: Za, backendName: "cpu", kernelFunc: xC };
 var wC = st(ei, (e) => Math.min(Math.max(0, e), 6));
 var hG = { kernelName: ei, backendName: "cpu", kernelFunc: wC };
-function iv(e, t, n, s, r) {
+function av(e, t, n, s, r) {
   if (n === "linear")
     return Os({ inputs: { x: t }, backend: e });
   if (n === "relu")
@@ -20961,11 +20961,11 @@ function mt(e) {
   }
   return { dataId: r.dataId, shape: o, dtype: r.dtype };
 }
-var fG = { kernelName: Eo, backendName: "cpu", kernelFunc: mt };
+var fG = { kernelName: Ao, backendName: "cpu", kernelFunc: mt };
 function kC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   ve([r, a], "matMul");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
   w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
   let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], C = mt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), T = mt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = i ? C.shape[1] : C.shape[2], A = i ? C.shape[2] : C.shape[1], P = o ? T.shape[1] : T.shape[2], R = Math.max(g, b), F = n.data.get(C.dataId).values, $ = n.data.get(T.dataId).values, z = w.computeStrides(C.shape), W = w.computeStrides(T.shape), [q, K, Y] = i ? [z[0], 1, z[1]] : [z[0], z[1], 1], [Z, te, ee] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = A * P, ne = De([R, A, P], C.dtype), oe = ne.values, re = n.blockSize;
   for (let le = 0; le < R; le++)
@@ -20988,16 +20988,16 @@ function kC(e) {
 var mG = { kernelName: Ia, backendName: "cpu", kernelFunc: kC };
 function gG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d, h, f, m = [];
-  d = kC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = Kl({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = iv(n, d, c, o, p), m.push(d), d = f);
+  d = kC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = jl({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = av(n, d, c, o, p), m.push(d), d = f);
   for (let b of m)
     n.disposeIntermediateTensorInfo(b);
   return d;
 }
 var bG = { kernelName: na, backendName: "cpu", kernelFunc: gG };
-var yG = st(sl, (e) => Math.acos(e));
-var vG = { kernelName: sl, backendName: "cpu", kernelFunc: yG };
-var xG = st(rl, (e) => Math.acosh(e));
-var wG = { kernelName: rl, backendName: "cpu", kernelFunc: xG };
+var yG = st(nl, (e) => Math.acos(e));
+var vG = { kernelName: nl, backendName: "cpu", kernelFunc: yG };
+var xG = st(sl, (e) => Math.acosh(e));
+var wG = { kernelName: sl, backendName: "cpu", kernelFunc: xG };
 function kG(e) {
   let { inputs: t, backend: n } = e, s = t;
   ve(t, "addN");
@@ -21032,7 +21032,7 @@ function SG(e) {
   }
   return g;
 }
-var CG = { kernelName: al, backendName: "cpu", kernelFunc: SG };
+var CG = { kernelName: rl, backendName: "cpu", kernelFunc: SG };
 function NG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   ve(r, "any");
@@ -21055,7 +21055,7 @@ function NG(e) {
   }
   return g;
 }
-var TG = { kernelName: il, backendName: "cpu", kernelFunc: NG };
+var TG = { kernelName: al, backendName: "cpu", kernelFunc: NG };
 function $G(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
   ve(r, "argMax");
@@ -21089,19 +21089,19 @@ function AG(e) {
   }
   return l.forEach((g) => n.disposeIntermediateTensorInfo(g)), n.makeTensorInfo(c, "int32", h);
 }
-var EG = { kernelName: ol, backendName: "cpu", kernelFunc: AG };
-var RG = st(ul, (e) => Math.asin(e));
-var DG = { kernelName: ul, backendName: "cpu", kernelFunc: RG };
-var FG = st(ll, (e) => Math.asinh(e));
-var OG = { kernelName: ll, backendName: "cpu", kernelFunc: FG };
-var PG = st(cl, (e) => Math.atan(e));
-var zG = { kernelName: cl, backendName: "cpu", kernelFunc: PG };
+var EG = { kernelName: il, backendName: "cpu", kernelFunc: AG };
+var RG = st(ol, (e) => Math.asin(e));
+var DG = { kernelName: ol, backendName: "cpu", kernelFunc: RG };
+var FG = st(ul, (e) => Math.asinh(e));
+var OG = { kernelName: ul, backendName: "cpu", kernelFunc: FG };
+var PG = st(ll, (e) => Math.atan(e));
+var zG = { kernelName: ll, backendName: "cpu", kernelFunc: PG };
 var MG = At((e, t) => Math.atan2(e, t));
-var LG = Gt(pl, MG);
-var BG = { kernelName: pl, backendName: "cpu", kernelFunc: LG };
-var VG = st(dl, (e) => Math.atanh(e));
-var WG = { kernelName: dl, backendName: "cpu", kernelFunc: VG };
-function ov(e, t, n, s, r, a) {
+var LG = Gt(dl, MG);
+var BG = { kernelName: dl, backendName: "cpu", kernelFunc: LG };
+var VG = st(cl, (e) => Math.atanh(e));
+var WG = { kernelName: cl, backendName: "cpu", kernelFunc: VG };
+function iv(e, t, n, s, r, a) {
   let i = r.strideHeight, o = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = De(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
   for (let x = 0; x < r.batchSize; ++x) {
     let k = x * b, C = x * s[0];
@@ -21239,7 +21239,7 @@ function GG(e) {
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     p = Os({ inputs: { x: r }, backend: n });
   else {
-    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = ov(d, r.shape, r.dtype, h, c, "avg");
+    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = iv(d, r.shape, r.dtype, h, c, "avg");
     p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
   }
   return p;
@@ -21251,7 +21251,7 @@ function qG(e) {
   let c = N.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = SC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
   return n.makeTensorInfo(d.shape, "float32", d.values);
 }
-var jG = { kernelName: qd, backendName: "cpu", kernelFunc: qG };
+var jG = { kernelName: Hd, backendName: "cpu", kernelFunc: qG };
 function KG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
   ve([r, a], "avgPool3DGrad");
@@ -21280,7 +21280,7 @@ function KG(e) {
           }
   return n.makeTensorInfo(P.shape, P.dtype, P.values);
 }
-var XG = { kernelName: ig, backendName: "cpu", kernelFunc: KG };
+var XG = { kernelName: ag, backendName: "cpu", kernelFunc: KG };
 function YG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
   ve([r, a], "avgPoolGrad");
@@ -21304,7 +21304,7 @@ function YG(e) {
         }
   return n.makeTensorInfo(k.shape, k.dtype, k.values);
 }
-var QG = { kernelName: ag, backendName: "cpu", kernelFunc: YG };
+var QG = { kernelName: rg, backendName: "cpu", kernelFunc: YG };
 function ZG(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, scale: a, offset: i, mean: o, variance: u } = t;
   w.assert(o.shape.length === u.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks."), w.assert(i == null || o.shape.length === i.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks."), w.assert(a == null || o.shape.length === a.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks."), ve([r, o, u, a, i], "batchNorm");
@@ -21322,17 +21322,17 @@ function eH(e) {
   let o = a.reduce((b, y) => b * y), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = mt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = Vn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = fa({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
-var tH = { kernelName: oo, backendName: "cpu", kernelFunc: eH };
+var tH = { kernelName: io, backendName: "cpu", kernelFunc: eH };
 function nH(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = Jy(o, u, a.dtype, a.shape, i);
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, l = Zy(o, u, a.dtype, a.shape, i);
   return n.makeTensorInfo([i], a.dtype, l);
 }
-var sH = { kernelName: og, backendName: "cpu", kernelFunc: nH };
+var sH = { kernelName: ig, backendName: "cpu", kernelFunc: nH };
 function rH(e) {
   let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, o = N.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
   return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
 }
-var aH = { kernelName: ug, backendName: "cpu", kernelFunc: rH };
+var aH = { kernelName: og, backendName: "cpu", kernelFunc: rH };
 var iH = st(Ir, (e, t) => {
   let n = t;
   return e > n.clipValueMax ? n.clipValueMax : e < n.clipValueMin ? n.clipValueMin : e;
@@ -21346,13 +21346,13 @@ var uH = (e) => {
   }
   return n.makeOutput(s, t.shape, "float32");
 };
-var lH = { kernelName: Kd, backendName: "cpu", kernelFunc: uH };
-function to(e) {
+var lH = { kernelName: jd, backendName: "cpu", kernelFunc: uH };
+function eo(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.data.get(s.dataId).complexTensorInfos.imag, a = n.data.get(r.dataId).values;
   return n.makeTensorInfo(r.shape, r.dtype, a);
 }
-var cH = { kernelName: Zd, backendName: "cpu", kernelFunc: to };
-function no(e) {
+var cH = { kernelName: Qd, backendName: "cpu", kernelFunc: eo };
+function to(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = N.computeOutShape(t.map((m) => m.shape), a);
   if (w.sizeFromShape(i) === 0)
     return n.makeTensorInfo(i, t[0].dtype, []);
@@ -21361,7 +21361,7 @@ function no(e) {
     return Os({ inputs: { x: o[0] }, backend: n });
   let u = o.map((m) => m.shape);
   if (N.assertParamsConsistent(u, a), o[0].dtype === "complex64") {
-    let m = o.map((x) => ha({ inputs: { input: x }, backend: n })), g = o.map((x) => to({ inputs: { input: x }, backend: n })), b = no({ inputs: m, backend: n, attrs: { axis: a } }), y = no({ inputs: g, backend: n, attrs: { axis: a } }), v = An({ inputs: { real: b, imag: y }, backend: n });
+    let m = o.map((x) => ha({ inputs: { input: x }, backend: n })), g = o.map((x) => eo({ inputs: { input: x }, backend: n })), b = to({ inputs: m, backend: n, attrs: { axis: a } }), y = to({ inputs: g, backend: n, attrs: { axis: a } }), v = An({ inputs: { real: b, imag: y }, backend: n });
     return m.forEach((x) => n.disposeIntermediateTensorInfo(x)), g.forEach((x) => n.disposeIntermediateTensorInfo(x)), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), v;
   }
   let l = o.map((m) => {
@@ -21369,10 +21369,10 @@ function no(e) {
     return mt({ inputs: { x: m }, backend: n, attrs: { shape: [-1, g] } });
   }), c = l.map((m) => ({ vals: n.data.get(m.dataId).values, shape: m.shape }));
   i = N.computeOutShape(l.map((m) => m.shape), 1);
-  let p = l[0].shape[0] === 1, d = ev(c, i, t[0].dtype, p), h = N.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
+  let p = l[0].shape[0] === 1, d = Jy(c, i, t[0].dtype, p), h = N.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
   return l.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var dH = { kernelName: uo, backendName: "cpu", kernelFunc: no };
+var dH = { kernelName: oo, backendName: "cpu", kernelFunc: to };
 function CC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dataFormat: u, dilations: l, dimRoundingMode: c } = s;
   ve([r, a], "conv2d");
@@ -21432,7 +21432,7 @@ function hH(e) {
   }
   return n.makeTensorInfo(y.shape, y.dtype, y.values);
 }
-var fH = { kernelName: lg, backendName: "cpu", kernelFunc: hH };
+var fH = { kernelName: ug, backendName: "cpu", kernelFunc: hH };
 function mH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s;
   ve([r, a], "conv2dBackpropInput");
@@ -21504,7 +21504,7 @@ function bH(e) {
   }
   return n.makeTensorInfo(x.shape, x.dtype, x.values);
 }
-var yH = { kernelName: Xd, backendName: "cpu", kernelFunc: bH };
+var yH = { kernelName: Kd, backendName: "cpu", kernelFunc: bH };
 function vH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s;
   ve([r, a], "conv3dBackpropFilterV2");
@@ -21540,7 +21540,7 @@ function vH(e) {
   }
   return n.makeTensorInfo(y.shape, y.dtype, y.values);
 }
-var xH = { kernelName: cg, backendName: "cpu", kernelFunc: vH };
+var xH = { kernelName: lg, backendName: "cpu", kernelFunc: vH };
 function wH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s;
   ve([r], "conv3dBackpropInputV2");
@@ -21560,8 +21560,8 @@ function wH(e) {
                 for (let Ms = Et; Ms < Jt; ++Ms) {
                   let Ls = Ms * le - In, xi = v * Ee + x * Cn + k * Rn + C * Ms, Js = E * ($ - 1 - Xt) + A * (z - 1 - en) + P * (W - 1 - Ls) + R * Pe;
                   for (let Bs = 0; Bs < te; ++Bs) {
-                    let pu = y[xi + Bs], wi = T[Js + Bs];
-                    Sn += pu * wi;
+                    let du = y[xi + Bs], wi = T[Js + Bs];
+                    Sn += du * wi;
                   }
                 }
               }
@@ -21572,7 +21572,7 @@ function wH(e) {
       }
   return n.makeTensorInfo(d.shape, d.dtype, d.values);
 }
-var kH = { kernelName: dg, backendName: "cpu", kernelFunc: wH };
+var kH = { kernelName: cg, backendName: "cpu", kernelFunc: wH };
 var IH = st($a, (e) => Math.cos(e));
 var SH = { kernelName: $a, backendName: "cpu", kernelFunc: IH };
 var CH = st(_a, (e) => Math.cosh(e));
@@ -21637,7 +21637,7 @@ function TH(e) {
   }
   return n.makeTensorInfo(b.shape, b.dtype, b.values);
 }
-var $H = { kernelName: co, backendName: "cpu", kernelFunc: TH };
+var $H = { kernelName: lo, backendName: "cpu", kernelFunc: TH };
 function _H(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
   ve(r, "cumsum");
@@ -21664,11 +21664,11 @@ function _H(e) {
   }
   return g;
 }
-var AH = { kernelName: lo, backendName: "cpu", kernelFunc: _H };
+var AH = { kernelName: uo, backendName: "cpu", kernelFunc: _H };
 function EH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
   if (r.shape.length === 1) {
-    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = Jy(u, l, a.dtype, a.shape, i);
+    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = Zy(u, l, a.dtype, a.shape, i);
     return n.makeTensorInfo([i], a.dtype, c);
   } else if (r.shape.length === 2) {
     let u = n.bufferSync(r), l = n.bufferSync(a), c = OS(u, l, i, o);
@@ -21676,7 +21676,7 @@ function EH(e) {
   }
   throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
 }
-var RH = { kernelName: pg, backendName: "cpu", kernelFunc: EH };
+var RH = { kernelName: dg, backendName: "cpu", kernelFunc: EH };
 function DH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s;
   w.assert(i === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${i}`);
@@ -21694,7 +21694,7 @@ function DH(e) {
     }
   return n.makeTensorInfo([o, p, d, h], r.dtype, m);
 }
-var FH = { kernelName: po, backendName: "cpu", kernelFunc: DH };
+var FH = { kernelName: co, backendName: "cpu", kernelFunc: DH };
 function NC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s;
   ve([r, a], "depthwiseConv2DNative");
@@ -21755,7 +21755,7 @@ function PH(e) {
   }
   return n.makeTensorInfo(g.shape, g.dtype, g.values);
 }
-var zH = { kernelName: hg, backendName: "cpu", kernelFunc: PH };
+var zH = { kernelName: pg, backendName: "cpu", kernelFunc: PH };
 function MH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s;
   ve([r, a], "depthwiseConv2DNativeBackpropInput");
@@ -21781,7 +21781,7 @@ function MH(e) {
       }
   return n.makeTensorInfo(f.shape, f.dtype, f.values);
 }
-var LH = { kernelName: fg, backendName: "cpu", kernelFunc: MH };
+var LH = { kernelName: hg, backendName: "cpu", kernelFunc: MH };
 function BH(e) {
   let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, i = De([r, r], s.dtype), o = i.values;
   for (let l = 0; l < a.length; l++)
@@ -21789,8 +21789,8 @@ function BH(e) {
   let u = [...s.shape, ...s.shape];
   return n.makeTensorInfo(u, i.dtype, i.values);
 }
-var VH = { kernelName: mg, backendName: "cpu", kernelFunc: BH };
-var WH = { kernelName: Yd, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+var VH = { kernelName: fg, backendName: "cpu", kernelFunc: BH };
+var WH = { kernelName: Xd, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, filter: r } = e, { strides: a, pad: i, dilations: o } = n, u = t, l = u.data.get(s.dataId).values, c = s.shape.length, p = u.data.get(r.dataId).values, d = r.shape.length, { batchSize: h, inHeight: f, inWidth: m, inChannels: g, outHeight: b, outWidth: y, padInfo: v, strideHeight: x, strideWidth: k, filterHeight: C, filterWidth: T, dilationHeight: E, dilationWidth: A, outShape: P } = N.computeDilation2DInfo(s.shape, r.shape, a, i, "NHWC", o), R = w.sizeFromShape(P), F = P.length, $ = w.getArrayFromDType(s.dtype, R);
   for (let W = 0; W < h; ++W)
     for (let q = 0; q < b; ++q) {
@@ -21817,9 +21817,9 @@ var WH = { kernelName: Yd, backendName: "cpu", kernelFunc: ({ inputs: e, backend
     }
   return { dataId: u.write(w.toTypedArray($, s.dtype), P, s.dtype), shape: P, dtype: s.dtype };
 } };
-var UH = { kernelName: Yf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+var UH = { kernelName: Xf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: C, dilationHeight: T, dilationWidth: E, outShape: A } = N.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
-  w.assert(a.rank === A.length, () => `Error in ${Yf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
+  w.assert(a.rank === A.length, () => `Error in ${Xf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
   let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(r.shape, r.dtype);
   for (let $ = 0; $ < d; ++$)
     for (let z = 0; z < g; ++z) {
@@ -21845,9 +21845,9 @@ var UH = { kernelName: Yf, backendName: "cpu", kernelFunc: ({ inputs: e, backend
     }
   return { dataId: l.write(w.toTypedArray(R, s.dtype), r.shape, r.dtype), shape: r.shape, dtype: r.dtype };
 } };
-var GH = { kernelName: Xf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
+var GH = { kernelName: Kf, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s, filter: r, dy: a } = e, { strides: i, pad: o, dilations: u } = n, l = t, c = w.toNestedArray(s.shape, l.data.get(s.dataId).values), p = w.toNestedArray(r.shape, l.data.get(r.dataId).values), { batchSize: d, inHeight: h, inWidth: f, inChannels: m, outHeight: g, outWidth: b, padInfo: y, strideHeight: v, strideWidth: x, filterHeight: k, filterWidth: C, dilationHeight: T, dilationWidth: E, outShape: A } = N.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
-  w.assert(a.rank === A.length, () => `Error in ${Xf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
+  w.assert(a.rank === A.length, () => `Error in ${Kf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
   let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(s.shape, s.dtype);
   for (let $ = 0; $ < d; ++$)
     for (let z = 0; z < g; ++z) {
@@ -21873,14 +21873,14 @@ var GH = { kernelName: Xf, backendName: "cpu", kernelFunc: ({ inputs: e, backend
     }
   return { dataId: l.write(w.toTypedArray(R, s.dtype), s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-function Xl(e) {
+function Kl(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   ve(r, "sum");
   let o;
   r.dtype === "bool" ? o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : o = Os({ inputs: { x: r }, backend: n });
   let u = o.shape.length, l = w.parseAxisParam(a, o.shape), c = N.getAxesPermutation(l, u), p = l, d = o;
   c != null && (d = Vn({ inputs: { x: o }, backend: n, attrs: { perm: c } }), p = N.getInnerMostAxes(p.length, u)), N.assertAxesAreInnerMostDims("sum", p, d.shape.length);
-  let [h, f] = N.computeOutAndReduceShapes(d.shape, p), m = N.upcastType(d.dtype, "int32"), g = Dd(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
+  let [h, f] = N.computeOutAndReduceShapes(d.shape, p), m = N.upcastType(d.dtype, "int32"), g = Rd(n, h, m), b = w.sizeFromShape(f), y = n.data.get(g.dataId).values, v = n.data.get(d.dataId).values;
   for (let x = 0; x < y.length; ++x) {
     let k = x * b, C = 0;
     for (let T = 0; T < b; ++T)
@@ -21893,7 +21893,7 @@ function Xl(e) {
   }
   return n.disposeIntermediateTensorInfo(o), c != null && n.disposeIntermediateTensorInfo(d), g;
 }
-var HH = { kernelName: ai, backendName: "cpu", kernelFunc: Xl };
+var HH = { kernelName: ai, backendName: "cpu", kernelFunc: Kl };
 function qH(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = N.decodeEinsumEquation(r, a.length);
   N.checkEinsumDimSizes(i.length, u, a);
@@ -21905,15 +21905,15 @@ function qH(e) {
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = mt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Hp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = mt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Gp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = Xl({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Kl({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeIntermediateTensorInfo(m);
   return d;
 }
-var jH = { kernelName: Qd, backendName: "cpu", kernelFunc: qH };
+var jH = { kernelName: Yd, backendName: "cpu", kernelFunc: qH };
 function KH(e) {
   let { inputs: t, backend: n } = e, { dy: s, y: r } = t;
   ve([s, r], "eluGrad");
@@ -21924,26 +21924,26 @@ function KH(e) {
   }
   return n.makeTensorInfo(r.shape, "float32", a);
 }
-var XH = { kernelName: gg, backendName: "cpu", kernelFunc: KH };
+var XH = { kernelName: mg, backendName: "cpu", kernelFunc: KH };
 var YH = N.ERF_P;
 var QH = N.ERF_A1;
 var ZH = N.ERF_A2;
 var JH = N.ERF_A3;
 var e6 = N.ERF_A4;
 var t6 = N.ERF_A5;
-var n6 = st(hl, (e) => {
+var n6 = st(pl, (e) => {
   let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + YH * n);
   return t * (1 - ((((t6 * s + e6) * s + JH) * s + ZH) * s + QH) * s * Math.exp(-n * n));
 });
-var s6 = { kernelName: hl, backendName: "cpu", kernelFunc: n6 };
-function Od(e) {
+var s6 = { kernelName: pl, backendName: "cpu", kernelFunc: n6 };
+function Fd(e) {
   let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
   return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), mt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
 }
-var r6 = { kernelName: fo, backendName: "cpu", kernelFunc: Od };
+var r6 = { kernelName: ho, backendName: "cpu", kernelFunc: Fd };
 var a6 = At((e, t) => e / t);
-var uv = Gt(Ea, a6);
-var zm = { kernelName: Ea, backendName: "cpu", kernelFunc: uv };
+var ov = Gt(Ea, a6);
+var Pm = { kernelName: Ea, backendName: "cpu", kernelFunc: ov };
 function TC(e, t, n) {
   let s = e.shape, r = s[0], a = s[1], i = n.data.get(e.dataId), o = i.complexTensorInfos.real, u = i.complexTensorInfos.imag, l = [r, a], c = w.sizeFromShape(l), p = w.getTypedArrayFromDType("float32", c), d = w.getTypedArrayFromDType("float32", c);
   for (let g = 0; g < r; g++) {
@@ -21960,9 +21960,9 @@ function TC(e, t, n) {
 function i6(e, t, n) {
   let s = w.sizeFromShape(e.shape), r = n.data.get(e.dataId), a = n.data.get(r.complexTensorInfos.real.dataId).values, i = n.data.get(r.complexTensorInfos.imag.dataId).values;
   if (o6(s)) {
-    let o = Mm(a, i, s, t, n), u = [e.shape[0], e.shape[1]];
+    let o = zm(a, i, s, t, n), u = [e.shape[0], e.shape[1]];
     if (t) {
-      let l = n.makeTensorInfo(u, "float32", o.real), c = n.makeTensorInfo(u, "float32", o.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = zm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = zm.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
+      let l = n.makeTensorInfo(u, "float32", o.real), c = n.makeTensorInfo(u, "float32", o.imag), p = n.makeTensorInfo([], "float32", w.createScalarValue(s, "float32")), d = Os({ inputs: { x: p }, backend: n }), h = Pm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = Pm.kernelFunc({ inputs: { a: c, b: d }, backend: n }), m = n.data.get(h.dataId).values, g = n.data.get(f.dataId).values;
       return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), { real: m, imag: g };
     }
     return o;
@@ -21974,10 +21974,10 @@ function i6(e, t, n) {
 function o6(e) {
   return (e & e - 1) === 0;
 }
-function Mm(e, t, n, s, r) {
+function zm(e, t, n, s, r) {
   if (n === 1)
     return { real: e, imag: t };
-  let a = N.mergeRealAndImagArrays(e, t), i = n / 2, o = N.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = An({ inputs: { real: p, imag: d }, backend: r }), f = N.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = An({ inputs: { real: y, imag: v }, backend: r }), k = Mm(u, l, i, s, r), C = k.real, T = k.imag, E = [C.length], A = r.makeTensorInfo(E, "float32", C), P = r.makeTensorInfo(E, "float32", T), R = An({ inputs: { real: A, imag: P }, backend: r }), F = Mm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = An({ inputs: { real: q, imag: K }, backend: r }), Z = N.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = An({ inputs: { real: ee, imag: se }, backend: r }), oe = Hp({ inputs: { a: ne, b: Y }, backend: r }), re = Kl({ inputs: { a: R, b: oe }, backend: r }), le = av({ inputs: { a: R, b: oe }, backend: r }), me = ha({ inputs: { input: re }, backend: r }), we = ha({ inputs: { input: le }, backend: r }), Se = to({ inputs: { input: re }, backend: r }), Ee = to({ inputs: { input: le }, backend: r }), Pe = no({ inputs: [me, we], backend: r, attrs: { axis: 0 } }), Xe = no({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
+  let a = N.mergeRealAndImagArrays(e, t), i = n / 2, o = N.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = An({ inputs: { real: p, imag: d }, backend: r }), f = N.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = An({ inputs: { real: y, imag: v }, backend: r }), k = zm(u, l, i, s, r), C = k.real, T = k.imag, E = [C.length], A = r.makeTensorInfo(E, "float32", C), P = r.makeTensorInfo(E, "float32", T), R = An({ inputs: { real: A, imag: P }, backend: r }), F = zm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = An({ inputs: { real: q, imag: K }, backend: r }), Z = N.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = An({ inputs: { real: ee, imag: se }, backend: r }), oe = Gp({ inputs: { a: ne, b: Y }, backend: r }), re = jl({ inputs: { a: R, b: oe }, backend: r }), le = rv({ inputs: { a: R, b: oe }, backend: r }), me = ha({ inputs: { input: re }, backend: r }), we = ha({ inputs: { input: le }, backend: r }), Se = eo({ inputs: { input: re }, backend: r }), Ee = eo({ inputs: { input: le }, backend: r }), Pe = to({ inputs: [me, we], backend: r, attrs: { axis: 0 } }), Xe = to({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
   return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(R), r.disposeIntermediateTensorInfo(q), r.disposeIntermediateTensorInfo(K), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(ee), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(ne), r.disposeIntermediateTensorInfo(oe), r.disposeIntermediateTensorInfo(re), r.disposeIntermediateTensorInfo(le), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Se), r.disposeIntermediateTensorInfo(we), r.disposeIntermediateTensorInfo(Ee), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
 }
 function u6(e, t, n) {
@@ -21996,16 +21996,16 @@ function l6(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = TC(o, false, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
 }
-var c6 = { kernelName: bg, backendName: "cpu", kernelFunc: l6 };
-function lv(e) {
+var c6 = { kernelName: gg, backendName: "cpu", kernelFunc: l6 };
+function uv(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r, dtype: a } = n, i = a || w.inferDtype(r), o = w.getArrayFromDType(i, w.sizeFromShape(s));
   return p6(o, r, i), t.makeTensorInfo(s, i, o);
 }
-var d6 = { kernelName: fl, backendName: "cpu", kernelFunc: lv };
+var d6 = { kernelName: hl, backendName: "cpu", kernelFunc: uv };
 function p6(e, t, n) {
   e.fill(t);
 }
-var h6 = { kernelName: go, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var h6 = { kernelName: mo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, r = n, a = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [i, o, u, l] = s.shape, c = r.data.get(s.dataId).values;
   for (let d = 0; d < i; d++) {
     let h = d * u * o * l;
@@ -22033,11 +22033,11 @@ function b6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = CC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
   if (i) {
     let g = m;
-    m = Kl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
+    m = jl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
   }
   if (h) {
     let g = m;
-    m = iv(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
+    m = av(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
   }
   return m;
 }
@@ -22046,11 +22046,11 @@ function v6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = NC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
   if (i) {
     let g = m;
-    m = Kl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
+    m = jl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
   }
   if (h) {
     let g = m;
-    m = iv(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
+    m = av(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
   }
   return m;
 }
@@ -22062,7 +22062,7 @@ function w6(e) {
   let d = n.data.get(r.dataId).values, h = n.bufferSync(s), f = US(d, h, s.dtype, l, o, c, p, s.shape, a);
   return n.makeTensorInfo(u, s.dtype, f.values);
 }
-var k6 = { kernelName: yo, backendName: "cpu", kernelFunc: w6 };
+var k6 = { kernelName: bo, backendName: "cpu", kernelFunc: w6 };
 function I6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s;
   ve([r, a], "gatherV2");
@@ -22076,33 +22076,33 @@ function I6(e) {
   let d = w.sizeFromShape(a.shape), h = N.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = mt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = mt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = GS(y, b, g);
   return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
 }
-var S6 = { kernelName: bo, backendName: "cpu", kernelFunc: I6 };
+var S6 = { kernelName: go, backendName: "cpu", kernelFunc: I6 };
 function C6(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = TC(o, true, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
 }
-var N6 = { kernelName: yg, backendName: "cpu", kernelFunc: C6 };
-var T6 = st(ml, (e) => Number.isFinite(e) ? 1 : 0, "bool");
-var $6 = { kernelName: ml, backendName: "cpu", kernelFunc: T6 };
-var _6 = st(gl, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
-var A6 = { kernelName: gl, backendName: "cpu", kernelFunc: _6 };
-var E6 = st(bl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
-var R6 = { kernelName: bl, backendName: "cpu", kernelFunc: E6 };
+var N6 = { kernelName: bg, backendName: "cpu", kernelFunc: C6 };
+var T6 = st(fl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
+var $6 = { kernelName: fl, backendName: "cpu", kernelFunc: T6 };
+var _6 = st(ml, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
+var A6 = { kernelName: ml, backendName: "cpu", kernelFunc: _6 };
+var E6 = st(gl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
+var R6 = { kernelName: gl, backendName: "cpu", kernelFunc: E6 };
 function D6(e) {
   let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = XS(s, r, a);
   return t.makeTensorInfo([i.length], "float32", i);
 }
-var F6 = { kernelName: vg, backendName: "cpu", kernelFunc: D6 };
-var O6 = st(yl, (e) => Math.log1p(e));
-var P6 = { kernelName: yl, backendName: "cpu", kernelFunc: O6 };
+var F6 = { kernelName: yg, backendName: "cpu", kernelFunc: D6 };
+var O6 = st(bl, (e) => Math.log1p(e));
+var P6 = { kernelName: bl, backendName: "cpu", kernelFunc: O6 };
 var z6 = At((e, t) => e && t);
-var M6 = Gt(ko, z6, null, "bool");
-var L6 = { kernelName: ko, backendName: "cpu", kernelFunc: M6 };
-var B6 = st(vl, (e) => e ? 0 : 1, "bool");
-var V6 = { kernelName: vl, backendName: "cpu", kernelFunc: B6 };
+var M6 = Gt(wo, z6, null, "bool");
+var L6 = { kernelName: wo, backendName: "cpu", kernelFunc: M6 };
+var B6 = st(yl, (e) => e ? 0 : 1, "bool");
+var V6 = { kernelName: yl, backendName: "cpu", kernelFunc: B6 };
 var W6 = At((e, t) => e || t);
-var U6 = Gt(Jd, W6, null, "bool");
-var G6 = { kernelName: Jd, backendName: "cpu", kernelFunc: U6 };
+var U6 = Gt(Zd, W6, null, "bool");
+var G6 = { kernelName: Zd, backendName: "cpu", kernelFunc: U6 };
 function H6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s;
   ve(r, "LRN");
@@ -22121,7 +22121,7 @@ function H6(e) {
   }
   return n.makeTensorInfo(r.shape, r.dtype, h);
 }
-var q6 = { kernelName: ep, backendName: "cpu", kernelFunc: H6 };
+var q6 = { kernelName: Jd, backendName: "cpu", kernelFunc: H6 };
 function j6(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s;
   ve(i, "LRNGrad");
@@ -22138,14 +22138,14 @@ function j6(e) {
   }
   return n.makeTensorInfo(i.shape, r.dtype, g);
 }
-var K6 = { kernelName: xg, backendName: "cpu", kernelFunc: j6 };
+var K6 = { kernelName: vg, backendName: "cpu", kernelFunc: j6 };
 function $C(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s, o = n, u = r.shape, l = u.length, c = w.parseAxisParam(a, u), p = c, d = N.getAxesPermutation(p, l), h = o.data.get(r.dataId).values;
   if (d != null) {
     let x = new Array(l);
     for (let k = 0; k < x.length; k++)
       x[k] = u[d[k]];
-    h = nv(h, u, r.dtype, d, x), p = N.getInnerMostAxes(p.length, l), u = x;
+    h = tv(h, u, r.dtype, d, x), p = N.getInnerMostAxes(p.length, l), u = x;
   }
   ve(r, "max"), N.assertAxesAreInnerMostDims("max", p, l);
   let [f, m] = N.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = QS(h, g, f, r.dtype), y = o.write(b, f, r.dtype), v = f;
@@ -22161,7 +22161,7 @@ function Y6(e) {
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     p = Os({ inputs: { x: r }, backend: n });
   else {
-    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = ov(d, r.shape, r.dtype, h, c, "max");
+    let d = n.data.get(r.dataId).values, h = w.computeStrides(r.shape), f = iv(d, r.shape, r.dtype, h, c, "max");
     p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
   }
   return p;
@@ -22173,7 +22173,7 @@ function Z6(e) {
   let c = N.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = SC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
   return n.makeTensorInfo(d.shape, "float32", d.values);
 }
-var J6 = { kernelName: tp, backendName: "cpu", kernelFunc: Z6 };
+var J6 = { kernelName: ep, backendName: "cpu", kernelFunc: Z6 };
 function eq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
   ve([r, a], "maxPool3DGrad");
@@ -22205,7 +22205,7 @@ function eq(e) {
           }
   return n.makeTensorInfo(A.shape, A.dtype, A.values);
 }
-var tq = { kernelName: kg, backendName: "cpu", kernelFunc: eq };
+var tq = { kernelName: wg, backendName: "cpu", kernelFunc: eq };
 function nq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
   ve([a, i], "maxPoolGrad");
@@ -22232,12 +22232,12 @@ function nq(e) {
         }
   return n.makeTensorInfo(T.shape, T.dtype, T.values);
 }
-var sq = { kernelName: wg, backendName: "cpu", kernelFunc: nq };
+var sq = { kernelName: xg, backendName: "cpu", kernelFunc: nq };
 function rq(e, t, n, s, r) {
-  let a = w.computeStrides(t), i = ov(e, t, n, a, r, "max"), o = IC(e, t, n, r, true, s);
+  let a = w.computeStrides(t), i = iv(e, t, n, a, r, "max"), o = IC(e, t, n, r, true, s);
   return [i.values, o.values];
 }
-var aq = { kernelName: Ig, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var aq = { kernelName: kg, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
   ve(s, "MaxPoolWithArgmax");
   let l = u.data.get(s.dataId).values, c = N.computePool2DInfo(s.shape, r, a, [1, 1], i), [p, d] = rq(l, s.shape, s.dtype, o, c), h = u.write(p, c.outShape, s.dtype), f = u.write(d, c.outShape, s.dtype);
@@ -22248,9 +22248,9 @@ function iq(e) {
   p.push(d);
   let h = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
   p.push(h);
-  let f = uv({ inputs: { a: h, b: d }, backend: n });
+  let f = ov({ inputs: { a: h, b: d }, backend: n });
   p.push(f);
-  let m = Xl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
+  let m = Kl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
   return p.forEach((g) => n.disposeIntermediateTensorInfo(g)), m;
 }
 var oq = { kernelName: Ga, backendName: "cpu", kernelFunc: iq };
@@ -22296,14 +22296,14 @@ var pq = At((e, t) => {
   let n = e % t;
   return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
 });
-var hq = Gt(xl, pq);
-var fq = { kernelName: xl, backendName: "cpu", kernelFunc: hq };
-var mq = ya(Wd());
+var hq = Gt(vl, pq);
+var fq = { kernelName: vl, backendName: "cpu", kernelFunc: hq };
+var mq = ya(Vd());
 function _C(e) {
   let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = r.shape.length, o = a;
   if (o === -1 && (o = i - 1), o !== i - 1)
     throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${i} and dim was ${o}`);
-  let u = w.parseAxisParam([o], r.shape), l = $C({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = N.expandShapeToKeepDim(l.shape, u), p = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = av({ inputs: { a: r, b: p }, backend: n }), h = BS({ inputs: { x: d }, backend: n }), f = Xl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = uv({ inputs: { a: h, b: m }, backend: n });
+  let u = w.parseAxisParam([o], r.shape), l = $C({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = N.expandShapeToKeepDim(l.shape, u), p = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = rv({ inputs: { a: r, b: p }, backend: n }), h = BS({ inputs: { x: d }, backend: n }), f = Kl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ov({ inputs: { a: h, b: m }, backend: n });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
 }
 var gq = { kernelName: ii, backendName: "cpu", kernelFunc: _C };
@@ -22329,7 +22329,7 @@ function bq(e) {
   }
   return o || n.disposeIntermediateTensorInfo(u), n.makeTensorInfo(d, "int32", h);
 }
-var yq = { kernelName: Sg, backendName: "cpu", kernelFunc: bq };
+var yq = { kernelName: Ig, backendName: "cpu", kernelFunc: bq };
 var vq = xs.nonMaxSuppressionV3Impl;
 function xq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s;
@@ -22337,7 +22337,7 @@ function xq(e) {
   let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = vq(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var wq = { kernelName: Co, backendName: "cpu", kernelFunc: xq };
+var wq = { kernelName: So, backendName: "cpu", kernelFunc: xq };
 var kq = xs.nonMaxSuppressionV4Impl;
 function Iq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s;
@@ -22345,7 +22345,7 @@ function Iq(e) {
   let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = kq(c, p, i, o, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var Sq = { kernelName: wl, backendName: "cpu", kernelFunc: Iq };
+var Sq = { kernelName: xl, backendName: "cpu", kernelFunc: Iq };
 var Cq = xs.nonMaxSuppressionV5Impl;
 function Nq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s;
@@ -22353,7 +22353,7 @@ function Nq(e) {
   let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = Cq(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var Tq = { kernelName: No, backendName: "cpu", kernelFunc: Nq };
+var Tq = { kernelName: Co, backendName: "cpu", kernelFunc: Nq };
 function $q(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s;
   ve(r, "oneHot");
@@ -22364,44 +22364,44 @@ function $q(e) {
     c[p] >= 0 && c[p] < a && (l[p * a + c[p]] = i);
   return n.makeTensorInfo([...r.shape, a], "int32", l);
 }
-var _q = { kernelName: $o, backendName: "cpu", kernelFunc: $q };
-function Pd(e) {
+var _q = { kernelName: To, backendName: "cpu", kernelFunc: $q };
+function Od(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("zerosLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ha({ inputs: { input: s }, backend: n }), a = Pd({ inputs: { x: r }, backend: n }), i = to({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = An({ inputs: { real: a, imag: o }, backend: n });
+    let r = ha({ inputs: { input: s }, backend: n }), a = Od({ inputs: { x: r }, backend: n }), i = eo({ inputs: { input: s }, backend: n }), o = Od({ inputs: { x: i }, backend: n }), u = An({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
-    return lv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
+    return uv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
 }
-var Aq = { kernelName: Ho, backendName: "cpu", kernelFunc: Pd };
+var Aq = { kernelName: Go, backendName: "cpu", kernelFunc: Od };
 function AC(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported for string tensors");
   if (s.dtype === "complex64") {
-    let r = ha({ inputs: { input: s }, backend: n }), a = AC({ inputs: { x: r }, backend: n }), i = to({ inputs: { input: s }, backend: n }), o = Pd({ inputs: { x: i }, backend: n }), u = An({ inputs: { real: a, imag: o }, backend: n });
+    let r = ha({ inputs: { input: s }, backend: n }), a = AC({ inputs: { x: r }, backend: n }), i = eo({ inputs: { input: s }, backend: n }), o = Od({ inputs: { x: i }, backend: n }), u = An({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
-    return lv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
+    return uv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
 }
-var Eq = { kernelName: To, backendName: "cpu", kernelFunc: AC };
+var Eq = { kernelName: No, backendName: "cpu", kernelFunc: AC };
 function EC(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Od({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return Fd({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Od({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = Fd({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
-  }), l = no({ inputs: u, backend: n, attrs: { axis: r } });
+  }), l = to({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var Rq = { kernelName: _o, backendName: "cpu", kernelFunc: EC };
+var Rq = { kernelName: $o, backendName: "cpu", kernelFunc: EC };
 function Dq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
   ve(r, "pad");
@@ -22418,12 +22418,12 @@ var Fq = At((e, t) => Math.pow(e, t));
 var Oq = Gt(Ya, Fq);
 var Pq = { kernelName: Ya, backendName: "cpu", kernelFunc: Oq };
 function zq(e) {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = sv(s, r, i, a);
+  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = nv(s, r, i, a);
   return t.makeTensorInfo([o.length], a, o);
 }
-var Mq = { kernelName: kl, backendName: "cpu", kernelFunc: zq };
-var Lq = st(Il, (e) => 1 / e);
-var Bq = { kernelName: Il, backendName: "cpu", kernelFunc: Lq };
+var Mq = { kernelName: wl, backendName: "cpu", kernelFunc: zq };
+var Lq = st(kl, (e) => 1 / e);
+var Bq = { kernelName: kl, backendName: "cpu", kernelFunc: Lq };
 function Vq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
   ve(r, "resizeBilinear");
@@ -22465,7 +22465,7 @@ function Uq(e) {
   }
   return n.makeTensorInfo([u, c, l, p], "float32", f);
 }
-var Gq = { kernelName: Ng, backendName: "cpu", kernelFunc: Uq };
+var Gq = { kernelName: Cg, backendName: "cpu", kernelFunc: Uq };
 function Hq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
   ve(r, "resizeNearestNeighbor");
@@ -22489,7 +22489,7 @@ function Hq(e) {
   }
   return n.makeTensorInfo([p, l, c, f], r.dtype, g);
 }
-var qq = { kernelName: Sl, backendName: "cpu", kernelFunc: Hq };
+var qq = { kernelName: Il, backendName: "cpu", kernelFunc: Hq };
 function jq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
   ve([a, r], "resizeNearestNeighborGrad");
@@ -22523,7 +22523,7 @@ function jq(e) {
   }
   return n.makeTensorInfo(r.shape, r.dtype, m);
 }
-var Kq = { kernelName: Cg, backendName: "cpu", kernelFunc: jq };
+var Kq = { kernelName: Sg, backendName: "cpu", kernelFunc: jq };
 function Xq(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s;
   ve(r, "reverse");
@@ -22537,8 +22537,8 @@ function Xq(e) {
   }
   return n.makeTensorInfo(u.shape, u.dtype, u.values);
 }
-var Yq = { kernelName: Ro, backendName: "cpu", kernelFunc: Xq };
-var Qq = { kernelName: qo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var Yq = { kernelName: Eo, backendName: "cpu", kernelFunc: Xq };
+var Qq = { kernelName: Ho, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = w.getTypedArrayFromDType(s.dtype, w.sizeFromShape(s.shape)), [l, c, p, d] = s.shape, [h, f] = N.getImageCenter(i, c, p), m = 255, g = Math.sin(r), b = Math.cos(r), y = o.data.get(s.dataId).values;
   for (let x = 0; x < l; x++) {
     let k = x * p * c * d;
@@ -22562,11 +22562,11 @@ var Qq = { kernelName: qo, backendName: "cpu", kernelFunc: ({ inputs: e, attrs:
   }
   return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
 } };
-var Zq = st(Do, (e) => {
+var Zq = st(Ro, (e) => {
   let t = Math.floor(e);
   return e - t < 0.5 ? Math.floor(e) : e - t > 0.5 ? Math.ceil(e) : t % 2 === 0 ? t : t + 1;
 });
-var Jq = { kernelName: Do, backendName: "cpu", kernelFunc: Zq };
+var Jq = { kernelName: Ro, backendName: "cpu", kernelFunc: Zq };
 function DC(e, t, n, s, r, a, i, o, u, l) {
   let c = [s / r, r], p = e.values, d = t.values;
   if (s === 0)
@@ -22590,7 +22590,7 @@ function ej(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = N.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = DC(h, f, i, p, l, u, o, c, 0, d);
   return n.makeTensorInfo(i, m.dtype, m.values);
 }
-var tj = { kernelName: Fo, backendName: "cpu", kernelFunc: ej };
+var tj = { kernelName: Do, backendName: "cpu", kernelFunc: ej };
 function nj(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t;
   ve([s, r, a], "select");
@@ -22600,24 +22600,24 @@ function nj(e) {
       o[f] === 1 ? p[d++] = u[f] : p[d++] = l[f];
   return n.makeTensorInfo(r.shape, c, p);
 }
-var sj = { kernelName: Oo, backendName: "cpu", kernelFunc: nj };
+var sj = { kernelName: Fo, backendName: "cpu", kernelFunc: nj };
 var rj = N.SELU_SCALEALPHA;
 var aj = N.SELU_SCALE;
-var ij = st(Cl, (e) => e >= 0 ? aj * e : rj * (Math.exp(e) - 1));
-var oj = { kernelName: Cl, backendName: "cpu", kernelFunc: ij };
-var uj = st(Nl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
-var lj = { kernelName: Nl, backendName: "cpu", kernelFunc: uj };
+var ij = st(Sl, (e) => e >= 0 ? aj * e : rj * (Math.exp(e) - 1));
+var oj = { kernelName: Sl, backendName: "cpu", kernelFunc: ij };
+var uj = st(Cl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
+var lj = { kernelName: Cl, backendName: "cpu", kernelFunc: uj };
 var cj = st(ni, (e) => Math.sin(e));
 var dj = { kernelName: ni, backendName: "cpu", kernelFunc: cj };
-var pj = st(zo, (e) => Math.sinh(e));
-var hj = { kernelName: zo, backendName: "cpu", kernelFunc: pj };
+var pj = st(Po, (e) => Math.sinh(e));
+var hj = { kernelName: Po, backendName: "cpu", kernelFunc: pj };
 var fj = 11920928955078125e-23;
-var ew = Math.log(fj) + 2;
-var mj = st(Tl, (e) => {
-  let t = e > -ew, n = e < ew, s = Math.exp(e), r;
+var Jx = Math.log(fj) + 2;
+var mj = st(Nl, (e) => {
+  let t = e > -Jx, n = e < Jx, s = Math.exp(e), r;
   return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
 });
-var gj = { kernelName: Tl, backendName: "cpu", kernelFunc: mj };
+var gj = { kernelName: Nl, backendName: "cpu", kernelFunc: mj };
 function bj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   ve([r], "spaceToBatchND");
@@ -22628,7 +22628,7 @@ function bj(e) {
   let l = RC.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = N.getReshaped(l.shape, a, o, false), p = N.getPermuted(c.length, a.length, false), d = N.getReshapedPermuted(l.shape, a, o, false), m = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = Vn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = mt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
 }
-var yj = { kernelName: Mo, backendName: "cpu", kernelFunc: bj };
+var yj = { kernelName: zo, backendName: "cpu", kernelFunc: bj };
 function vj(e) {
   let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
   if (a.shape.length !== 1)
@@ -22646,7 +22646,7 @@ function vj(e) {
   let o = n.data.get(s.dataId).values, u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = n.data.get(i.dataId).values[0], [p, d, h, f, m] = aC(o, s.shape, s.dtype, u, r.dtype, l, c);
   return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
 }
-var xj = { kernelName: sp, backendName: "cpu", kernelFunc: vj };
+var xj = { kernelName: np, backendName: "cpu", kernelFunc: vj };
 function wj(e) {
   let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
   if (s.shape.length !== 2)
@@ -22660,7 +22660,7 @@ function wj(e) {
   let i = Array.from(n.data.get(r.dataId).values), o = n.data.get(s.dataId).values, u = Array.from(n.data.get(a.dataId).values), [l, c, p] = iC(o, s.shape, s.dtype, i, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var kj = { kernelName: $l, backendName: "cpu", kernelFunc: wj };
+var kj = { kernelName: Tl, backendName: "cpu", kernelFunc: wj };
 function Ij(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
@@ -22673,10 +22673,10 @@ function Ij(e) {
           ${a.shape}`);
   if (r.shape[0] !== a.shape[0])
     throw new Error("segmentIds and indices should have same size.");
-  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = rv(i, s.shape, s.dtype, o, u, true);
+  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = sv(i, s.shape, s.dtype, o, u, true);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var Sj = { kernelName: rp, backendName: "cpu", kernelFunc: Ij };
+var Sj = { kernelName: sp, backendName: "cpu", kernelFunc: Ij };
 function Cj(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
@@ -22689,15 +22689,15 @@ function Cj(e) {
          ${a.shape}`);
   if (r.shape[0] !== a.shape[0])
     throw new Error("segmentIds and indices should have same size.");
-  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = rv(i, s.shape, s.dtype, o, u);
+  let i = n.data.get(s.dataId).values, o = n.data.get(r.dataId).values, u = n.data.get(a.dataId).values, [l, c] = sv(i, s.shape, s.dtype, o, u);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var Nj = { kernelName: ap, backendName: "cpu", kernelFunc: Cj };
+var Nj = { kernelName: rp, backendName: "cpu", kernelFunc: Cj };
 function Tj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = N.calculateShapes(a, r, o), h = false, f = n.bufferSync(r), m = n.bufferSync(a), g = n.data.get(i.dataId).values[0], b = DC(f, m, o, d, c, l, u, p, g, h);
   return n.makeTensorInfo(o, b.dtype, b.values);
 }
-var $j = { kernelName: ip, backendName: "cpu", kernelFunc: Tj };
+var $j = { kernelName: ap, backendName: "cpu", kernelFunc: Tj };
 function _j(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = N.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
@@ -22707,8 +22707,8 @@ function _j(e) {
     return l[o] += p, h;
   });
 }
-var Aj = { kernelName: Lo, backendName: "cpu", kernelFunc: _j };
-var Ej = { kernelName: _l, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var Aj = { kernelName: Mo, backendName: "cpu", kernelFunc: _j };
+var Ej = { kernelName: $l, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { x: n } = e, s = t;
   ve(n, "square");
   let r = s.data.get(n.dataId).values, a = new Float32Array(r.length);
@@ -22739,12 +22739,12 @@ function Fj(e) {
   }
   return k;
 }
-var Oj = { kernelName: Bo, backendName: "cpu", kernelFunc: Fj };
+var Oj = { kernelName: Lo, backendName: "cpu", kernelFunc: Fj };
 function Pj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.data.get(c.dataId).values, h = n.data.get(p.dataId).values, [f, m] = lC(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var zj = { kernelName: op, backendName: "cpu", kernelFunc: Pj };
+var zj = { kernelName: ip, backendName: "cpu", kernelFunc: Pj };
 function Mj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
   if (a.dtype !== "string")
@@ -22756,7 +22756,7 @@ function Mj(e) {
   let o = n.data.get(a.dataId).values, u = n.data.get(i.dataId).values[0], [l, c, p] = cC(o, u, r), d = c.length;
   return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
 }
-var Lj = { kernelName: Tg, backendName: "cpu", kernelFunc: Mj };
+var Lj = { kernelName: Ng, backendName: "cpu", kernelFunc: Mj };
 function Bj(e) {
   let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
   if (a.dtype !== "string")
@@ -22766,9 +22766,9 @@ function Bj(e) {
   let i = n.data.get(a.dataId).values, o = dC(i, r);
   return n.makeTensorInfo(a.shape, "int32", o);
 }
-var Vj = { kernelName: $g, backendName: "cpu", kernelFunc: Bj };
-var Wj = st(Vo, (e) => Math.tan(e));
-var Uj = { kernelName: Vo, backendName: "cpu", kernelFunc: Wj };
+var Vj = { kernelName: Tg, backendName: "cpu", kernelFunc: Bj };
+var Wj = st(Bo, (e) => Math.tan(e));
+var Uj = { kernelName: Bo, backendName: "cpu", kernelFunc: Wj };
 var Gj = st(li, (e) => Math.tanh(e));
 var Hj = { kernelName: li, backendName: "cpu", kernelFunc: Gj };
 function qj(e) {
@@ -22784,7 +22784,7 @@ function Kj(e) {
   let o = n.data.get(r.dataId).values, [u, l] = mC(o, r.shape, r.dtype, a, i);
   return [n.makeTensorInfo(u.shape, u.dtype, u.values), n.makeTensorInfo(l.shape, l.dtype, l.values)];
 }
-var Xj = { kernelName: Wo, backendName: "cpu", kernelFunc: Kj };
+var Xj = { kernelName: Vo, backendName: "cpu", kernelFunc: Kj };
 function Yj(e) {
   let { inputs: t, attrs: n, backend: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = n, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = w.computeStrides(r.shape), y = b[0], v = b[1], x = b[2], k = w.getTypedArrayFromDType(r.dtype, w.sizeFromShape(g));
   k.fill(u);
@@ -22797,7 +22797,7 @@ function Yj(e) {
           let z, W = P[6] * F + P[7] * R + 1;
           if (W === 0)
             continue;
-          let q = (P[0] * F + P[1] * R + P[2]) / W, K = (P[3] * F + P[4] * R + P[5]) / W, Y = tw(q, d, o), Z = tw(K, p, o);
+          let q = (P[0] * F + P[1] * R + P[2]) / W, K = (P[3] * F + P[4] * R + P[5]) / W, Y = ew(q, d, o), Z = ew(K, p, o);
           switch (i) {
             case "nearest":
               z = n5(C, p, d, y, v, x, A, Z, Y, $, u);
@@ -22815,8 +22815,8 @@ function Yj(e) {
   }
   return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
 }
-var Qj = { kernelName: Uo, backendName: "cpu", kernelFunc: Yj };
-function tw(e, t, n) {
+var Qj = { kernelName: Wo, backendName: "cpu", kernelFunc: Yj };
+function ew(e, t, n) {
   switch (n) {
     case "reflect":
       return Zj(e, t);
@@ -22871,16 +22871,16 @@ function e5(e, t) {
 function t5(e, t) {
   return w.clamp(0, e, t - 1);
 }
-function Du(e, t, n, s, r, a, i, o, u, l, c) {
+function Ru(e, t, n, s, r, a, i, o, u, l, c) {
   let p = i * s + o * r + u * a + l;
   return 0 <= o && o < t && 0 <= u && u < n ? e[p] : c;
 }
 function n5(e, t, n, s, r, a, i, o, u, l, c) {
   let p = Math.round(o), d = Math.round(u);
-  return Du(e, t, n, s, r, a, i, p, d, l, c);
+  return Ru(e, t, n, s, r, a, i, p, d, l, c);
 }
 function s5(e, t, n, s, r, a, i, o, u, l, c) {
-  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Du(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Du(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Du(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Du(e, t, n, s, r, a, i, h, f, l, c);
+  let p = Math.floor(o), d = Math.floor(u), h = p + 1, f = d + 1, m = (f - u) * Ru(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Ru(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Ru(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Ru(e, t, n, s, r, a, i, h, f, l, c);
   return (h - o) * m + (o - p) * g;
 }
 function r5(e) {
@@ -22889,7 +22889,7 @@ function r5(e) {
   let i = s.data.get(a.dataId).values, { outputValues: o, outputShape: u, indices: l } = gC(i, r, a.shape, a.dtype);
   return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
 }
-var a5 = { kernelName: _g, backendName: "cpu", kernelFunc: r5 };
+var a5 = { kernelName: $g, backendName: "cpu", kernelFunc: r5 };
 function i5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
@@ -22906,30 +22906,30 @@ function i5(e) {
   }
   return d;
 }
-var o5 = { kernelName: Go, backendName: "cpu", kernelFunc: i5 };
+var o5 = { kernelName: Uo, backendName: "cpu", kernelFunc: i5 };
 function u5(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s;
   ve(r, "unsortedSegmentSum");
   let o = r.shape.length, u = a.shape.length, l = [], c = [], p = o - u, d = a;
   for (let f = 0; f < p; ++f) {
-    let m = Od({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
+    let m = Fd({ inputs: { input: d }, backend: n, attrs: { dim: f + 1 } });
     d = m, c.push(m);
   }
   for (let f = 0; f < i; ++f) {
-    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = MS({ inputs: { a: g, b: d }, backend: n }), y = xr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = Hp({ inputs: { a: y, b: r }, backend: n }), x = Xl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
+    let m = w.createScalarValue(f, "int32"), g = n.makeTensorInfo([], "int32", m), b = MS({ inputs: { a: g, b: d }, backend: n }), y = xr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = Gp({ inputs: { a: y, b: r }, backend: n }), x = Kl({ inputs: { x: v }, backend: n, attrs: { axis: 0, keepDims: false } });
     l.push(x), c.push(g), c.push(b), c.push(y), c.push(v), c.push(x);
   }
   let h = EC({ inputs: l, backend: n, attrs: { axis: 0 } });
   return c.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var l5 = { kernelName: up, backendName: "cpu", kernelFunc: u5 };
-var c5 = [bG, cU, vG, wG, gU, IG, CG, TG, _G, EG, DG, OG, zG, BG, WG, HG, jG, XG, QG, mG, JG, tH, sH, aH, fU, yU, oH, dU, lH, dH, pH, fH, gH, yH, xH, kH, SH, NH, $H, AH, RH, FH, OH, zH, LH, VH, WH, UH, GH, jH, uG, XH, vU, s6, xU, r6, kU, c6, d6, h6, SU, g6, y6, x6, k6, S6, NU, $U, pU, N6, cH, $6, A6, R6, lG, AU, RU, F6, FU, P6, L6, V6, G6, q6, K6, X6, PU, Q6, J6, tq, sq, aq, oq, lq, MU, dq, fq, yq, BU, WU, wq, Sq, Tq, GU, _q, Eq, Rq, RC, Pq, dG, jU, Mq, hU, zm, Bq, pG, hG, fG, Wq, Gq, qq, Kq, Yq, Qq, Jq, XU, tj, sj, oj, QU, lj, dj, hj, ZU, gq, gj, yj, xj, kj, Sj, Nj, $j, Aj, tG, Ej, sG, Dj, Oj, zj, Lj, Vj, oG, HH, Uj, Hj, jj, Xj, Qj, HU, a5, o5, l5, Aq];
+var l5 = { kernelName: op, backendName: "cpu", kernelFunc: u5 };
+var c5 = [bG, cU, vG, wG, gU, IG, CG, TG, _G, EG, DG, OG, zG, BG, WG, HG, jG, XG, QG, mG, JG, tH, sH, aH, fU, yU, oH, dU, lH, dH, pH, fH, gH, yH, xH, kH, SH, NH, $H, AH, RH, FH, OH, zH, LH, VH, WH, UH, GH, jH, uG, XH, vU, s6, xU, r6, kU, c6, d6, h6, SU, g6, y6, x6, k6, S6, NU, $U, pU, N6, cH, $6, A6, R6, lG, AU, RU, F6, FU, P6, L6, V6, G6, q6, K6, X6, PU, Q6, J6, tq, sq, aq, oq, lq, MU, dq, fq, yq, BU, WU, wq, Sq, Tq, GU, _q, Eq, Rq, RC, Pq, dG, jU, Mq, hU, Pm, Bq, pG, hG, fG, Wq, Gq, qq, Kq, Yq, Qq, Jq, XU, tj, sj, oj, QU, lj, dj, hj, ZU, gq, gj, yj, xj, kj, Sj, Nj, $j, Aj, tG, Ej, sG, Dj, Oj, zj, Lj, Vj, oG, HH, Uj, Hj, jj, Xj, Qj, HU, a5, o5, l5, Aq];
 for (let e of c5)
-  Al(e);
+  _l(e);
 var d5 = {};
-Ae(d5, { assertNotComplex: () => nu, bindCanvasToFramebuffer: () => S5, bindColorTextureToFramebuffer: () => ad, bindTextureToProgramUniformSampler: () => KC, bindTextureUnit: () => HC, bindVertexBufferToProgramAttribute: () => Lm, callAndCheck: () => fe, canBeRepresented: () => FC, createFragmentShader: () => zC, createFramebuffer: () => GC, createProgram: () => MC, createStaticIndexBuffer: () => VC, createStaticVertexBuffer: () => BC, createTexture: () => WC, createVertexShader: () => PC, getBatchDim: () => ma, getExtensionOrThrow: () => Fu, getFramebufferErrorMessage: () => XC, getMaxTexturesInShader: () => JC, getNumChannels: () => k5, getProgramUniformLocation: () => jC, getProgramUniformLocationOrThrow: () => qC, getRowsCols: () => ga, getShapeAs3D: () => id, getTextureShapeFromLogicalShape: () => QC, getWebGLDisjointQueryTimerVersion: () => e1, getWebGLErrorMessage: () => OC, getWebGLMaxTextureSize: () => ZC, hasExtension: () => Mn, isCapableOfRenderingToFloatTexture: () => t1, isDownloadFloatTextureEnabled: () => n1, isReshapeFree: () => el, isWebGLFenceEnabled: () => s1, isWebGLVersionEnabled: () => Vm, linkProgram: () => LC, resetMaxTextureSize: () => C5, resetMaxTexturesInShader: () => N5, unbindColorTextureFromFramebuffer: () => Bm, unbindTextureUnit: () => I5, validateFramebuffer: () => Ou, validateProgram: () => rd, validateTextureSize: () => UC });
+Ae(d5, { assertNotComplex: () => tu, bindCanvasToFramebuffer: () => S5, bindColorTextureToFramebuffer: () => rd, bindTextureToProgramUniformSampler: () => KC, bindTextureUnit: () => HC, bindVertexBufferToProgramAttribute: () => Mm, callAndCheck: () => fe, canBeRepresented: () => FC, createFragmentShader: () => zC, createFramebuffer: () => GC, createProgram: () => MC, createStaticIndexBuffer: () => VC, createStaticVertexBuffer: () => BC, createTexture: () => WC, createVertexShader: () => PC, getBatchDim: () => ma, getExtensionOrThrow: () => Du, getFramebufferErrorMessage: () => XC, getMaxTexturesInShader: () => JC, getNumChannels: () => k5, getProgramUniformLocation: () => jC, getProgramUniformLocationOrThrow: () => qC, getRowsCols: () => ga, getShapeAs3D: () => ad, getTextureShapeFromLogicalShape: () => QC, getWebGLDisjointQueryTimerVersion: () => e1, getWebGLErrorMessage: () => OC, getWebGLMaxTextureSize: () => ZC, hasExtension: () => Mn, isCapableOfRenderingToFloatTexture: () => t1, isDownloadFloatTextureEnabled: () => n1, isReshapeFree: () => Ju, isWebGLFenceEnabled: () => s1, isWebGLVersionEnabled: () => Bm, linkProgram: () => LC, resetMaxTextureSize: () => C5, resetMaxTexturesInShader: () => N5, unbindColorTextureFromFramebuffer: () => Lm, unbindTextureUnit: () => I5, validateFramebuffer: () => Fu, validateProgram: () => sd, validateTextureSize: () => UC });
 var jr = {};
-var qf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
+var Hf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
 function p5(e, t) {
   jr[e] = t;
 }
@@ -22957,26 +22957,26 @@ function f5(e, t) {
   let n = t == null ? h5(e) : t;
   return n.addEventListener("webglcontextlost", (s) => {
     s.preventDefault(), delete jr[e];
-  }, false), e === 1 ? n.getContext("webgl", qf) || n.getContext("experimental-webgl", qf) : n.getContext("webgl2", qf);
+  }, false), e === 1 ? n.getContext("webgl", Hf) || n.getContext("experimental-webgl", Hf) : n.getContext("webgl2", Hf);
 }
-function Yl(e, t) {
+function Xl(e, t) {
   return [t, e];
 }
 function m5(e, t) {
   return e * t;
 }
-function Yc(e) {
+function Xc(e) {
   let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
   return w.sizeToSquarishShape(n);
 }
-function tu(e, t) {
+function eu(e, t) {
   return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
 }
 function g5(e, t) {
-  let [n, s] = tu(e, t);
+  let [n, s] = eu(e, t);
   return n * s * 4;
 }
-function cv(e, t) {
+function lv(e, t) {
   let n = e, s, r, a, i, o, u, l, c, p, d;
   return X().getNumber("WEBGL_VERSION") === 2 ? (s = n.R32F, r = n.R16F, a = n.RGBA16F, i = n.RGBA32F, o = n.RED, l = 4, c = 1, p = n.HALF_FLOAT, d = n.FLOAT, u = n.RGBA8) : (s = e.RGBA, r = e.RGBA, a = e.RGBA, i = n.RGBA, o = e.RGBA, l = 4, c = 4, p = t != null ? t.HALF_FLOAT_OES : null, d = e.FLOAT, u = e.RGBA), { internalFormatFloat: s, internalFormatHalfFloat: r, internalFormatPackedHalfFloat: a, internalFormatPackedFloat: i, textureFormatFloat: o, downloadTextureFormat: u, downloadUnpackNumChannels: l, defaultNumChannels: c, textureTypeHalfFloat: p, textureTypeFloat: d };
 }
@@ -23014,7 +23014,7 @@ function OC(e, t) {
       return `Unknown error code ${t}`;
   }
 }
-function Fu(e, t) {
+function Du(e, t) {
   return Qs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
 }
 function PC(e, t) {
@@ -23053,7 +23053,7 @@ function LC(e, t) {
   if (fe(e, () => e.linkProgram(t)), e.getProgramParameter(t, e.LINK_STATUS) === false)
     throw console.log(e.getProgramInfoLog(t)), new Error("Failed to link vertex and fragment shaders.");
 }
-function rd(e, t) {
+function sd(e, t) {
   if (fe(e, () => e.validateProgram(t)), e.getProgramParameter(t, e.VALIDATE_STATUS) === false)
     throw console.log(e.getProgramInfoLog(t)), new Error("Shader program validation failed.");
 }
@@ -23085,7 +23085,7 @@ function UC(e, t) {
 function GC(e) {
   return Qs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
 }
-function Lm(e, t, n, s, r, a, i) {
+function Mm(e, t, n, s, r, a, i) {
   let o = e.getAttribLocation(t, n);
   return o === -1 ? false : (fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, s)), fe(e, () => e.vertexAttribPointer(o, r, e.FLOAT, false, a, i)), fe(e, () => e.enableVertexAttribArray(o)), true);
 }
@@ -23107,13 +23107,13 @@ function KC(e, t, n, s) {
 function S5(e) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, null)), fe(e, () => e.viewport(0, 0, e.canvas.width, e.canvas.height)), fe(e, () => e.scissor(0, 0, e.canvas.width, e.canvas.height));
 }
-function ad(e, t, n) {
+function rd(e, t, n) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, n)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, t, 0));
 }
-function Bm(e, t) {
+function Lm(e, t) {
   fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
 }
-function Ou(e) {
+function Fu(e) {
   let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
   if (t !== e.FRAMEBUFFER_COMPLETE)
     throw new Error("Error binding framebuffer: " + XC(e, t));
@@ -23153,7 +23153,7 @@ function ga(e) {
     throw Error("Cannot get rows and columns of an empty shape array.");
   return [e.length > 1 ? e[e.length - 2] : 1, e[e.length - 1]];
 }
-function id(e) {
+function ad(e) {
   let t = [1, 1, 1];
   return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [ma(e), ...ga(e)]), t;
 }
@@ -23179,40 +23179,40 @@ function QC(e, t = false) {
   }
   return w.sizeToSquarishShape(s);
 }
-function Qc(e) {
+function Yc(e) {
   return e % 2 === 0;
 }
-function el(e, t) {
+function Ju(e, t) {
   if (e = e.slice(-2), t = t.slice(-2), w.arraysEqual(e, t) || !e.length || !t.length || e[0] === 0 || e[1] === 0 || t[0] === 0 || t[1] === 0)
     return true;
   if (e.length !== t.length) {
     let n = e.slice(-1)[0], s = t.slice(-1)[0];
-    if (n === s || Qc(n) && Qc(s) && (e[0] === 1 || t[0] === 1))
+    if (n === s || Yc(n) && Yc(s) && (e[0] === 1 || t[0] === 1))
       return true;
   }
-  return e[1] === t[1] && Qc(e[0]) && Qc(t[0]);
+  return e[1] === t[1] && Yc(e[0]) && Yc(t[0]);
 }
+var id;
 var od;
-var ud;
 function ZC(e) {
-  if (od == null) {
+  if (id == null) {
     let t = vs(e);
-    od = t.getParameter(t.MAX_TEXTURE_SIZE);
+    id = t.getParameter(t.MAX_TEXTURE_SIZE);
   }
-  return od;
+  return id;
 }
 function C5() {
-  od = null;
+  id = null;
 }
 function N5() {
-  ud = null;
+  od = null;
 }
 function JC(e) {
-  if (ud == null) {
+  if (od == null) {
     let t = vs(e);
-    ud = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
+    od = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
   }
-  return Math.min(16, ud);
+  return Math.min(16, od);
 }
 function e1(e) {
   if (e === 0)
@@ -23223,7 +23223,7 @@ function e1(e) {
 function Mn(e, t) {
   return e.getExtension(t) != null;
 }
-function Vm(e) {
+function Bm(e) {
   try {
     if (vs(e) != null)
       return true;
@@ -23241,7 +23241,7 @@ function t1(e) {
       return false;
   } else if (!Mn(t, "EXT_color_buffer_float"))
     return false;
-  return Wm(t);
+  return Vm(t);
 }
 function n1(e) {
   if (e === 0)
@@ -23252,7 +23252,7 @@ function n1(e) {
       return false;
   } else {
     if (Mn(t, "EXT_color_buffer_float"))
-      return Wm(t);
+      return Vm(t);
     let s = "EXT_color_buffer_half_float";
     if (Mn(t, s)) {
       let r = t.getExtension(s);
@@ -23260,10 +23260,10 @@ function n1(e) {
     }
     return false;
   }
-  return Wm(t);
+  return Vm(t);
 }
-function Wm(e) {
-  let t = cv(e), n = e.createTexture();
+function Vm(e) {
+  let t = lv(e), n = e.createTexture();
   e.bindTexture(e.TEXTURE_2D, n);
   let s = 1, r = 1;
   e.texImage2D(e.TEXTURE_2D, 0, t.internalFormatFloat, s, r, 0, t.textureFormatFloat, t.textureTypeFloat, null);
@@ -23273,7 +23273,7 @@ function Wm(e) {
   return e.bindTexture(e.TEXTURE_2D, null), e.bindFramebuffer(e.FRAMEBUFFER, null), e.deleteTexture(n), e.deleteFramebuffer(a), i;
 }
 function T5(e, t) {
-  let n = cv(e, t), s = e.createTexture();
+  let n = lv(e, t), s = e.createTexture();
   e.bindTexture(e.TEXTURE_2D, s);
   let r = 1, a = 1;
   e.texImage2D(e.TEXTURE_2D, 0, n.internalFormatHalfFloat, r, a, 0, n.textureFormatFloat, n.textureTypeHalfFloat, null);
@@ -23285,14 +23285,14 @@ function T5(e, t) {
 function s1(e) {
   return e !== 2 ? false : vs(e).fenceSync != null;
 }
-function nu(e, t) {
+function tu(e, t) {
   Array.isArray(e) || (e = [e]), e.forEach((n) => {
     n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the WebGL backend.`);
   });
 }
 var Ne = X();
 Ne.registerFlag("HAS_WEBGL", () => Ne.getNumber("WEBGL_VERSION") > 0);
-Ne.registerFlag("WEBGL_VERSION", () => Vm(2) ? 2 : Vm(1) ? 1 : 0);
+Ne.registerFlag("WEBGL_VERSION", () => Bm(2) ? 2 : Bm(1) ? 1 : 0);
 Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
 Ne.registerFlag("WEBGL_BUFFER_SUPPORTED", () => Ne.get("WEBGL_VERSION") === 2);
 Ne.registerFlag("WEBGL_CPU_FORWARD", () => true);
@@ -23314,7 +23314,7 @@ Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
   let e = Ne.getNumber("WEBGL_VERSION");
   return e === 0 ? 0 : e1(e);
 });
-Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !dp.isMobile());
+Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !cp.isMobile());
 Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => t1(Ne.getNumber("WEBGL_VERSION")));
 Ne.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => Ne.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : Ne.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));
 Ne.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => n1(Ne.getNumber("WEBGL_VERSION")));
@@ -23324,7 +23324,7 @@ Ne.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => -1, (e) => {
   if (e < 0 && e !== -1)
     throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`);
 });
-Ne.registerFlag("WEBGL_FLUSH_THRESHOLD", () => dp.isMobile() ? 1 : -1, (e) => {
+Ne.registerFlag("WEBGL_FLUSH_THRESHOLD", () => cp.isMobile() ? 1 : -1, (e) => {
   if (e < 0 && e !== -1)
     throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`);
 });
@@ -23336,7 +23336,8 @@ function hn() {
   let e, t, n, s, r, a, i, o, u, l;
   return X().getNumber("WEBGL_VERSION") === 2 ? (e = "#version 300 es", t = "in", n = "out", s = "in", r = "texture", a = "outputColor", i = "out vec4 outputColor;", o = `
       bool isnan_custom(float val) {
-        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
+        uint floatToUint = floatBitsToUint(val);
+        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
       }
 
       bvec4 isnan_custom(vec4 val) {
@@ -23388,7 +23389,7 @@ function gi(e, t, n = "index") {
     return `${i}; ${o};`;
   }).join("");
 }
-function qp(e, t, n = "index") {
+function Hp(e, t, n = "index") {
   let s = w.computeStrides(t);
   return s.map((r, a) => {
     let i = `int ${e[a]} = ${n} / outShapeStrides[${a}]`, o = a === s.length - 1 ? `int ${e[a + 1]} = ${n} - ${e[a]} * outShapeStrides[${a}]` : `index -= ${e[a]} * outShapeStrides[${a}]`;
@@ -23409,7 +23410,7 @@ function _5(e, t, n = "index") {
     return `${o}; ${u};`;
   }).join("");
 }
-function dv(e) {
+function cv(e) {
   let t = w.computeStrides(e).map((n) => n.toString());
   return `
   int getFlatIndex(ivec3 coords) {
@@ -23417,7 +23418,7 @@ function dv(e) {
   }
 `;
 }
-function pv() {
+function dv() {
   return `
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
@@ -23470,7 +23471,7 @@ function A5(e, t, n) {
   if (e.forEach((h) => {
     let f = w.sizeFromShape(h.shapeInfo.logicalShape);
     if (h.shapeInfo.isUniform ? s.push(`uniform float ${h.name}${f > 1 ? `[${f}]` : ""};`) : (s.push(`uniform sampler2D ${h.name};`), s.push(`uniform int offset${h.name};`)), n.enableShapeUniforms) {
-      let { uniformShape: m } = hv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
+      let { uniformShape: m } = pv(n.packedInputs, h.shapeInfo.logicalShape, h.shapeInfo.texShape);
       switch (m.length) {
         case 1:
           s.push(`uniform int ${h.name}Shape;`);
@@ -23517,7 +23518,7 @@ function A5(e, t, n) {
   return t.isPacked ? (l = R5(t.logicalShape, i, n.enableShapeUniforms), c = P5(o)) : (l = D5(t.logicalShape, i, n.enableShapeUniforms), c = O5(o)), n.packedInputs && (p += V5), [p, u, c, r, l, a, n.userCode].join(`
 `);
 }
-function su(e, t = false) {
+function nu(e, t = false) {
   let n = e.shapeInfo.logicalShape;
   switch (n.length) {
     case 0:
@@ -23554,7 +23555,7 @@ function i1(e, t) {
 }
 function E5(e, t, n = false, s) {
   let r = "";
-  n ? r += i1(e, s) : r += su(e, s);
+  n ? r += i1(e, s) : r += nu(e, s);
   let a = e.shapeInfo.logicalShape, i = t.logicalShape;
   return a.length <= i.length && (n ? r += cK(e, t) : r += dK(e, t)), r;
 }
@@ -23833,7 +23834,7 @@ function H5(e, t, n) {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${qp(["r", "c", "d"], e)}
+    ${Hp(["r", "c", "d"], e)}
     return ivec3(r, c, d);
   }
 `;
@@ -23904,7 +23905,7 @@ function j5(e, t, n) {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${qp(["r", "c", "d", "d2"], e)}
+      ${Hp(["r", "c", "d", "d2"], e)}
       return ivec4(r, c, d, d2);
     }
   `;
@@ -24110,7 +24111,7 @@ function tK(e, t) {
   if (e.shapeInfo.isUniform)
     return `
       float ${s}(int index) {
-        ${ru(e)}
+        ${su(e)}
       }
     `;
   let r = e.shapeInfo.texShape, a = r[0], i = r[1];
@@ -24206,11 +24207,11 @@ function sK(e, t) {
   }
   let { newShape: i, keptDims: o } = w.squeezeShape(n), u = i;
   if (u.length < n.length) {
-    let d = au(e, u), h = ["row", "col"];
+    let d = ru(e, u), h = ["row", "col"];
     return `
-      ${su(d, t)}
+      ${nu(d, t)}
       float ${r}(int row, int col) {
-        return ${r}(${iu(h, o)});
+        return ${r}(${au(h, o)});
       }
     `;
   }
@@ -24218,7 +24219,7 @@ function sK(e, t) {
     return `
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;
   let l = a[0], c = a[1], p = bi(s);
@@ -24265,11 +24266,11 @@ function sK(e, t) {
 function rK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = e.shapeInfo.texShape, i = [Math.ceil(a[0] / 2), Math.ceil(a[1] / 2)];
   if (n[0] === 1) {
-    let d = n.slice(1), h = [1, 2], f = au(e, d), m = ["b", "row", "col"];
+    let d = n.slice(1), h = [1, 2], f = ru(e, d), m = ["b", "row", "col"];
     return `
         ${i1(f, t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${iu(m, h)});
+          return ${r}(${au(m, h)});
         }
       `;
   }
@@ -24297,11 +24298,11 @@ function rK(e, t) {
 function aK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[1] * n[2], i = n[2], { newShape: o, keptDims: u } = w.squeezeShape(n), l = o;
   if (l.length < n.length) {
-    let m = au(e, l), g = ["row", "col", "depth"];
+    let m = ru(e, l), g = ["row", "col", "depth"];
     return `
-        ${su(m, t)}
+        ${nu(m, t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${iu(g, u)});
+          return ${r}(${au(g, u)});
         }
       `;
   }
@@ -24310,7 +24311,7 @@ function aK(e, t) {
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${i}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;
   let c = e.shapeInfo.texShape, p = c[0], d = c[1], h = e.shapeInfo.flatOffset;
@@ -24400,11 +24401,11 @@ function iK(e, t) {
 function oK(e, t) {
   let n = e.shapeInfo.logicalShape, s = e.name, r = "get" + s.charAt(0).toUpperCase() + s.slice(1), a = n[3], i = n[2] * a, o = n[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(n);
   if (u.length < n.length) {
-    let y = au(e, u), v = ["row", "col", "depth", "depth2"];
+    let y = ru(e, u), v = ["row", "col", "depth", "depth2"];
     return `
-      ${su(y, t)}
+      ${nu(y, t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${iu(v, l)});
+        return ${r}(${au(v, l)});
       }
     `;
   }
@@ -24413,7 +24414,7 @@ function oK(e, t) {
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${a}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1], f = `int stride2 = ${s}Shape[3];`, m = `int stride1 = ${s}Shape[2] * stride2;`, g = `int stride0 = ${s}Shape[1] * stride1;`;
@@ -24486,11 +24487,11 @@ function oK(e, t) {
 function uK(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = t[4], a = t[3] * r, i = t[2] * a, o = t[1] * i, { newShape: u, keptDims: l } = w.squeezeShape(t);
   if (u.length < t.length) {
-    let m = au(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
+    let m = ru(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
     return `
-      ${su(m)}
+      ${nu(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${iu(g, l)});
+        return ${s}(${au(g, l)});
       }
     `;
   }
@@ -24501,7 +24502,7 @@ function uK(e) {
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${a}, ${r})) +
           depth3;
-        ${ru(e)}
+        ${su(e)}
       }
     `;
   let c = e.shapeInfo.flatOffset, p = e.shapeInfo.texShape, d = p[0], h = p[1];
@@ -24543,12 +24544,12 @@ function uK(e) {
 function lK(e) {
   let t = e.shapeInfo.logicalShape, n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), { newShape: r, keptDims: a } = w.squeezeShape(t);
   if (r.length < t.length) {
-    let g = au(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
+    let g = ru(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
     return `
-      ${su(g)}
+      ${nu(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${iu(b, a)});
+        return ${s}(${au(b, a)});
       }
     `;
   }
@@ -24563,7 +24564,7 @@ function lK(e) {
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;
   let p = e.shapeInfo.flatOffset, d = e.shapeInfo.texShape, h = d[0], f = d[1];
@@ -24607,7 +24608,7 @@ function lK(e) {
     }
   `;
 }
-function ru(e) {
+function su(e) {
   let t = e.name, n = w.sizeFromShape(e.shapeInfo.logicalShape);
   return n < 2 ? `return ${t};` : `
     for (int i = 0; i < ${n}; i++) {
@@ -24682,15 +24683,15 @@ function ot(e) {
     return "ivec6";
   throw Error(`GPU for rank ${e} is not yet supported`);
 }
-function hv(e, t, n) {
+function pv(e, t, n) {
   let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, i = e && a === 3 && t[0] === 1, o = i ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || i;
   return { useSqueezeShape: u, uniformShape: u ? o : t, keptDims: r };
 }
-function au(e, t) {
+function ru(e, t) {
   let n = JSON.parse(JSON.stringify(e));
   return n.shapeInfo.logicalShape = t, n;
 }
-function iu(e, t) {
+function au(e, t) {
   return t.map((n) => e[n]).join(", ");
 }
 function pK(e, t, n, s) {
@@ -24711,7 +24712,7 @@ function pK(e, t, n, s) {
     v[k] = e.getUniformLocation(l, x.name, d);
   }), { program: t, fragmentShader: u, source: o, webGLProgram: l, uniformLocations: h, customUniformLocations: v, inShapeInfos: a, outShapeInfo: i, infLoc: c, nanLoc: p, inShapesLocations: f, inTexShapesLocations: m, outShapeLocation: g, outShapeStridesLocation: y, outTexShapeLocation: b };
 }
-function nw(e, t) {
+function tw(e, t) {
   if (e.length !== t.length)
     throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);
   e.forEach((n, s) => {
@@ -24726,12 +24727,12 @@ function nw(e, t) {
   });
 }
 function hK(e, t, n, s, r) {
-  t.program.enableShapeUniforms || (nw(t.inShapeInfos, n), nw([t.outShapeInfo], [s]));
+  t.program.enableShapeUniforms || (tw(t.inShapeInfos, n), tw([t.outShapeInfo], [s]));
   let a = s.texData.texture, i = s.texData.texShape;
   s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, i[0], i[1]) : e.setOutputMatrixTexture(a.texture, i[0], i[1]), e.setProgram(t.webGLProgram), X().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
     let c = t.program.variableNames[l], p = t.uniformLocations[c], d = t.uniformLocations[`offset${c}`], h = t.inShapesLocations[`${c}Shape`], f = t.inTexShapesLocations[`${c}TexShape`];
     if (h) {
-      let { uniformShape: m } = hv(t.program.packedInputs, u.shape, u.texData.texShape);
+      let { uniformShape: m } = pv(t.program.packedInputs, u.shape, u.texData.texShape);
       switch (m.length) {
         case 1:
           e.gl.uniform1iv(h, new Int32Array(m));
@@ -24823,7 +24824,7 @@ function fK(e, t, n) {
   t.concat(n).forEach((i) => {
     let o = i.texData != null && i.texData.slice != null && i.texData.slice.flatOffset > 0;
     if (e.enableShapeUniforms && !i.isUniform) {
-      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = hv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
+      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = pv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
       if (c.length === 1 && e.packedInputs) {
         let k = [Math.ceil(u[0] / 2), Math.ceil(u[1] / 2)];
         d = `${k[0] > 1}_${k[1] > 1}`;
@@ -24852,7 +24853,7 @@ var mK = class {
     let t = hn();
     this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms ? qp(["r", "c", "d"], e) : gi(["r", "c", "d"], e)}
+        ${this.enableShapeUniforms ? Hp(["r", "c", "d"], e) : gi(["r", "c", "d"], e)}
         return ivec3(r, c, d);
       }
 
@@ -24879,7 +24880,7 @@ var gK = class {
     let t = hn();
     this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms ? qp(["r", "c", "d"], e) : gi(["r", "c", "d"], e)}
+        ${this.enableShapeUniforms ? Hp(["r", "c", "d"], e) : gi(["r", "c", "d"], e)}
         return ivec3(r, c, d);
       }
 
@@ -24936,7 +24937,7 @@ var vK = class {
     this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length);
     let s = "result";
     t && (s = "floor(result * 255. + 0.5)"), this.userCode = `
-      ${this.enableShapeUniforms ? pv() : dv(e)}
+      ${this.enableShapeUniforms ? dv() : cv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -25009,7 +25010,7 @@ var xK = class {
         `;
       }
     this.userCode = `
-        ${this.enableShapeUniforms ? pv() : dv(e)}
+        ${this.enableShapeUniforms ? dv() : cv(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -25028,7 +25029,7 @@ var xK = class {
   }
 };
 var wK = {};
-Ae(wK, { bindVertexProgramAttributeStreams: () => g1, createBufferFromOutputTexture: () => v1, createFloat16MatrixTexture: () => p1, createFloat16PackedMatrixTexture: () => m1, createFloat32MatrixTexture: () => d1, createIndexBuffer: () => c1, createPackedMatrixTexture: () => f1, createUnsignedBytesMatrixTexture: () => h1, createVertexBuffer: () => l1, createVertexShader: () => u1, downloadByteEncodedFloatMatrixFromOutputTexture: () => w1, downloadFloat32MatrixFromBuffer: () => x1, downloadMatrixFromPackedOutputTexture: () => I1, downloadPackedMatrixFromBuffer: () => k1, getInternalFormatForFloat16MatrixTexture: () => mv, getInternalFormatForFloat16PackedMatrixTexture: () => yv, getInternalFormatForFloat32MatrixTexture: () => fv, getInternalFormatForPackedMatrixTexture: () => bv, getInternalFormatForUnsignedBytesMatrixTexture: () => gv, uploadDenseMatrixToTexture: () => b1, uploadPixelDataToTexture: () => y1 });
+Ae(wK, { bindVertexProgramAttributeStreams: () => g1, createBufferFromOutputTexture: () => v1, createFloat16MatrixTexture: () => p1, createFloat16PackedMatrixTexture: () => m1, createFloat32MatrixTexture: () => d1, createIndexBuffer: () => c1, createPackedMatrixTexture: () => f1, createUnsignedBytesMatrixTexture: () => h1, createVertexBuffer: () => l1, createVertexShader: () => u1, downloadByteEncodedFloatMatrixFromOutputTexture: () => w1, downloadFloat32MatrixFromBuffer: () => x1, downloadMatrixFromPackedOutputTexture: () => I1, downloadPackedMatrixFromBuffer: () => k1, getInternalFormatForFloat16MatrixTexture: () => fv, getInternalFormatForFloat16PackedMatrixTexture: () => bv, getInternalFormatForFloat32MatrixTexture: () => hv, getInternalFormatForPackedMatrixTexture: () => gv, getInternalFormatForUnsignedBytesMatrixTexture: () => mv, uploadDenseMatrixToTexture: () => b1, uploadPixelDataToTexture: () => y1 });
 function u1(e) {
   let t = hn(), n = `${t.version}
     precision highp float;
@@ -25050,48 +25051,48 @@ function c1(e) {
   let t = new Uint16Array([0, 1, 2, 2, 1, 3]);
   return VC(e, t);
 }
-function Ql(e, t, n, s, r, a) {
+function Yl(e, t, n, s, r, a) {
   UC(t, n);
   let i = WC(e), o = e.TEXTURE_2D;
   return fe(e, () => e.bindTexture(o, i)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_S, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_WRAP_T, e.CLAMP_TO_EDGE)), fe(e, () => e.texParameteri(o, e.TEXTURE_MIN_FILTER, e.NEAREST)), fe(e, () => e.texParameteri(o, e.TEXTURE_MAG_FILTER, e.NEAREST)), X().getNumber("WEBGL_VERSION") === 1 ? fe(e, () => e.texImage2D(o, 0, s, t, n, 0, r, a, null)) : fe(e, () => e.texStorage2D(o, 1, s, t, n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null)), { texture: i, texShape: [n, t] };
 }
-function fv(e) {
+function hv(e) {
   return e.internalFormatFloat;
 }
 function d1(e, t, n, s) {
-  let [r, a] = Yl(t, n);
-  return Ql(e, r, a, fv(s), s.textureFormatFloat, e.FLOAT);
+  let [r, a] = Xl(t, n);
+  return Yl(e, r, a, hv(s), s.textureFormatFloat, e.FLOAT);
 }
-function mv(e) {
+function fv(e) {
   return e.internalFormatHalfFloat;
 }
 function p1(e, t, n, s) {
-  let [r, a] = Yl(t, n);
-  return Ql(e, r, a, mv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
+  let [r, a] = Xl(t, n);
+  return Yl(e, r, a, fv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
 }
-function gv(e) {
+function mv(e) {
   return e.downloadTextureFormat;
 }
 function h1(e, t, n, s) {
-  let [r, a] = Yl(t, n);
-  return Ql(e, r, a, gv(s), e.RGBA, e.UNSIGNED_BYTE);
+  let [r, a] = Xl(t, n);
+  return Yl(e, r, a, mv(s), e.RGBA, e.UNSIGNED_BYTE);
 }
-function bv(e) {
+function gv(e) {
   return e.internalFormatPackedFloat;
 }
 function f1(e, t, n, s) {
-  let [r, a] = tu(t, n);
-  return Ql(e, r, a, bv(s), e.RGBA, e.FLOAT);
+  let [r, a] = eu(t, n);
+  return Yl(e, r, a, gv(s), e.RGBA, e.FLOAT);
 }
-function yv(e) {
+function bv(e) {
   return e.internalFormatPackedHalfFloat;
 }
 function m1(e, t, n, s) {
-  let [r, a] = tu(t, n);
-  return Ql(e, r, a, yv(s), e.RGBA, s.textureTypeHalfFloat);
+  let [r, a] = eu(t, n);
+  return Yl(e, r, a, bv(s), e.RGBA, s.textureTypeHalfFloat);
 }
 function g1(e, t, n) {
-  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Lm(e, t, "clipSpacePos", n, 3, 20, 0) && Lm(e, t, "uv", n, 2, 20, 12);
+  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Mm(e, t, "clipSpacePos", n, 3, 20, 0) && Mm(e, t, "uv", n, 2, 20, 12);
 }
 function b1(e, t, n, s, r, a) {
   fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
@@ -25112,7 +25113,7 @@ function x1(e, t, n) {
   return s.bindBuffer(s.PIXEL_PACK_BUFFER, t), s.getBufferSubData(s.PIXEL_PACK_BUFFER, 0, r), s.bindBuffer(s.PIXEL_PACK_BUFFER, null), r;
 }
 function w1(e, t, n, s) {
-  let [r, a] = Yl(t, n), i = 4, o = new Uint8Array(m5(t * n, i));
+  let [r, a] = Xl(t, n), i = 4, o = new Uint8Array(m5(t * n, i));
   return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, o)), new Float32Array(o.buffer);
 }
 function k1(e, t, n, s, r, a, i, o) {
@@ -25123,7 +25124,7 @@ function I1(e, t, n) {
   let s = new Float32Array(t * n * 4);
   return fe(e, () => e.readPixels(0, 0, n, t, e.RGBA, e.FLOAT, s)), s;
 }
-var jf = class {
+var qf = class {
   constructor(e) {
     this.outputTexture = null, this.program = null, this.disposed = false, this.vertexAttrsAreBound = false, this.itemsToPoll = [];
     let t = X().getNumber("WEBGL_VERSION");
@@ -25131,12 +25132,12 @@ var jf = class {
     let n = "WEBGL_color_buffer_float", s = "EXT_color_buffer_half_float";
     if (X().getNumber("WEBGL_VERSION") === 1) {
       let r = "OES_texture_float", a = "OES_texture_half_float";
-      if (this.textureFloatExtension = Fu(this.gl, r), Mn(this.gl, a))
-        this.textureHalfFloatExtension = Fu(this.gl, a);
+      if (this.textureFloatExtension = Du(this.gl, r), Mn(this.gl, a))
+        this.textureHalfFloatExtension = Du(this.gl, a);
       else if (X().get("WEBGL_FORCE_F16_TEXTURES"))
         throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
       if (this.colorBufferFloatExtension = this.gl.getExtension(n), Mn(this.gl, s))
-        this.colorBufferHalfFloatExtension = Fu(this.gl, s);
+        this.colorBufferHalfFloatExtension = Du(this.gl, s);
       else if (X().get("WEBGL_FORCE_F16_TEXTURES"))
         throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
     } else if (n = "EXT_color_buffer_float", Mn(this.gl, n))
@@ -25145,7 +25146,7 @@ var jf = class {
       this.colorBufferHalfFloatExtension = this.gl.getExtension(s);
     else
       throw new Error("GL context does not support color renderable floats");
-    this.vertexBuffer = l1(this.gl), this.indexBuffer = c1(this.gl), this.framebuffer = GC(this.gl), this.textureConfig = cv(this.gl, this.textureHalfFloatExtension);
+    this.vertexBuffer = l1(this.gl), this.indexBuffer = c1(this.gl), this.framebuffer = GC(this.gl), this.textureConfig = lv(this.gl, this.textureHalfFloatExtension);
   }
   get debug() {
     return X().getBool("DEBUG");
@@ -25179,7 +25180,7 @@ var jf = class {
     return this.throwIfDisposed(), f1(this.gl, e, t, this.textureConfig);
   }
   deleteMatrixTexture(e) {
-    this.throwIfDisposed(), this.outputTexture === e && (Bm(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
+    this.throwIfDisposed(), this.outputTexture === e && (Lm(this.gl, this.framebuffer), this.outputTexture = null), fe(this.gl, () => this.gl.deleteTexture(e));
   }
   downloadByteEncodedFloatMatrixFromOutputTexture(e, t, n) {
     return this.downloadMatrixDriver(e, () => w1(this.gl, t, n, this.textureConfig));
@@ -25219,13 +25220,13 @@ var jf = class {
     let t = this.gl;
     this.vertexShader == null && (this.vertexShader = u1(t));
     let n = MC(t);
-    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), LC(t, n), this.debug && rd(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = g1(t, this.program, this.vertexBuffer)), n;
+    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), LC(t, n), this.debug && sd(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = g1(t, this.program, this.vertexBuffer)), n;
   }
   deleteProgram(e) {
     this.throwIfDisposed(), e === this.program && (this.program = null), e != null && fe(this.gl, () => this.gl.deleteProgram(e));
   }
   setProgram(e) {
-    this.throwIfDisposed(), this.program = e, this.program != null && this.debug && rd(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
+    this.throwIfDisposed(), this.program = e, this.program != null && this.debug && sd(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
   }
   getUniformLocation(e, t, n = true) {
     return this.throwIfDisposed(), n ? qC(this.gl, e, t) : jC(this.gl, e, t);
@@ -25244,7 +25245,7 @@ var jf = class {
   }
   setOutputPackedMatrixTexture(e, t, n) {
     this.throwIfDisposed();
-    let [s, r] = tu(t, n);
+    let [s, r] = eu(t, n);
     this.setOutputMatrixTextureDriver(e, s, r);
   }
   setOutputMatrixWriteRegion(e, t, n, s) {
@@ -25254,7 +25255,7 @@ var jf = class {
     throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
   }
   debugValidate() {
-    this.program != null && rd(this.gl, this.program), Ou(this.gl);
+    this.program != null && sd(this.gl, this.program), Fu(this.gl);
   }
   executeProgram() {
     this.throwIfDisposed(), this.throwIfNoProgram();
@@ -25265,7 +25266,7 @@ var jf = class {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.finish());
   }
   getQueryTimerExtension() {
-    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Fu(this.gl, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
+    return this.disjointQueryTimerExtension == null && (this.disjointQueryTimerExtension = Du(this.gl, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query")), this.disjointQueryTimerExtension;
   }
   getQueryTimerExtensionWebGL2() {
     return this.getQueryTimerExtension();
@@ -25332,10 +25333,10 @@ var jf = class {
     this.itemsToPoll.push({ isDoneFn: e, resolveFn: t }), !(this.itemsToPoll.length > 1) && w.repeatedTry(() => (this.pollItems(), this.itemsToPoll.length === 0));
   }
   bindTextureToFrameBuffer(e) {
-    this.throwIfDisposed(), ad(this.gl, e, this.framebuffer), this.debug && Ou(this.gl);
+    this.throwIfDisposed(), rd(this.gl, e, this.framebuffer), this.debug && Fu(this.gl);
   }
   unbindTextureToFrameBuffer() {
-    this.outputTexture != null ? (ad(this.gl, this.outputTexture, this.framebuffer), this.debug && Ou(this.gl)) : Bm(this.gl, this.framebuffer);
+    this.outputTexture != null ? (rd(this.gl, this.outputTexture, this.framebuffer), this.debug && Fu(this.gl)) : Lm(this.gl, this.framebuffer);
   }
   downloadMatrixDriver(e, t) {
     this.bindTextureToFrameBuffer(e);
@@ -25345,7 +25346,7 @@ var jf = class {
   setOutputMatrixTextureDriver(e, t, n) {
     this.throwIfDisposed();
     let s = this.gl;
-    ad(s, e, this.framebuffer), this.debug && Ou(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
+    rd(s, e, this.framebuffer), this.debug && Fu(s), this.outputTexture = e, fe(s, () => s.viewport(0, 0, t, n)), fe(s, () => s.scissor(0, 0, t, n));
   }
   setOutputMatrixWriteRegionDriver(e, t, n, s) {
     this.throwIfDisposed(), fe(this.gl, () => this.gl.scissor(e, t, n, s));
@@ -25365,7 +25366,7 @@ function kK(e) {
     ;
   return t - 1;
 }
-var { addImpl: IK, bincountImpl: S1, bincountReduceImpl: SK, ceilImpl: CK, concatImpl: NK, equalImpl: TK, expImpl: $K, expm1Impl: _K, floorImpl: AK, gatherNdImpl: EK, gatherV2Impl: RK, greaterImpl: DK, greaterEqualImpl: FK, lessImpl: OK, lessEqualImpl: PK, linSpaceImpl: zK, logImpl: MK, maxImpl: LK, maximumImpl: BK, minimumImpl: VK, multiplyImpl: WK, negImpl: UK, notEqualImpl: GK, prodImpl: HK, rangeImpl: qK, rsqrtImpl: jK, sigmoidImpl: KK, simpleAbsImpl: C1, sliceImpl: XK, sparseFillEmptyRowsImpl: YK, sparseReshapeImpl: QK, sparseSegmentReductionImpl: N1, sqrtImpl: ZK, stridedSliceImpl: JK, stringNGramsImpl: eX, stringSplitImpl: tX, stringToHashBucketFastImpl: nX, subImpl: sX, tileImpl: rX, topKImpl: aX, transposeImpl: vv, uniqueImpl: iX } = Qy;
+var { addImpl: IK, bincountImpl: S1, bincountReduceImpl: SK, ceilImpl: CK, concatImpl: NK, equalImpl: TK, expImpl: $K, expm1Impl: _K, floorImpl: AK, gatherNdImpl: EK, gatherV2Impl: RK, greaterImpl: DK, greaterEqualImpl: FK, lessImpl: OK, lessEqualImpl: PK, linSpaceImpl: zK, logImpl: MK, maxImpl: LK, maximumImpl: BK, minimumImpl: VK, multiplyImpl: WK, negImpl: UK, notEqualImpl: GK, prodImpl: HK, rangeImpl: qK, rsqrtImpl: jK, sigmoidImpl: KK, simpleAbsImpl: C1, sliceImpl: XK, sparseFillEmptyRowsImpl: YK, sparseReshapeImpl: QK, sparseSegmentReductionImpl: N1, sqrtImpl: ZK, stridedSliceImpl: JK, stringNGramsImpl: eX, stringSplitImpl: tX, stringToHashBucketFastImpl: nX, subImpl: sX, tileImpl: rX, topKImpl: aX, transposeImpl: yv, uniqueImpl: iX } = Yy;
 function T1(e, t) {
   return ["x", "y", "z", "w", "u", "v"].slice(0, t).map((n) => `${e}.${n}`);
 }
@@ -25467,7 +25468,7 @@ var $1 = class {
     }
     this.userCode = `
       ${lX(t, this.enableShapeUniforms)}
-      ${this.enableShapeUniforms ? pv() : dv(e)}
+      ${this.enableShapeUniforms ? dv() : cv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -25498,9 +25499,9 @@ var cX = class {
     this.gpgpu = e, this.numUsedTextures = 0, this.numFreeTextures = 0, this._numBytesAllocated = 0, this._numBytesFree = 0, this.freeTextures = {}, this.logEnabled = false, this.usedTextures = {};
   }
   acquireTexture(e, t, n) {
-    let s = rw(t, n), r = aw(e, s, n);
+    let s = sw(t, n), r = rw(e, s, n);
     r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []);
-    let a = sw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
+    let a = nw(e, s, this.gpgpu.gl, this.gpgpu.textureConfig, n);
     if (this.freeTextures[r].length > 0) {
       this.numFreeTextures--, this.numUsedTextures++, this._numBytesFree -= a, this.log();
       let o = this.freeTextures[r].shift();
@@ -25512,9 +25513,9 @@ var cX = class {
   releaseTexture(e, t, n, s) {
     if (this.freeTextures == null)
       return;
-    let r = rw(n, s), a = aw(t, r, s);
+    let r = sw(n, s), a = rw(t, r, s);
     a in this.freeTextures || (this.freeTextures[a] = []);
-    let i = sw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
+    let i = nw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
     o !== -1 && this._numBytesAllocated > o ? (this.gpgpu.deleteMatrixTexture(e.texture), this._numBytesAllocated -= i) : (this.freeTextures[a].push(e), this.numFreeTextures++, this._numBytesFree += i), this.numUsedTextures--;
     let u = this.usedTextures[a], l = u.indexOf(e);
     if (l < 0)
@@ -25571,13 +25572,13 @@ function dX(e, t) {
     return 4;
   throw new Error(`Unknown internal format ${t}`);
 }
-function sw(e, t, n, s, r) {
+function nw(e, t, n, s, r) {
   let a = pX(t, s), i;
   if (r) {
-    let [u, l] = tu(e[0], e[1]);
+    let [u, l] = eu(e[0], e[1]);
     i = u * l;
   } else {
-    let [u, l] = Yl(e[0], e[1]);
+    let [u, l] = Xl(e[0], e[1]);
     i = u * l;
   }
   let o = dX(n, a);
@@ -25586,15 +25587,15 @@ function sw(e, t, n, s, r) {
 function pX(e, t) {
   switch (e) {
     case 3:
-      return bv(t);
-    case 4:
-      return yv(t);
-    case 1:
-      return fv(t);
-    case 0:
-      return mv(t);
-    case 2:
       return gv(t);
+    case 4:
+      return bv(t);
+    case 1:
+      return hv(t);
+    case 0:
+      return fv(t);
+    case 2:
+      return mv(t);
     default:
       throw new Error(`Unknown physical texture type ${e}`);
   }
@@ -25602,7 +25603,7 @@ function pX(e, t) {
 function hX(e) {
   return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? e ? 3 : 1 : e ? 4 : 0;
 }
-function rw(e, t) {
+function sw(e, t) {
   if (e === 1)
     return 3;
   if (e === 0 || e == null)
@@ -25611,7 +25612,7 @@ function rw(e, t) {
     return 2;
   throw new Error(`Unknown logical texture type ${e}`);
 }
-function aw(e, t, n) {
+function rw(e, t, n) {
   return `${e[0]}_${e[1]}_${t}_${n}`;
 }
 var Hs = class {
@@ -25632,7 +25633,7 @@ var Hs = class {
 };
 var ts = "if (isnan(x)) return x;";
 var fX = "return x;";
-var iw = "return abs(x);";
+var aw = "return abs(x);";
 var mX = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
 var gX = ts + `
   return (x < 0.0) ? 0.0 : x;
@@ -25709,34 +25710,34 @@ var SX = class {
 var CX = xs.whereImpl;
 var NX = 1e-7;
 var TX = 1e-4;
-var Zc = {};
+var Qc = {};
 function $X(e) {
-  return e in Zc || (Zc[e] = {}), Zc[e];
+  return e in Qc || (Qc[e] = {}), Qc[e];
 }
 var _X = X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
 var AX = 600;
 function EX() {
   return X().global.screen == null ? 1024 : X().global.screen.height * X().global.screen.width * window.devicePixelRatio * AX / 1024 / 1024;
 }
-var _1 = class extends nl {
+var _1 = class extends tl {
   constructor(e) {
     super();
     if (this.pendingRead = /* @__PURE__ */ new WeakMap(), this.pendingDisposal = /* @__PURE__ */ new WeakSet(), this.dataRefCount = /* @__PURE__ */ new WeakMap(), this.numBytesInGPU = 0, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.lastGlFlushTime = 0, this.warnedAboutMemory = false, this.pendingDeletes = 0, this.disposed = false, !X().getBool("HAS_WEBGL"))
       throw new Error("WebGL is not supported on this device");
     let t;
     if (e != null) {
-      if (e instanceof jf)
+      if (e instanceof qf)
         t = e;
       else {
         let n = vs(X().getNumber("WEBGL_VERSION"), e);
-        t = new jf(n);
+        t = new qf(n);
       }
       this.binaryCache = {}, this.gpgpuCreatedLocally = false;
     } else {
       let n = vs(X().getNumber("WEBGL_VERSION"));
-      t = new jf(n), this.binaryCache = $X(X().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
+      t = new qf(n), this.binaryCache = $X(X().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
     }
-    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new cX(this.gpgpu), this.numMBBeforeWarning = EX(), this.texData = new Ud(this, Ss());
+    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new cX(this.gpgpu), this.numMBBeforeWarning = EX(), this.texData = new Wd(this, Ss());
   }
   nextDataId() {
     return _1.nextDataId++;
@@ -25813,7 +25814,7 @@ var _1 = class extends nl {
     if (a !== "complex64" && X().get("WEBGL_BUFFER_SUPPORTED")) {
       l = this.decode(e);
       let h = this.texData.get(l.dataId);
-      u = this.gpgpu.createBufferFromTexture(h.texture.texture, ...Yc(s));
+      u = this.gpgpu.createBufferFromTexture(h.texture.texture, ...Xc(s));
     }
     this.pendingRead.set(e, []), a !== "complex64" && await this.gpgpu.createAndWaitForFence();
     let c;
@@ -25869,10 +25870,10 @@ var _1 = class extends nl {
   getValuesFromTexture(e) {
     let { shape: t, dtype: n, isPacked: s } = this.texData.get(e), r = w.sizeFromShape(t);
     if (X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
-      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Yc(t)).subarray(0, r);
+      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Xc(t)).subarray(0, r);
       return this.disposeIntermediateTensorInfo(p), h;
     }
-    let a = X().getBool("WEBGL_PACK") && s === true, i = a ? id(t) : t, o = a ? new yK(i) : new bK(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
+    let a = X().getBool("WEBGL_PACK") && s === true, i = a ? ad(t) : t, o = a ? new yK(i) : new bK(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
     return this.disposeIntermediateTensorInfo(u), c;
   }
   timerAvailable() {
@@ -25954,8 +25955,8 @@ var _1 = class extends nl {
       return this.makeOutput(e.shape, e.dtype, s);
     }
     if (X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
-      return this.packedUnaryOp(e, iw, e.dtype);
-    let t = new Hs(e.shape, iw), n = this.compileAndRun(t, [e]);
+      return this.packedUnaryOp(e, aw, e.dtype);
+    let t = new Hs(e.shape, aw), n = this.compileAndRun(t, [e]);
     return Ss().makeTensorFromDataId(n.dataId, n.shape, n.dtype);
   }
   makeTensorInfo(e, t, n) {
@@ -25989,15 +25990,15 @@ var _1 = class extends nl {
       let p = w.sizeFromShape(r), d = t[0] * t[1] * 4;
       w.assert(p <= d, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
     }
-    let i = id(r), o;
+    let i = ad(r), o;
     s ? o = new gK(i) : o = new mK(i);
-    let u = true, l = [t != null ? t : Yc(i)], c = this.runWebGLProgram(o, [{ shape: i, dtype: a, dataId: e }], a, l, u, t);
+    let u = true, l = [t != null ? t : Xc(i)], c = this.runWebGLProgram(o, [{ shape: i, dtype: a, dataId: e }], a, l, u, t);
     return { dtype: a, shape: r, dataId: c.dataId };
   }
   runWebGLProgram(e, t, n, s, r = false, a) {
     let i = this.makeTensorInfo(e.outputShape, n), o = this.texData.get(i.dataId);
     if (e.packedOutput && (o.isPacked = true), e.outPackingScheme === 0) {
-      let g = a != null ? a : Yc(e.outputShape);
+      let g = a != null ? a : Xc(e.outputShape);
       o.texShape = g.map((b) => b * 2);
     }
     if (e.outTexUsage != null && (o.usage = e.outTexUsage), w.sizeFromShape(i.shape) === 0)
@@ -26013,7 +26014,7 @@ var _1 = class extends nl {
       }
       if (this.uploadToGPU(g.dataId), !!b.isPacked != !!e.packedInputs)
         g = b.isPacked ? this.unpackTensor(g) : this.packTensor(g), u.push(g), b = this.texData.get(g.dataId);
-      else if (b.isPacked && !el(b.shape, g.shape)) {
+      else if (b.isPacked && !Ju(b.shape, g.shape)) {
         let y = g, v = g.shape;
         g.shape = b.shape, g = this.packedReshape(g, v), u.push(g), b = this.texData.get(g.dataId), y.shape = v;
       }
@@ -26070,8 +26071,8 @@ var _1 = class extends nl {
     u && (l = w.now());
     let c = t.texShape;
     if (c == null && (c = QC(n, o), t.texShape = c), r != null) {
-      let p = id(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
-      (o || !m) && ([h, f] = tu(c[0], c[1])), o ? d = new xK(p, m) : d = new vK(p, m);
+      let p = ad(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
+      (o || !m) && ([h, f] = eu(c[0], c[1])), o ? d = new xK(p, m) : d = new vK(p, m);
       let g = m ? [f, h] : c, b = this.makeTensorInfo(g, s), y = this.texData.get(b.dataId);
       m ? y.usage = 2 : y.usage = 1, y.texShape = g, this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId), h, f, r);
       let v = [[f, h]], x = true, k = this.runWebGLProgram(d, [b], s, v, x), C = this.texData.get(k.dataId);
@@ -26113,13 +26114,13 @@ var xpe = "0.0.0";
 function DX() {
   X().set("WEBGL_FORCE_F16_TEXTURES", true);
 }
-dp.isBrowser() && pp("webgl", () => new A1(), 2);
+cp.isBrowser() && dp("webgl", () => new A1(), 2);
 var wpe = { forceHalfFloat: DX };
 var E1 = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `;
-var so = class {
+var no = class {
   constructor(e, t, n) {
     this.variableNames = ["A", "B"], this.outputShape = N.assertAndGetBroadcastShape(t, n), this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
       float binaryOperation(float a, float b) {
@@ -26134,13 +26135,13 @@ var so = class {
     `;
   }
 };
-var jp = `
+var qp = `
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
 `;
-var Zl = class {
+var Ql = class {
   constructor(e, t, n, s = false) {
     this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = N.assertAndGetBroadcastShape(t, n);
     let r = this.outputShape.length;
@@ -26211,14 +26212,14 @@ function Er(e) {
   let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.texData.get(a.dataId), o = En({ inputs: { x: s }, backend: n }), u = En({ inputs: { x: r }, backend: n });
   return i.complexTensorInfos = { real: o, imag: u }, a;
 }
-var OX = { kernelName: jd, backendName: "webgl", kernelFunc: Er };
+var OX = { kernelName: qd, backendName: "webgl", kernelFunc: Er };
 var R1 = "return (a < 0.) ? b * a : a;";
 var D1 = `
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
 function PX(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(D1, r.shape, i.shape) : new so(R1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ql(D1, r.shape, i.shape) : new no(R1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
   return n.disposeIntermediateTensorInfo(i), u;
 }
 var zX = { kernelName: La, backendName: "webgl", kernelFunc: PX };
@@ -26228,11 +26229,11 @@ var O1 = `
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
 `;
 function MX(e) {
-  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(O1, s.shape, r.shape) : new so(F1, s.shape, r.shape);
+  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ql(O1, s.shape, r.shape) : new no(F1, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], "float32");
 }
 var LX = { kernelName: Qa, backendName: "webgl", kernelFunc: MX };
-var ou = "if (isnan(x)) return x;";
+var iu = "if (isnan(x)) return x;";
 var BX = `
   if (isnan(a)) return a;
   if (isnan(b)) return b;
@@ -26259,7 +26260,7 @@ function qt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, sup
     let { a: u, b: l } = i, c = o;
     if (s && u.dtype === "complex64") {
       let f = c.texData.get(u.dataId), m = c.texData.get(l.dataId), [g, b] = [[f.complexTensorInfos.real, m.complexTensorInfos.real], [f.complexTensorInfos.imag, m.complexTensorInfos.imag]].map((v) => {
-        let [x, k] = v, C = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, T = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new so(e, u.shape, l.shape);
+        let [x, k] = v, C = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, T = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new no(e, u.shape, l.shape);
         return c.runWebGLProgram(E, [C, T], yn(x.dtype, k.dtype));
       }), y = Er({ inputs: { real: g, imag: b }, backend: c });
       return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(b), y;
@@ -26270,10 +26271,10 @@ function qt({ opSnippet: e, packedOpSnippet: t, checkOutOfBounds: n = false, sup
       return k.values = y, x;
     }
     let d = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t != null, h;
-    return d ? h = new Zl(t, u.shape, l.shape, n) : h = new so(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
+    return d ? h = new Ql(t, u.shape, l.shape, n) : h = new no(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
   };
 }
-function Kp(e, t = false) {
+function jp(e, t = false) {
   if (e === "linear")
     return t ? vX : fX;
   if (e === "relu")
@@ -26340,8 +26341,8 @@ var P1 = class {
     `;
   }
 };
-var ow = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
-var uw = class {
+var iw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
+var ow = class {
   constructor(e, t, n) {
     this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = N.assertAndGetBroadcastShape(t, n), this.userCode = `
       float binaryOpComplex(
@@ -26359,11 +26360,11 @@ var uw = class {
     `;
   }
 };
-var lw = "return a * b;";
-function xv(e) {
+var uw = "return a * b;";
+function vv(e) {
   let { inputs: t, backend: n } = e, { a: s, b: r } = t, a = N.upcastType(s.dtype, r.dtype);
   if (s.dtype === "complex64") {
-    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new uw(ow.REAL, s.shape, r.shape), c = new uw(ow.IMAG, s.shape, r.shape), p = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Er({ inputs: { real: d, imag: h }, backend: n });
+    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new ow(iw.REAL, s.shape, r.shape), c = new ow(iw.IMAG, s.shape, r.shape), p = [{ dataId: o.complexTensorInfos.real.dataId, dtype: o.complexTensorInfos.real.dtype, shape: s.shape }, { dataId: o.complexTensorInfos.imag.dataId, dtype: o.complexTensorInfos.imag.dtype, shape: s.shape }, { dataId: u.complexTensorInfos.real.dataId, dtype: u.complexTensorInfos.real.dtype, shape: r.shape }, { dataId: u.complexTensorInfos.imag.dataId, dtype: u.complexTensorInfos.imag.dtype, shape: r.shape }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Er({ inputs: { real: d, imag: h }, backend: n });
     return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
   }
   if (n.shouldExecuteOnCPU([s, r])) {
@@ -26371,9 +26372,9 @@ function xv(e) {
     return d.values = l, p;
   }
   let i;
-  return X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Zl(lw, s.shape, r.shape) : i = new so(lw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
+  return X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Ql(uw, s.shape, r.shape) : i = new no(uw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
 }
-var WX = { kernelName: Ka, backendName: "webgl", kernelFunc: xv };
+var WX = { kernelName: Ka, backendName: "webgl", kernelFunc: vv };
 function UX(e, t, n) {
   let s = [ma(e.shape), ...ga(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ma(t), ...ga(t)], i = new $1(a, s), o = true, u = [s], l = n.runWebGLProgram(i, [r], e.dtype, u, o);
   return { dataId: l.dataId, shape: t, dtype: l.dtype };
@@ -26382,10 +26383,10 @@ function he(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = n, o = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, o), l = w.sizeFromShape(u);
   w.assert(o === l, () => `The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);
   let c = i.texData.get(r.dataId);
-  return c.isPacked && !el(r.shape, u) && !(c.texture !== null && el(c.shape, u)) ? UX(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
+  return c.isPacked && !Ju(r.shape, u) && !(c.texture !== null && Ju(c.shape, u)) ? UX(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
 }
-var GX = { kernelName: Eo, backendName: "webgl", kernelFunc: he };
-var cw = class {
+var GX = { kernelName: Ao, backendName: "webgl", kernelFunc: he };
+var lw = class {
   constructor(e, t) {
     this.variableNames = ["x"];
     let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
@@ -26571,7 +26572,7 @@ function yi(e, t, n, s) {
   let r = qX(e.shape), a = e;
   for (let i = 0; i < r.length; i++) {
     let { inSize: o, windowSize: u, outSize: l } = r[i], c, p;
-    n === "mean" ? c = i === 0 ? new cw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new cw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new HX({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
+    n === "mean" ? c = i === 0 ? new lw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new lw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new HX({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, n), p = a, a = s.runWebGLProgram(c, [a], t), p.dataId !== e.dataId && s.disposeIntermediateTensorInfo(p);
   }
   return a;
 }
@@ -26632,43 +26633,43 @@ var XX = class {
     `;
   }
 };
-function Xp(e, t, n) {
+function Kp(e, t, n) {
   let s = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new XX(e.shape, t) : new jX(e.shape, t);
   return n.runWebGLProgram(s, [e], e.dtype);
 }
 function YX(e, t, n, s) {
   let r = t, a = e.shape.length, i = w.parseAxisParam(r, e.shape), o = i, u = N.getAxesPermutation(o, a), l = u != null, c = e;
-  l && (c = Xp(e, u, s), o = N.getInnerMostAxes(o.length, a)), N.assertAxesAreInnerMostDims("sum", o, a);
+  l && (c = Kp(e, u, s), o = N.getInnerMostAxes(o.length, a)), N.assertAxesAreInnerMostDims("sum", o, a);
   let [p, d] = N.computeOutAndReduceShapes(c.shape, o), h = p;
   n && (h = N.expandShapeToKeepDim(p, i));
-  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = he({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = cp(e.dtype), v = yi(b, y, "sum", s), x = he({ inputs: { x: v }, attrs: { shape: h }, backend: s });
+  let f = w.sizeFromShape(d), g = w.sizeFromShape(e.shape) / f, b = he({ inputs: { x: c }, attrs: { shape: [g, f] }, backend: s }), y = lp(e.dtype), v = yi(b, y, "sum", s), x = he({ inputs: { x: v }, attrs: { shape: h }, backend: s });
   return s.disposeIntermediateTensorInfo(b), s.disposeIntermediateTensorInfo(v), l && s.disposeIntermediateTensorInfo(c), x;
 }
-function Yp(e) {
+function Xp(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
   return YX(r, a, i, n);
 }
-var QX = { kernelName: ai, backendName: "webgl", kernelFunc: Yp };
+var QX = { kernelName: ai, backendName: "webgl", kernelFunc: Xp };
 function cn(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { perm: a } = s, i = n, o = r.shape.length, u = new Array(o);
   for (let c = 0; c < u.length; c++)
     u[c] = r.shape[a[c]];
   let l;
   if (i.shouldExecuteOnCPU([r])) {
-    let p = i.texData.get(r.dataId).values, d = vv(p, r.shape, r.dtype, a, u);
+    let p = i.texData.get(r.dataId).values, d = yv(p, r.shape, r.dtype, a, u);
     l = i.makeTensorInfo(u, r.dtype);
     let h = i.texData.get(l.dataId);
     h.values = d;
   } else
-    l = Xp(r, a, i);
+    l = Kp(r, a, i);
   return l;
 }
 var ZX = { kernelName: ci, backendName: "webgl", kernelFunc: cn };
 var z1 = 1e3;
-function zd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = jo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+function Pd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
-  let k = n ? [b, p, h] : [b, h, p], C = s ? [y, f, d] : [y, d, f], T = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: C } }), A = [T, E], P = Math.max(b, y), R = n ? T.shape[1] : T.shape[2], F = a != null, $ = i != null, z = u === "leakyrelu", W = u != null ? Kp(u, true) : null, q = F || $ || z || W != null, K;
+  let k = n ? [b, p, h] : [b, h, p], C = s ? [y, f, d] : [y, d, f], T = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: C } }), A = [T, E], P = Math.max(b, y), R = n ? T.shape[1] : T.shape[2], F = a != null, $ = i != null, z = u === "leakyrelu", W = u != null ? jp(u, true) : null, q = F || $ || z || W != null, K;
   if ((h === 1 || f === 1) && R > z1 && q === false) {
     let Z = T, te = E;
     n && (Z = cn({ inputs: { x: T }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(Z)), s && (te = cn({ inputs: { x: E }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(te));
@@ -26676,8 +26677,8 @@ function zd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
     ee && (ne = he({ inputs: { x: Z }, backend: r, attrs: { shape: [P, R, 1] } }), A.push(ne));
     let oe = f === 1 ? 2 : 1, re = te;
     se && (re = he({ inputs: { x: te }, backend: r, attrs: { shape: [P, 1, R] } }), A.push(re));
-    let le = xv({ inputs: { a: ne, b: re }, backend: r });
-    K = Yp({ inputs: { x: le }, backend: r, attrs: { axis: oe, keepDims: true } }), A.push(le);
+    let le = vv({ inputs: { a: ne, b: re }, backend: r });
+    K = Xp({ inputs: { x: le }, backend: r, attrs: { axis: oe, keepDims: true } }), A.push(le);
   } else {
     let Z = yn(e.dtype, t.dtype), te = new P1(k, C, [P, h, f], n, s, F, W, $, z), ee = [T, E];
     if (a != null && ee.push(a), $ && ee.push(i), z) {
@@ -26694,10 +26695,10 @@ function zd({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
 }
 function JX(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
-  return zd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
+  return Pd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
 }
 var e8 = { kernelName: na, backendName: "webgl", kernelFunc: JX };
-var dw = "return abs(x);";
+var cw = "return abs(x);";
 function t8(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s]) && s.dtype !== "complex64") {
@@ -26705,9 +26706,9 @@ function t8(e) {
     return n.makeTensorInfo(s.shape, s.dtype, i);
   }
   let r;
-  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Yr(s.shape, dw) : r = new Hs(s.shape, dw), n.runWebGLProgram(r, [s], s.dtype);
+  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Yr(s.shape, cw) : r = new Hs(s.shape, cw), n.runWebGLProgram(r, [s], s.dtype);
 }
-var n8 = { kernelName: io, backendName: "webgl", kernelFunc: t8 };
+var n8 = { kernelName: ao, backendName: "webgl", kernelFunc: t8 };
 var s8 = ts + `
   if (abs(x) > 1.) {
     return NAN;
@@ -26715,14 +26716,14 @@ var s8 = ts + `
   return acos(x);
 `;
 var r8 = Ke({ opSnippet: s8 });
-var a8 = { kernelName: sl, backendName: "webgl", kernelFunc: r8 };
+var a8 = { kernelName: nl, backendName: "webgl", kernelFunc: r8 };
 var i8 = ts + `
   if (x < 1.0) return NAN;
 return log(x + sqrt(x * x - 1.0));`;
 var o8 = Ke({ opSnippet: i8 });
-var u8 = { kernelName: rl, backendName: "webgl", kernelFunc: o8 };
-var pw = "return a + b;";
-var l8 = qt({ opSnippet: pw, packedOpSnippet: pw, supportsComplex: true, cpuKernelImpl: IK });
+var u8 = { kernelName: sl, backendName: "webgl", kernelFunc: o8 };
+var dw = "return a + b;";
+var l8 = qt({ opSnippet: dw, packedOpSnippet: dw, supportsComplex: true, cpuKernelImpl: IK });
 var c8 = { kernelName: kr, backendName: "webgl", kernelFunc: l8 };
 var d8 = class {
   constructor(e, t) {
@@ -26762,18 +26763,18 @@ var p8 = class {
     `;
   }
 };
-function ld(e) {
+function ud(e) {
   let { inputs: t, backend: n } = e, s = t;
   if (s.length === 1)
     return En({ inputs: { x: s[0] }, backend: n });
   if (s.length > X().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
-    let u = Math.floor(s.length / 2), l = ld({ inputs: s.slice(0, u), backend: n }), c = ld({ inputs: s.slice(u), backend: n });
-    return ld({ inputs: [l, c], backend: n });
+    let u = Math.floor(s.length / 2), l = ud({ inputs: s.slice(0, u), backend: n }), c = ud({ inputs: s.slice(u), backend: n });
+    return ud({ inputs: [l, c], backend: n });
   }
   let r = s.map((u) => u.dtype).reduce((u, l) => yn(u, l)), a = s.map((u) => u.shape), o = X().getBool("WEBGL_PACK") ? new p8(s[0].shape, a) : new d8(s[0].shape, a);
   return n.runWebGLProgram(o, s, r);
 }
-var h8 = { kernelName: xa, backendName: "webgl", kernelFunc: ld };
+var h8 = { kernelName: xa, backendName: "webgl", kernelFunc: ud };
 function f8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = N.getAxesPermutation(l, o), p = r;
   c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = N.getInnerMostAxes(l.length, o)), N.assertAxesAreInnerMostDims("all", l, o);
@@ -26785,7 +26786,7 @@ function f8(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var m8 = { kernelName: al, backendName: "webgl", kernelFunc: f8 };
+var m8 = { kernelName: rl, backendName: "webgl", kernelFunc: f8 };
 function g8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = N.getAxesPermutation(l, o), p = r;
   c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = N.getInnerMostAxes(l.length, o)), N.assertAxesAreInnerMostDims("any", l, o);
@@ -26797,7 +26798,7 @@ function g8(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var b8 = { kernelName: il, backendName: "webgl", kernelFunc: g8 };
+var b8 = { kernelName: al, backendName: "webgl", kernelFunc: g8 };
 var y8 = class {
   constructor(e, t, n) {
     this.variableNames = ["A"];
@@ -26951,7 +26952,7 @@ function k8(e) {
   let c = B1(n, u, i[0], "min");
   return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
 }
-var I8 = { kernelName: ol, backendName: "webgl", kernelFunc: k8 };
+var I8 = { kernelName: il, backendName: "webgl", kernelFunc: k8 };
 var S8 = ts + `
   if (abs(x) > 1.) {
     return NAN;
@@ -26959,15 +26960,15 @@ var S8 = ts + `
   return asin(x);
 `;
 var C8 = Ke({ opSnippet: S8 });
-var N8 = { kernelName: ul, backendName: "webgl", kernelFunc: C8 };
+var N8 = { kernelName: ol, backendName: "webgl", kernelFunc: C8 };
 var T8 = ts + "return log(x + sqrt(x * x + 1.0));";
 var $8 = Ke({ opSnippet: T8 });
-var _8 = { kernelName: ll, backendName: "webgl", kernelFunc: $8 };
+var _8 = { kernelName: ul, backendName: "webgl", kernelFunc: $8 };
 var A8 = ts + `
   return atan(x);
 `;
 var E8 = Ke({ opSnippet: A8 });
-var R8 = { kernelName: cl, backendName: "webgl", kernelFunc: E8 };
+var R8 = { kernelName: ll, backendName: "webgl", kernelFunc: E8 };
 var D8 = BX + `
   return atan(a, b);
 `;
@@ -26978,13 +26979,13 @@ var F8 = `
   return result;
 `;
 var O8 = qt({ opSnippet: D8, packedOpSnippet: F8 });
-var P8 = { kernelName: pl, backendName: "webgl", kernelFunc: O8 };
+var P8 = { kernelName: dl, backendName: "webgl", kernelFunc: O8 };
 var z8 = ts + `
   if ((x < -1.0) || (x > 1.0)) return NAN;
 return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
 var M8 = Ke({ opSnippet: z8 });
-var L8 = { kernelName: dl, backendName: "webgl", kernelFunc: M8 };
-var tl = class {
+var L8 = { kernelName: cl, backendName: "webgl", kernelFunc: M8 };
+var el = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
@@ -27143,7 +27144,7 @@ var tl = class {
     `;
   }
 };
-var wv = class {
+var xv = class {
   constructor(e, t, n, s = false, r = false) {
     if (this.variableNames = ["x"], t === "avg" && n)
       throw new Error("Cannot compute positions for average pool.");
@@ -27326,21 +27327,21 @@ var wv = class {
 };
 function B8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  nu(r, "avgPool");
+  tu(r, "avgPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
   w.assert(N.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
   let c = N.computePool2DInfo(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return En({ inputs: { x: r }, backend: n });
-  let p = new tl(c, "avg", false);
+  let p = new el(c, "avg", false);
   return n.runWebGLProgram(p, [r], "float32");
 }
 var V8 = { kernelName: ka, backendName: "webgl", kernelFunc: B8 };
 function W8(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = N.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new wv(p, "avg", false);
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s, c = [1, 1, 1], p = N.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new xv(p, "avg", false);
   return n.runWebGLProgram(d, [r], "float32");
 }
-var U8 = { kernelName: qd, backendName: "webgl", kernelFunc: W8 };
+var U8 = { kernelName: Hd, backendName: "webgl", kernelFunc: W8 };
 var G8 = class {
   constructor(e) {
     this.variableNames = ["dy"], this.outputShape = e.inShape;
@@ -27457,17 +27458,17 @@ function q8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = N.computePool3DInfo(i.shape, o, u, p, l, c), h = new H8(d);
   return n.runWebGLProgram(h, [r], i.dtype);
 }
-var j8 = { kernelName: ig, backendName: "webgl", kernelFunc: q8 };
+var j8 = { kernelName: ag, backendName: "webgl", kernelFunc: q8 };
 function K8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
-  nu([r, a], "avgPoolGrad");
+  tu([r, a], "avgPoolGrad");
   let { filterSize: o, strides: u, pad: l } = s, c = N.computePool2DInfo(i.shape, o, u, 1, l), p = new G8(c);
   return n.runWebGLProgram(p, [r], i.dtype);
 }
-var X8 = { kernelName: ag, backendName: "webgl", kernelFunc: K8 };
+var X8 = { kernelName: rg, backendName: "webgl", kernelFunc: K8 };
 function Y8(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
-  return zd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
+  return Pd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
 }
 var Q8 = { kernelName: Ia, backendName: "webgl", kernelFunc: Y8 };
 var Z8 = class {
@@ -27529,7 +27530,7 @@ var nY = class {
     this.variableNames = ["source"], this.outputShape = e, this.rank = e.length;
     let t = ot(this.rank);
     this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
-    let n = sY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Um[i]} = start[${i}] + coords.${Um[i]};`);
+    let n = sY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Wm[i]} = start[${i}] + coords.${Wm[i]};`);
     s = `
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
@@ -27543,12 +27544,12 @@ var nY = class {
     `;
   }
 };
-var Um = ["x", "y", "z", "w", "u", "v"];
+var Wm = ["x", "y", "z", "w", "u", "v"];
 function sY(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
-    return Um.slice(0, e).map((t) => "sourceLoc." + t).join(",");
+    return Wm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
 var rY = class {
@@ -27595,7 +27596,7 @@ function aY(e, t, n, s) {
   let u = s.dataRefCount.get(i.slice.origDataId) || 1;
   return s.dataRefCount.set(i.slice.origDataId, u + 1), a;
 }
-function uu(e) {
+function ou(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
   if (wt.assertParamsValid(r, o, u), w.sizeFromShape(u) === 0)
     return n.makeTensorInfo(u, r.dtype, []);
@@ -27610,47 +27611,47 @@ function uu(e) {
   }
   return n.uploadToGPU(r.dataId), aY(r, o, u, n);
 }
-var iY = { kernelName: Po, backendName: "webgl", kernelFunc: uu };
+var iY = { kernelName: Oo, backendName: "webgl", kernelFunc: ou };
 var oY = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = cn({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = uu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((y, v) => y * v), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = cn({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = ou({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 };
-var uY = { kernelName: oo, backendName: "webgl", kernelFunc: oY };
+var uY = { kernelName: io, backendName: "webgl", kernelFunc: oY };
 function lY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i } = s, o = n.readSync(r.dataId), u = n.readSync(a.dataId), l = S1(o, u, a.dtype, a.shape, i);
   return n.makeTensorInfo([i], a.dtype, l);
 }
-var cY = { kernelName: og, backendName: "webgl", kernelFunc: lY };
+var cY = { kernelName: ig, backendName: "webgl", kernelFunc: lY };
 function dY(e) {
   let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.readSync(s.dataId), i = n.readSync(r.dataId), o = N.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
   return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
 }
-var pY = { kernelName: ug, backendName: "webgl", kernelFunc: dY };
+var pY = { kernelName: og, backendName: "webgl", kernelFunc: dY };
 var hY = "return float(a != b);";
 var V1 = qt({ opSnippet: hY, cpuKernelImpl: GK, dtype: "bool" });
-var fY = { kernelName: So, backendName: "webgl", kernelFunc: V1 };
-function Jl(e) {
+var fY = { kernelName: Io, backendName: "webgl", kernelFunc: V1 };
+function Zl(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return En({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var mY = { kernelName: np, backendName: "webgl", kernelFunc: Jl };
+var mY = { kernelName: tp, backendName: "webgl", kernelFunc: Zl };
 var gY = "return float(int(x));";
 function bY(e, t) {
   let n = new Hs(e.shape, gY), s = t.runWebGLProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-function Gm(e) {
+function Um(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return En({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = Gm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Er({ inputs: { real: o, imag: i }, backend: n });
+    let i = $t(r.shape), o = Um({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Er({ inputs: { real: o, imag: i }, backend: n });
     return i.dispose(), n.disposeIntermediateTensorInfo(o), u;
   }
   if (r.dtype === "complex64") {
-    let i = Jl({ inputs: { input: r }, backend: n }), o = Gm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
+    let i = Zl({ inputs: { input: r }, backend: n }), o = Um({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
     return n.disposeIntermediateTensorInfo(i), o;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
@@ -27665,9 +27666,9 @@ function Gm(e) {
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var yY = { kernelName: Sa, backendName: "webgl", kernelFunc: Gm };
-var hw = "return ceil(x);";
-var vY = Ke({ opSnippet: hw, packedOpSnippet: hw, cpuKernelImpl: CK });
+var yY = { kernelName: Sa, backendName: "webgl", kernelFunc: Um };
+var pw = "return ceil(x);";
+var vY = Ke({ opSnippet: pw, packedOpSnippet: pw, cpuKernelImpl: CK });
 var xY = { kernelName: Ca, backendName: "webgl", kernelFunc: vY };
 var wY = class {
   constructor(e) {
@@ -27726,14 +27727,14 @@ var CY = class {
     `;
   }
 };
-function fw(e, t) {
+function hw(e, t) {
   return { dataId: t.dataId, dtype: t.dtype, shape: e.shape };
 }
 function NY(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new CY(s.shape), i = [fw(s, r.complexTensorInfos.real), fw(s, r.complexTensorInfos.imag)];
+  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new CY(s.shape), i = [hw(s, r.complexTensorInfos.real), hw(s, r.complexTensorInfos.imag)];
   return n.runWebGLProgram(a, i, i[0].dtype);
 }
-var TY = { kernelName: Kd, backendName: "webgl", kernelFunc: NY };
+var TY = { kernelName: jd, backendName: "webgl", kernelFunc: NY };
 var $Y = class {
   constructor(e) {
     this.outputShape = [], this.outputShape = N.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${i}`);
@@ -27777,15 +27778,15 @@ var _Y = class {
       p += `
         if (${u} < ${o[f]}  && ${u} >= ${o[f - 1]}) {
           return getChannel(
-            getT${f}(${Jc(i, u, m)}),
-            vec2(${Jc(l, u, m)}));
+            getT${f}(${Zc(i, u, m)}),
+            vec2(${Zc(l, u, m)}));
         }`;
     }
     let d = o.length, h = o[o.length - 1];
     p += `
         return getChannel(
-          getT${d}(${Jc(i, u, h)}),
-          vec2(${Jc(l, u, h)}));`, this.userCode = `
+          getT${d}(${Zc(i, u, h)}),
+          vec2(${Zc(l, u, h)}));`, this.userCode = `
       float getValue(${i.map((f) => "int " + f)}) {
         ${p}
       }
@@ -27814,19 +27815,19 @@ var _Y = class {
     `;
   }
 };
-function Jc(e, t, n) {
+function Zc(e, t, n) {
   let s = e.indexOf(t);
   return e.map((a, i) => i === s ? `${a} - ${n}` : a).join();
 }
-function Qp(e) {
+function Yp(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
   return En({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var AY = { kernelName: Zd, backendName: "webgl", kernelFunc: Qp };
-function Ui(e, t, n) {
+var AY = { kernelName: Qd, backendName: "webgl", kernelFunc: Yp };
+function Wi(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let c = e.map((m) => Jl({ inputs: { input: m }, backend: n })), p = e.map((m) => Qp({ inputs: { input: m }, backend: n })), d = Ui(c, t, n), h = Ui(p, t, n), f = Er({ inputs: { real: d, imag: h }, backend: n });
+    let c = e.map((m) => Zl({ inputs: { input: m }, backend: n })), p = e.map((m) => Yp({ inputs: { input: m }, backend: n })), d = Wi(c, t, n), h = Wi(p, t, n), f = Er({ inputs: { real: d, imag: h }, backend: n });
     return c.forEach((m) => n.disposeIntermediateTensorInfo(m)), p.forEach((m) => n.disposeIntermediateTensorInfo(m)), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
   }
   let r = n.shouldExecuteOnCPU(e);
@@ -27838,7 +27839,7 @@ function Ui(e, t, n) {
     return c.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
   }
   if (e.length > X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
-    let c = Math.floor(e.length / 2), p = Ui(e.slice(0, c), t, n), d = Ui(e.slice(c), t, n), h = Ui([p, d], t, n);
+    let c = Math.floor(e.length / 2), p = Wi(e.slice(0, c), t, n), d = Wi(e.slice(c), t, n), h = Wi([p, d], t, n);
     return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), h;
   }
   if (X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && e[0].shape.length > 1) {
@@ -27862,9 +27863,9 @@ function W1(e) {
   if (o.length === 1)
     return En({ inputs: { x: o[0] }, backend: n });
   let u = o.map((l) => l.shape);
-  return N.assertParamsConsistent(u, a), Ui(o, a, n);
+  return N.assertParamsConsistent(u, a), Wi(o, a, n);
 }
-var RY = { kernelName: uo, backendName: "webgl", kernelFunc: W1 };
+var RY = { kernelName: oo, backendName: "webgl", kernelFunc: W1 };
 var U1 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
@@ -28164,13 +28165,13 @@ function G1({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAct
   let u = e.shape, l = s.texData.get(e.dataId), c = n.inChannels, p = u[0] * u[1] * u[2], d = n.outChannels, h = n.dataFormat === "channelsLast", f = false, m = false, g, b = [];
   if (!((p === 1 || d === 1) && c > z1) && l.isPacked && h && l.texture != null && u[2] % 2 !== 0 && w.arraysEqual(l.shape.slice(-3), u.slice(-3))) {
     let x = u[0] * u[1] * (u[2] + 1), k = { dataId: e.dataId, shape: [1, x, n.inChannels], dtype: e.dtype }, C = l.shape;
-    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(el(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
+    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(Ju(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
     let T = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
     b.push(T);
-    let E = zd({ a: k, b: T, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
+    let E = Pd({ a: k, b: T, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
     w.assert(A.isPacked, () => "batchMatMul result is expected to be packed"), l.shape = C, A.shape = n.outShape, g = En({ inputs: { x: E }, backend: s }), g.shape = n.outShape, b.push(E);
   } else {
-    let x = h ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3], k = he({ inputs: { x: e }, backend: s, attrs: { shape: [1, x, n.inChannels] } }), C = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), T = zd({ a: k, b: C, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
+    let x = h ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3], k = he({ inputs: { x: e }, backend: s, attrs: { shape: [1, x, n.inChannels] } }), C = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), T = Pd({ a: k, b: C, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
     g = he({ inputs: { x: T }, backend: s, attrs: { shape: n.outShape } }), b.push(k), b.push(C), b.push(T);
   }
   for (let x of b)
@@ -28182,7 +28183,7 @@ function H1({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAct
   x.push(k), x.push(C);
   let T = new FY(b, n), E = [k.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], A = s.runWebGLProgram(T, [k], "float32", E), P = he({ inputs: { x: A }, backend: s, attrs: { shape: [1, b[0], b[1]] } });
   x.push(A), x.push(P);
-  let R = r != null, F = a != null, $ = o === "leakyrelu", z = o ? Kp(o, true) : null, W = new P1(P.shape, C.shape, [1, g, n.outChannels], y, v, R, z, F, $), q = [P, C];
+  let R = r != null, F = a != null, $ = o === "leakyrelu", z = o ? jp(o, true) : null, W = new P1(P.shape, C.shape, [1, g, n.outChannels], y, v, R, z, F, $), q = [P, C];
   if (r && q.push(r), F && q.push(a), $) {
     let te = s.makeTensorInfo([], "float32", w.createScalarValue(i, "float32"));
     q.push(te), x.push(te);
@@ -28433,7 +28434,7 @@ function VY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, dataFormat: u, dimRoundingMode: l, filterShape: c } = s, p = N.convertConv2DDataFormat(u), d = N.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), h = new zY(d);
   return n.runWebGLProgram(h, [r, a], "float32");
 }
-var WY = { kernelName: lg, backendName: "webgl", kernelFunc: VY };
+var WY = { kernelName: ug, backendName: "webgl", kernelFunc: VY };
 function UY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { inputShape: i, strides: o, pad: u, dataFormat: l, dimRoundingMode: c } = s, p = N.convertConv2DDataFormat(l), d = N.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = new MY(d);
   return n.runWebGLProgram(h, [r, a], "float32");
@@ -28443,29 +28444,29 @@ function HY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = N.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new DY(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var qY = { kernelName: Xd, backendName: "webgl", kernelFunc: HY };
+var qY = { kernelName: Kd, backendName: "webgl", kernelFunc: HY };
 function jY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = N.computeConv3DInfo(r.shape, u, i, 1, o), c = new LY(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var KY = { kernelName: cg, backendName: "webgl", kernelFunc: jY };
+var KY = { kernelName: lg, backendName: "webgl", kernelFunc: jY };
 function XY(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = N.computeConv3DInfo(u, a.shape, o, 1, i), c = new BY(l);
   return n.runWebGLProgram(c, [r, a], "float32");
 }
-var YY = { kernelName: dg, backendName: "webgl", kernelFunc: XY };
-var QY = ou + `
+var YY = { kernelName: cg, backendName: "webgl", kernelFunc: XY };
+var QY = iu + `
   return cos(x);
 `;
 var ZY = Ke({ opSnippet: QY });
 var JY = { kernelName: $a, backendName: "webgl", kernelFunc: ZY };
-var e7 = `
+var e9 = `
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
 `;
-var t7 = Ke({ opSnippet: e7 });
-var n7 = { kernelName: _a, backendName: "webgl", kernelFunc: t7 };
-var s7 = class {
+var t9 = Ke({ opSnippet: e9 });
+var n9 = { kernelName: _a, backendName: "webgl", kernelFunc: t9 };
+var s9 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
     let [a, i, o, u] = e, [l] = t, [c, p] = n;
@@ -28535,32 +28536,32 @@ var s7 = class {
     `;
   }
 };
-var r7 = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new s7(r.shape, a.shape, o, u, l);
+var r9 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new s9(r.shape, a.shape, o, u, l);
   return n.runWebGLProgram(c, [r, a, i], "float32");
 };
-var a7 = { kernelName: co, backendName: "webgl", kernelFunc: r7 };
-var mw = class {
+var a9 = { kernelName: lo, backendName: "webgl", kernelFunc: r9 };
+var fw = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.outputShape = e;
-    let s = e.length, r = t ? "0.0" : `getX(${gw(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
+    let s = e.length, r = t ? "0.0" : `getX(${mw(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
     t ? (i = n ? `end != ${a - 1}` : "end != 0", o = n ? "end + 1" : "end - 1") : (i = n ? `end + pow2 < ${a}` : "end >= pow2", o = n ? "end + pow2" : "end - pow2"), this.userCode = `
       void main() {
         ${ot(s)} coords = getOutputCoords();
-        int end = ${bw(s, "coords")};
+        int end = ${gw(s, "coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${bw(s, "coords")} = idx;
-          val += getX(${gw(s, "coords")});
+          ${gw(s, "coords")} = idx;
+          val += getX(${mw(s, "coords")});
         }
         setOutput(val);
       }
     `;
   }
 };
-function gw(e, t) {
+function mw(e, t) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28571,7 +28572,7 @@ function gw(e, t) {
     return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
   throw Error(`Cumulative sum for rank ${e} is not yet supported`);
 }
-function bw(e, t) {
+function gw(e, t) {
   if (e === 1)
     return `${t}`;
   if (e === 2)
@@ -28582,7 +28583,7 @@ function bw(e, t) {
     return `${t}.w`;
   throw Error(`Cumulative sum for rank ${e} is not yet supported`);
 }
-function i7(e) {
+function i9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length, l = N.getAxesPermutation([a], u), c = r;
   l != null && (c = cn({ inputs: { x: r }, backend: n, attrs: { perm: l } }));
   let p = N.getInnerMostAxes(1, u)[0];
@@ -28590,11 +28591,11 @@ function i7(e) {
     throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length - 1} but got axis=${a}`);
   let d = c.shape[p], h = En({ inputs: { x: c }, backend: n });
   for (let f = 0; f <= Math.ceil(Math.log2(d)) - 1; f++) {
-    let m = new mw(c.shape, false, o), g = [[f]], b = h;
+    let m = new fw(c.shape, false, o), g = [[f]], b = h;
     h = n.runWebGLProgram(m, [h], h.dtype, g), n.disposeIntermediateTensorInfo(b);
   }
   if (i) {
-    let f = new mw(c.shape, i, o), m = h;
+    let f = new fw(c.shape, i, o), m = h;
     h = n.runWebGLProgram(f, [h], h.dtype), n.disposeIntermediateTensorInfo(m);
   }
   if (l != null) {
@@ -28603,8 +28604,8 @@ function i7(e) {
   }
   return h;
 }
-var o7 = { kernelName: lo, backendName: "webgl", kernelFunc: i7 };
-function u7(e) {
+var o9 = { kernelName: uo, backendName: "webgl", kernelFunc: i9 };
+function u9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
   if (r.shape.length === 1) {
     let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = S1(u, l, a.dtype, a.shape, i);
@@ -28615,8 +28616,8 @@ function u7(e) {
   }
   throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
 }
-var l7 = { kernelName: pg, backendName: "webgl", kernelFunc: u7 };
-var c7 = class {
+var l9 = { kernelName: dg, backendName: "webgl", kernelFunc: u9 };
+var c9 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = [], this.outputShape = e, this.blockSize = t, this.dataFormat = n, this.userCode = `
     void main() {
@@ -28655,11 +28656,11 @@ var c7 = class {
     return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
   }
 };
-function d7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new c7(f, a, i);
+function d9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = new c9(f, a, i);
   return n.runWebGLProgram(m, [r], r.dtype);
 }
-var p7 = { kernelName: po, backendName: "webgl", kernelFunc: d7 };
+var p9 = { kernelName: co, backendName: "webgl", kernelFunc: d9 };
 var q1 = class {
   constructor(e, t = false, n = null, s = false, r = false) {
     this.variableNames = ["x", "W"], this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = kn(this.outputShape.length);
@@ -28937,7 +28938,7 @@ var j1 = class {
     `;
   }
 };
-function h7(e) {
+function h9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
   c == null && (c = [1, 1]), w.assert(N.eitherStridesOrDilationsAreOne(i, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);
   let p = N.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d;
@@ -28945,8 +28946,8 @@ function h7(e) {
   let h = [[p.padInfo.top, p.padInfo.left], [p.strideHeight, p.strideWidth], [p.dilationHeight, p.dilationWidth], [p.inHeight, p.inWidth]];
   return n.runWebGLProgram(d, [r, a], "float32", h);
 }
-var f7 = { kernelName: Aa, backendName: "webgl", kernelFunc: h7 };
-var m7 = class {
+var f9 = { kernelName: Aa, backendName: "webgl", kernelFunc: h9 };
+var m9 = class {
   constructor(e) {
     this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.outChannels / e.inChannels;
@@ -28988,7 +28989,7 @@ var m7 = class {
     `;
   }
 };
-var g7 = class {
+var g9 = class {
   constructor(e) {
     this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
     let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = t - 1 - e.padInfo.top, i = n - 1 - e.padInfo.left, o = e.outChannels / e.inChannels;
@@ -29040,17 +29041,17 @@ var g7 = class {
     `;
   }
 };
-function b7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s, p = N.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new m7(p);
+function b9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, filterShape: c } = s, p = N.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new m9(p);
   return n.runWebGLProgram(d, [r, a], "float32");
 }
-var y7 = { kernelName: hg, backendName: "webgl", kernelFunc: b7 };
-function v7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s, p = N.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new g7(p);
+var y9 = { kernelName: pg, backendName: "webgl", kernelFunc: b9 };
+function v9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { strides: i, dilations: o, pad: u, dimRoundingMode: l, inputShape: c } = s, p = N.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new g9(p);
   return n.runWebGLProgram(d, [r, a], "float32");
 }
-var x7 = { kernelName: fg, backendName: "webgl", kernelFunc: v7 };
-var w7 = class {
+var x9 = { kernelName: hg, backendName: "webgl", kernelFunc: v9 };
+var w9 = class {
   constructor(e) {
     this.variableNames = ["X"], this.outputShape = [e, e], this.userCode = `
       void main() {
@@ -29061,12 +29062,12 @@ var w7 = class {
     `;
   }
 };
-function k7(e) {
-  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), i = he({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), o = new w7(a), u = n.runWebGLProgram(o, [i], i.dtype), l = he({ inputs: { x: u }, backend: n, attrs: { shape: r } });
+function k9(e) {
+  let { inputs: t, backend: n } = e, { x: s } = t, r = [...s.shape, ...s.shape], a = w.sizeFromShape(s.shape), i = he({ inputs: { x: s }, backend: n, attrs: { shape: [a] } }), o = new w9(a), u = n.runWebGLProgram(o, [i], i.dtype), l = he({ inputs: { x: u }, backend: n, attrs: { shape: r } });
   return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(u), l;
 }
-var I7 = { kernelName: mg, backendName: "webgl", kernelFunc: k7 };
-var S7 = class {
+var I9 = { kernelName: fg, backendName: "webgl", kernelFunc: k9 };
+var S9 = class {
   constructor(e) {
     this.variableNames = ["x", "W"], this.outputShape = e.outShape;
     let { inHeight: t, inWidth: n, padInfo: s, strideHeight: r, strideWidth: a, filterHeight: i, filterWidth: o, dilationHeight: u, dilationWidth: l } = e, { top: c, left: p } = s;
@@ -29111,14 +29112,14 @@ var S7 = class {
     `;
   }
 };
-function C7(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = N.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new S7(l);
+function C9(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = N.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new S9(l);
   c = n.runWebGLProgram(p, [r, a], "float32");
   let d = he({ inputs: { x: c }, backend: n, attrs: { shape: l.outShape } });
   return n.disposeIntermediateTensorInfo(c), d;
 }
-var N7 = { kernelName: Yd, backendName: "webgl", kernelFunc: C7 };
-function T7(e) {
+var N9 = { kernelName: Xd, backendName: "webgl", kernelFunc: C9 };
+function T9(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = N.decodeEinsumEquation(r, a.length);
   N.checkEinsumDimSizes(i.length, u, a);
   let { path: l, steps: c } = N.getEinsumComputePath(o, u), p = c.length, d = null, h = i.length, f = [];
@@ -29129,17 +29130,17 @@ function T7(e) {
       let x = v.shape.slice();
       for (let k = 0; k < y.length; ++k)
         x.splice(y[k], 0, 1);
-      w.arraysEqual(v.shape, x) || (v = he({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = xv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
+      w.arraysEqual(v.shape, x) || (v = he({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = vv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = Yp({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Xp({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeIntermediateTensorInfo(m);
   return d;
 }
-var $7 = { kernelName: Qd, backendName: "webgl", kernelFunc: T7 };
-var _7 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
-var A7 = `
+var $9 = { kernelName: Yd, backendName: "webgl", kernelFunc: T9 };
+var _9 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+var A9 = `
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -29149,25 +29150,25 @@ var A7 = `
 
   return result;
 `;
-var E7 = Ke({ opSnippet: _7, packedOpSnippet: A7 });
-var R7 = { kernelName: Ra, backendName: "webgl", kernelFunc: E7 };
-var D7 = "return (b >= 1.0) ? a : a * (b + 1.0);";
-var F7 = `
+var E9 = Ke({ opSnippet: _9, packedOpSnippet: A9 });
+var R9 = { kernelName: Ra, backendName: "webgl", kernelFunc: E9 };
+var D9 = "return (b >= 1.0) ? a : a * (b + 1.0);";
+var F9 = `
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
 `;
-var O7 = (e) => {
-  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Zl(F7, s.shape, r.shape) : new so(D7, s.shape, r.shape);
+var O9 = (e) => {
+  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ql(F9, s.shape, r.shape) : new no(D9, s.shape, r.shape);
   return n.runWebGLProgram(a, [s, r], s.dtype);
 };
-var P7 = { kernelName: gg, backendName: "webgl", kernelFunc: O7 };
-var z7 = `
+var P9 = { kernelName: mg, backendName: "webgl", kernelFunc: O9 };
+var z9 = `
   return vec4(equal(a, b));
 `;
-var M7 = "return float(a == b);";
-var L7 = qt({ opSnippet: M7, packedOpSnippet: z7, dtype: "bool", cpuKernelImpl: TK });
-var B7 = { kernelName: ho, backendName: "webgl", kernelFunc: L7 };
-var V7 = `
+var M9 = "return float(a == b);";
+var L9 = qt({ opSnippet: M9, packedOpSnippet: z9, dtype: "bool", cpuKernelImpl: TK });
+var B9 = { kernelName: po, backendName: "webgl", kernelFunc: L9 };
+var V9 = `
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -29183,12 +29184,12 @@ var V7 = `
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
 `;
-var W7 = Ke({ opSnippet: V7 });
-var U7 = { kernelName: hl, backendName: "webgl", kernelFunc: W7 };
-var G7 = ou + `
+var W9 = Ke({ opSnippet: V9 });
+var U9 = { kernelName: pl, backendName: "webgl", kernelFunc: W9 };
+var G9 = iu + `
   return exp(x);
 `;
-var H7 = `
+var H9 = `
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -29198,17 +29199,17 @@ var H7 = `
 
   return result;
 `;
-var K1 = Ke({ opSnippet: G7, packedOpSnippet: H7, cpuKernelImpl: $K, dtype: "float32" });
-var q7 = { kernelName: Da, backendName: "webgl", kernelFunc: K1 };
-function Hm(e) {
+var K1 = Ke({ opSnippet: G9, packedOpSnippet: H9, cpuKernelImpl: $K, dtype: "float32" });
+var q9 = { kernelName: Da, backendName: "webgl", kernelFunc: K1 };
+function Gm(e) {
   let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
   return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), he({ inputs: { x: a }, backend: s, attrs: { shape: o } });
 }
-var j7 = { kernelName: fo, backendName: "webgl", kernelFunc: Hm };
-var yw = "return exp(x) - 1.0;";
-var K7 = Ke({ opSnippet: yw, packedOpSnippet: yw, cpuKernelImpl: _K });
-var X7 = { kernelName: mo, backendName: "webgl", kernelFunc: K7 };
-var vw = class {
+var j9 = { kernelName: ho, backendName: "webgl", kernelFunc: Gm };
+var bw = "return exp(x) - 1.0;";
+var K9 = Ke({ opSnippet: bw, packedOpSnippet: bw, cpuKernelImpl: _K });
+var X9 = { kernelName: fo, backendName: "webgl", kernelFunc: K9 };
+var yw = class {
   constructor(e, t, n) {
     this.variableNames = ["real", "imag"];
     let s = t[1];
@@ -29257,17 +29258,17 @@ var vw = class {
   }
 };
 function X1(e, t, n) {
-  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], i = r / a, o = he({ inputs: { x: e }, backend: n, attrs: { shape: [i, a] } }), u = o.shape, l = new vw("real", u, t), c = new vw("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Er({ inputs: { real: d, imag: h }, backend: n });
+  let s = n.texData.get(e.dataId), r = w.sizeFromShape(e.shape), a = e.shape[e.shape.length - 1], i = r / a, o = he({ inputs: { x: e }, backend: n, attrs: { shape: [i, a] } }), u = o.shape, l = new yw("real", u, t), c = new yw("imag", u, t), p = [{ dataId: s.complexTensorInfos.real.dataId, dtype: s.complexTensorInfos.real.dtype, shape: u }, { dataId: s.complexTensorInfos.imag.dataId, dtype: s.complexTensorInfos.imag.dtype, shape: u }], d = n.runWebGLProgram(l, p, "float32"), h = n.runWebGLProgram(c, p, "float32"), f = Er({ inputs: { real: d, imag: h }, backend: n });
   n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h);
   let m = he({ inputs: { x: f }, backend: n, attrs: { shape: e.shape } });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(f), m;
 }
-function Y7(e) {
+function Y9(e) {
   let { inputs: t, backend: n } = e, { input: s } = t;
   return X1(s, false, n);
 }
-var Q7 = { kernelName: bg, backendName: "webgl", kernelFunc: Y7 };
-var Z7 = class {
+var Q9 = { kernelName: gg, backendName: "webgl", kernelFunc: Y9 };
+var Z9 = class {
   constructor(e, t) {
     this.outputShape = [], this.customUniforms = [{ name: "value", type: "float" }], this.variableNames = ["x"], this.outputShape = e, this.userCode = `
       void main() {
@@ -29277,18 +29278,18 @@ var Z7 = class {
     `;
   }
 };
-function ec(e) {
+function Jl(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
   if (a = a || w.inferDtype(r), a === "string") {
     let i = w.getArrayFromDType(a, w.sizeFromShape(s));
     return i.fill(r), t.makeTensorInfo(s, a, i);
   } else {
-    let i = new Z7(s, r), o = [[r]];
+    let i = new Z9(s, r), o = [[r]];
     return t.runWebGLProgram(i, [], a, o);
   }
 }
-var J7 = { kernelName: fl, backendName: "webgl", kernelFunc: ec };
-var e9 = class {
+var J9 = { kernelName: hl, backendName: "webgl", kernelFunc: Jl };
+var eQ = class {
   constructor(e) {
     this.variableNames = ["Image"], this.outputShape = [];
     let t = e[2];
@@ -29309,14 +29310,14 @@ var e9 = class {
     `;
   }
 };
-var t9 = { kernelName: go, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { image: n } = e, s = t, r = new e9(n.shape);
+var tQ = { kernelName: mo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { image: n } = e, s = t, r = new eQ(n.shape);
   return s.runWebGLProgram(r, [n], n.dtype);
 } };
-var xw = "return floor(x);";
-var n9 = Ke({ opSnippet: xw, packedOpSnippet: xw, cpuKernelImpl: AK });
-var s9 = { kernelName: Fa, backendName: "webgl", kernelFunc: n9 };
-var r9 = `
+var vw = "return floor(x);";
+var nQ = Ke({ opSnippet: vw, packedOpSnippet: vw, cpuKernelImpl: AK });
+var sQ = { kernelName: Fa, backendName: "webgl", kernelFunc: nQ };
+var rQ = `
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -29327,7 +29328,7 @@ var r9 = `
     return NAN;
   }
 `;
-var a9 = `
+var aQ = `
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -29349,9 +29350,9 @@ var a9 = `
   }
   return vec4(result);
 `;
-var i9 = qt({ opSnippet: r9, packedOpSnippet: a9, dtype: "int32" });
-var o9 = { kernelName: Oa, backendName: "webgl", kernelFunc: i9 };
-var u9 = class {
+var iQ = qt({ opSnippet: rQ, packedOpSnippet: aQ, dtype: "int32" });
+var oQ = { kernelName: Oa, backendName: "webgl", kernelFunc: iQ };
+var uQ = class {
   constructor(e) {
     this.variableNames = ["A"];
     let t = hn(), [n, s] = e;
@@ -29380,7 +29381,7 @@ var u9 = class {
     `;
   }
 };
-var l9 = class {
+var lQ = class {
   constructor(e) {
     this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
     let t = hn(), [n, s] = e;
@@ -29421,24 +29422,24 @@ var l9 = class {
     `;
   }
 };
-var c9 = { kernelName: fd, backendName: "webgl", kernelFunc: d9 };
+var cQ = { kernelName: hd, backendName: "webgl", kernelFunc: dQ };
 var zi;
-function d9(e) {
+function dQ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s, i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, [u, l] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], c = [l, u], p = [l, u, a];
   (o || i) && (zi == null && (zi = document.createElement("canvas").getContext("2d")), zi.canvas.width = u, zi.canvas.height = l, zi.drawImage(r, 0, 0, u, l), r = zi.canvas);
   let d = n.makeTensorInfo(c, "int32");
   n.texData.get(d.dataId).usage = 2, n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId), r);
-  let h = X().getBool("WEBGL_PACK") ? new l9(p) : new u9(p), f = n.runWebGLProgram(h, [d], "int32");
+  let h = X().getBool("WEBGL_PACK") ? new lQ(p) : new uQ(p), f = n.runWebGLProgram(h, [d], "int32");
   return n.disposeData(d.dataId), f;
 }
-function p9(e) {
+function pQ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = s, m = N.convertConv2DDataFormat(c), g = N.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m), b, y = [];
   if (g.filterHeight === 1 && g.filterWidth === 1 && g.dilationHeight === 1 && g.dilationWidth === 1 && g.strideHeight === 1 && g.strideWidth === 1 && (g.padInfo.type === "SAME" || g.padInfo.type === "VALID"))
     b = G1({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
   else if (X().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
     b = H1({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
   else {
-    let x = i != null, k = o != null, C = h === "leakyrelu", T = h ? Kp(h, false) : null, E = new U1(g, x, T, k, C), A = [r, a];
+    let x = i != null, k = o != null, C = h === "leakyrelu", T = h ? jp(h, false) : null, E = new U1(g, x, T, k, C), A = [r, a];
     if (i && A.push(i), o && A.push(o), C) {
       let P = n.makeTensorInfo([], "float32", w.createScalarValue(f, "float32"));
       A.push(P), y.push(P);
@@ -29448,11 +29449,11 @@ function p9(e) {
   let v = he({ inputs: { x: b }, backend: n, attrs: { shape: g.outShape } });
   return y.push(b), y.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
 }
-var h9 = { kernelName: sa, backendName: "webgl", kernelFunc: p9 };
-function f9(e) {
+var hQ = { kernelName: sa, backendName: "webgl", kernelFunc: pQ };
+function fQ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dimRoundingMode: p, activation: d, leakyreluAlpha: h } = s, f = [], m = c;
   m == null && (m = [1, 1]), w.assert(N.eitherStridesOrDilationsAreOne(u, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);
-  let g = N.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = X().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? Kp(d, b) : null, v = [r, a], x = i != null, k = o != null, C = d === "leakyrelu";
+  let g = N.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = X().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? jp(d, b) : null, v = [r, a], x = i != null, k = o != null, C = d === "leakyrelu";
   if (x && v.push(i), k && v.push(o), C) {
     let P = n.makeTensorInfo([], "float32", w.createScalarValue(h, "float32"));
     v.push(P), f.push(P);
@@ -29462,8 +29463,8 @@ function f9(e) {
   let E = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], A = n.runWebGLProgram(T, v, "float32", E);
   return f.forEach((P) => n.disposeIntermediateTensorInfo(P)), A;
 }
-var m9 = { kernelName: ra, backendName: "webgl", kernelFunc: f9 };
-var g9 = class {
+var mQ = { kernelName: ra, backendName: "webgl", kernelFunc: fQ };
+var gQ = class {
   constructor(e, t, n) {
     this.sliceDim = e, this.strides = t, this.variableNames = ["x", "indices"], this.outputShape = n;
     let s = ot(t.length), r = ot(n.length), a = this.sliceDim > 1 ? "strides[j]" : "strides";
@@ -29481,20 +29482,20 @@ var g9 = class {
       `;
   }
 };
-function b9(e) {
+function bQ(e) {
   let { inputs: t, backend: n } = e, { params: s, indices: r } = t, a = r.shape, i = a[a.length - 1], o = w.sizeFromShape(s.shape), [u, l, c, p] = N.prepareAndValidate(s, r), d = he({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = he({ inputs: { x: s }, backend: n, attrs: { shape: [w.sizeFromShape(s.shape) / c, c] } });
   if (n.shouldExecuteOnCPU([s, r]) || s.dtype === "string") {
     let b = n.readSync(r.dataId), y = n.bufferSync(s), v = EK(b, y, s.dtype, l, i, c, p, s.shape, o);
     return n.makeTensorInfo(u, s.dtype, v.values);
   }
-  let f = new g9(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: u } });
+  let f = new gQ(i, p, [l, c]), m = n.runWebGLProgram(f, [h, d], h.dtype), g = he({ inputs: { x: m }, backend: n, attrs: { shape: u } });
   return n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(m), g;
 }
-var y9 = { kernelName: yo, backendName: "webgl", kernelFunc: b9 };
-var v9 = class {
+var yQ = { kernelName: bo, backendName: "webgl", kernelFunc: bQ };
+var vQ = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
-    let n = ot(this.rank), s = x9(e, 2);
+    let n = ot(this.rank), s = xQ(e, 2);
     this.userCode = `
       void main() {
         ${n} resRC = getOutputCoords();
@@ -29505,7 +29506,7 @@ var v9 = class {
     `;
   }
 };
-function x9(e, t) {
+function xQ(e, t) {
   let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [];
   for (let r = 0; r < e.length; r++)
     r === 2 ? s.push("index") : s.push(`${n[r]}`);
@@ -29527,59 +29528,59 @@ function Y1(e) {
     let y = n.bufferSync(h), v = n.bufferSync(d), x = RK(v, y, f);
     return p.forEach((k) => n.disposeIntermediateTensorInfo(k)), n.makeTensorInfo(l.outputShape, x.dtype, x.values);
   }
-  let m = new v9(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
+  let m = new vQ(d.shape, f), g = n.runWebGLProgram(m, [d, h], d.dtype);
   p.push(g);
   let b = he({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeIntermediateTensorInfo(y)), b;
 }
-var w9 = { kernelName: bo, backendName: "webgl", kernelFunc: Y1 };
-var k9 = "return float(a > b);";
-var I9 = `
+var wQ = { kernelName: go, backendName: "webgl", kernelFunc: Y1 };
+var kQ = "return float(a > b);";
+var IQ = `
   return vec4(greaterThan(a, b));
 `;
-var S9 = qt({ opSnippet: k9, packedOpSnippet: I9, cpuKernelImpl: DK, dtype: "bool" });
-var C9 = { kernelName: vo, backendName: "webgl", kernelFunc: S9 };
-var N9 = "return float(a >= b);";
-var T9 = `
+var SQ = qt({ opSnippet: kQ, packedOpSnippet: IQ, cpuKernelImpl: DK, dtype: "bool" });
+var CQ = { kernelName: yo, backendName: "webgl", kernelFunc: SQ };
+var NQ = "return float(a >= b);";
+var TQ = `
   return vec4(greaterThanEqual(a, b));
 `;
-var $9 = qt({ opSnippet: N9, packedOpSnippet: T9, dtype: "bool", cpuKernelImpl: FK });
-var _9 = { kernelName: za, backendName: "webgl", kernelFunc: $9 };
-function A9(e) {
+var $Q = qt({ opSnippet: NQ, packedOpSnippet: TQ, dtype: "bool", cpuKernelImpl: FK });
+var _Q = { kernelName: za, backendName: "webgl", kernelFunc: $Q };
+function AQ(e) {
   let { inputs: t, backend: n } = e, { input: s } = t;
   return X1(s, true, n);
 }
-var E9 = { kernelName: yg, backendName: "webgl", kernelFunc: A9 };
-var R9 = "return float(!isnan(x) && !isinf(x));";
-var D9 = Ke({ opSnippet: R9, dtype: "bool" });
-var F9 = { kernelName: ml, backendName: "webgl", kernelFunc: D9 };
-var O9 = "return float(isinf(x));";
-var P9 = Ke({ opSnippet: O9, dtype: "bool" });
-var z9 = { kernelName: gl, backendName: "webgl", kernelFunc: P9 };
-var M9 = "return float(isnan(x));";
-var L9 = Ke({ opSnippet: M9, dtype: "bool" });
-var B9 = { kernelName: bl, backendName: "webgl", kernelFunc: L9 };
-var V9 = "return float(a < b);";
-var W9 = `
+var EQ = { kernelName: bg, backendName: "webgl", kernelFunc: AQ };
+var RQ = "return float(!isnan(x) && !isinf(x));";
+var DQ = Ke({ opSnippet: RQ, dtype: "bool" });
+var FQ = { kernelName: fl, backendName: "webgl", kernelFunc: DQ };
+var OQ = "return float(isinf(x));";
+var PQ = Ke({ opSnippet: OQ, dtype: "bool" });
+var zQ = { kernelName: ml, backendName: "webgl", kernelFunc: PQ };
+var MQ = "return float(isnan(x));";
+var LQ = Ke({ opSnippet: MQ, dtype: "bool" });
+var BQ = { kernelName: gl, backendName: "webgl", kernelFunc: LQ };
+var VQ = "return float(a < b);";
+var WQ = `
   return vec4(lessThan(a, b));
 `;
-var U9 = qt({ opSnippet: V9, packedOpSnippet: W9, cpuKernelImpl: OK, dtype: "bool" });
-var G9 = { kernelName: xo, backendName: "webgl", kernelFunc: U9 };
-var H9 = "return float(a <= b);";
-var q9 = `
+var UQ = qt({ opSnippet: VQ, packedOpSnippet: WQ, cpuKernelImpl: OK, dtype: "bool" });
+var GQ = { kernelName: vo, backendName: "webgl", kernelFunc: UQ };
+var HQ = "return float(a <= b);";
+var qQ = `
   return vec4(lessThanEqual(a, b));
 `;
-var j9 = qt({ opSnippet: H9, packedOpSnippet: q9, cpuKernelImpl: PK, dtype: "bool" });
-var K9 = { kernelName: wo, backendName: "webgl", kernelFunc: j9 };
-function X9(e) {
+var jQ = qt({ opSnippet: HQ, packedOpSnippet: qQ, cpuKernelImpl: PK, dtype: "bool" });
+var KQ = { kernelName: xo, backendName: "webgl", kernelFunc: jQ };
+function XQ(e) {
   let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = zK(s, r, a);
   return t.makeTensorInfo([i.length], "float32", i);
 }
-var Y9 = { kernelName: vg, backendName: "webgl", kernelFunc: X9 };
-var Q9 = ou + `
+var YQ = { kernelName: yg, backendName: "webgl", kernelFunc: XQ };
+var QQ = iu + `
   return x < 0.0 ? 0./0. : log(x);
 `;
-var Z9 = `
+var ZQ = `
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -29588,34 +29589,34 @@ var Z9 = `
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
 `;
-var J9 = Ke({ opSnippet: Q9, packedOpSnippet: Z9, cpuKernelImpl: MK });
-var eQ = { kernelName: Ba, backendName: "webgl", kernelFunc: J9 };
-var tQ = ou + `
+var JQ = Ke({ opSnippet: QQ, packedOpSnippet: ZQ, cpuKernelImpl: MK });
+var eZ = { kernelName: Ba, backendName: "webgl", kernelFunc: JQ };
+var tZ = iu + `
   return log(1.0 + x);
 `;
-var nQ = Ke({ opSnippet: tQ });
-var sQ = { kernelName: yl, backendName: "webgl", kernelFunc: nQ };
-var rQ = "return float(a >= 1.0 && b >= 1.0);";
-var aQ = `
+var nZ = Ke({ opSnippet: tZ });
+var sZ = { kernelName: bl, backendName: "webgl", kernelFunc: nZ };
+var rZ = "return float(a >= 1.0 && b >= 1.0);";
+var aZ = `
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
 `;
-var iQ = qt({ opSnippet: rQ, packedOpSnippet: aQ, dtype: "bool" });
-var oQ = { kernelName: ko, backendName: "webgl", kernelFunc: iQ };
-var uQ = "return float(!(x >= 1.0));";
-var lQ = Ke({ opSnippet: uQ });
-var cQ = { kernelName: vl, backendName: "webgl", kernelFunc: lQ };
-var dQ = "return float(a >= 1.0 || b >= 1.0);";
-var pQ = `
+var iZ = qt({ opSnippet: rZ, packedOpSnippet: aZ, dtype: "bool" });
+var oZ = { kernelName: wo, backendName: "webgl", kernelFunc: iZ };
+var uZ = "return float(!(x >= 1.0));";
+var lZ = Ke({ opSnippet: uZ });
+var cZ = { kernelName: yl, backendName: "webgl", kernelFunc: lZ };
+var dZ = "return float(a >= 1.0 || b >= 1.0);";
+var pZ = `
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
 `;
-var hQ = qt({ opSnippet: dQ, packedOpSnippet: pQ, dtype: "bool" });
-var fQ = { kernelName: Jd, backendName: "webgl", kernelFunc: hQ };
-var mQ = class {
+var hZ = qt({ opSnippet: dZ, packedOpSnippet: pZ, dtype: "bool" });
+var fZ = { kernelName: Zd, backendName: "webgl", kernelFunc: hZ };
+var mZ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["x"], this.outputShape = [];
     let a = t, i = e[3] - 1;
@@ -29643,7 +29644,7 @@ var mQ = class {
     `;
   }
 };
-var gQ = class {
+var gZ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
     let a = t, i = e[3] - 1;
@@ -29714,12 +29715,12 @@ var gQ = class {
     `;
   }
 };
-var bQ = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = X().getBool("WEBGL_PACK_NORMALIZATION") ? new gQ(r.shape, a, i, o, u) : new mQ(r.shape, a, i, o, u);
+var bZ = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = X().getBool("WEBGL_PACK_NORMALIZATION") ? new gZ(r.shape, a, i, o, u) : new mZ(r.shape, a, i, o, u);
   return n.runWebGLProgram(l, [r], r.dtype);
 };
-var yQ = { kernelName: ep, backendName: "webgl", kernelFunc: bQ };
-var vQ = class {
+var yZ = { kernelName: Jd, backendName: "webgl", kernelFunc: bZ };
+var vZ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["inputImage", "outputImage", "dy"], this.outputShape = [], this.outputShape = e, this.depth = e[3], this.depthRadius = t, this.bias = n, this.alpha = s, this.beta = r, this.userCode = `
       void main() {
@@ -29779,12 +29780,12 @@ var vQ = class {
     `;
   }
 };
-var xQ = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s, p = new vQ(r.shape, o, u, l, c);
+var xZ = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r, y: a, dy: i } = t, { depthRadius: o, bias: u, alpha: l, beta: c } = s, p = new vZ(r.shape, o, u, l, c);
   return n.runWebGLProgram(p, [r, a, i], r.dtype);
 };
-var wQ = { kernelName: xg, backendName: "webgl", kernelFunc: xQ };
-function kQ(e, t, n, s) {
+var wZ = { kernelName: vg, backendName: "webgl", kernelFunc: xZ };
+function kZ(e, t, n, s) {
   let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = yi(o, e.dtype, "max", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
   return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
 }
@@ -29795,12 +29796,12 @@ function Q1(e) {
       let v = n.texData.get(h.dataId).values, x = new Array(o);
       for (let T = 0; T < x.length; T++)
         x[T] = r.shape[c[T]];
-      let k = vv(v, r.shape, r.dtype, c, x);
+      let k = yv(v, r.shape, r.dtype, c, x);
       h = n.makeTensorInfo(x, r.dtype);
       let C = n.texData.get(h.dataId);
       C.values = k;
     } else
-      h = Xp(r, c, n);
+      h = Kp(r, c, n);
     l = N.getInnerMostAxes(l.length, o);
   }
   N.assertAxesAreInnerMostDims("max", l, o);
@@ -29813,39 +29814,39 @@ function Q1(e) {
     let k = n.texData.get(b.dataId);
     k.values = x;
   } else
-    b = kQ(h, m, g, n);
+    b = kZ(h, m, g, n);
   return p && n.disposeIntermediateTensorInfo(h), b;
 }
-var IQ = { kernelName: Va, backendName: "webgl", kernelFunc: Q1 };
-var SQ = E1 + `
+var IZ = { kernelName: Va, backendName: "webgl", kernelFunc: Q1 };
+var SZ = E1 + `
   return max(a, b);
 `;
-var CQ = `
+var CZ = `
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  ` + jp + `
+  ` + qp + `
   return result;
 `;
-var NQ = qt({ opSnippet: SQ, packedOpSnippet: CQ, cpuKernelImpl: BK });
-var TQ = { kernelName: Wa, backendName: "webgl", kernelFunc: NQ };
-function $Q(e) {
+var NZ = qt({ opSnippet: SZ, packedOpSnippet: CZ, cpuKernelImpl: BK });
+var TZ = { kernelName: Wa, backendName: "webgl", kernelFunc: NZ };
+function $Z(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
-  nu(r, "maxPool");
+  tu(r, "maxPool");
   let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
   w.assert(N.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
   let c = N.computePool2DInfo(r.shape, a, i, l, o, u);
   if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
     return En({ inputs: { x: r }, backend: n });
-  let p = new tl(c, "max", false);
+  let p = new el(c, "max", false);
   return n.runWebGLProgram(p, [r], r.dtype);
 }
-var _Q = { kernelName: Ua, backendName: "webgl", kernelFunc: $Q };
-function AQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = N.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new wv(p, "max", false);
+var _Z = { kernelName: Ua, backendName: "webgl", kernelFunc: $Z };
+function AZ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = N.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new xv(p, "max", false);
   return n.runWebGLProgram(d, [r], r.dtype);
 }
-var EQ = { kernelName: tp, backendName: "webgl", kernelFunc: AQ };
-var RQ = class {
+var EZ = { kernelName: ep, backendName: "webgl", kernelFunc: AZ };
+var RZ = class {
   constructor(e) {
     this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
     let t = e.strideHeight, n = e.strideWidth, s = e.dilationHeight, r = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = r - 1 - e.padInfo.top, o = a - 1 - e.padInfo.left, u = r * a - 1;
@@ -29898,7 +29899,7 @@ var RQ = class {
     `;
   }
 };
-var DQ = class {
+var DZ = class {
   constructor(e) {
     this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
     let t = e.strideDepth, n = e.strideHeight, s = e.strideWidth, r = e.dilationDepth, a = e.dilationHeight, i = e.dilationWidth, o = e.effectiveFilterDepth, u = e.effectiveFilterHeight, l = e.effectiveFilterWidth, c = o - 1 - e.padInfo.front, p = u - 1 - e.padInfo.top, d = l - 1 - e.padInfo.left, h = o * u * l - 1;
@@ -29969,60 +29970,60 @@ var DQ = class {
     `;
   }
 };
-function FQ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = N.computePool3DInfo(i.shape, o, u, p, l, c), h = new wv(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new DQ(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
+function FZ(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a, { filterSize: o, strides: u, pad: l, dimRoundingMode: c } = s, p = [1, 1, 1], d = N.computePool3DInfo(i.shape, o, u, p, l, c), h = new xv(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new DZ(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
   return n.disposeIntermediateTensorInfo(f), g;
 }
-var OQ = { kernelName: kg, backendName: "webgl", kernelFunc: FQ };
-function PQ(e) {
+var OZ = { kernelName: wg, backendName: "webgl", kernelFunc: FZ };
+function PZ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
-  nu([a, i], "maxPoolGrad");
-  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = N.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new tl(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new RQ(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
+  tu([a, i], "maxPoolGrad");
+  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = N.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new el(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new RZ(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
   return n.disposeIntermediateTensorInfo(m), b;
 }
-var zQ = { kernelName: wg, backendName: "webgl", kernelFunc: PQ };
-function MQ(e, t, n, s) {
-  let r = new tl(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
-  r = new tl(n, "max", true, true, t);
+var zZ = { kernelName: xg, backendName: "webgl", kernelFunc: PZ };
+function MZ(e, t, n, s) {
+  let r = new el(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
+  r = new el(n, "max", true, true, t);
   let i = s.runWebGLProgram(r, [e], "float32");
   return [a, i];
 }
-var LQ = { kernelName: Ig, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var LZ = { kernelName: kg, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
   w.assert(s.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);
   let l = [1, 1];
   w.assert(N.eitherStridesOrDilationsAreOne(a, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);
-  let c = N.computePool2DInfo(s.shape, r, a, l, i), [p, d] = MQ(s, o, c, u);
+  let c = N.computePool2DInfo(s.shape, r, a, l, i), [p, d] = MZ(s, o, c, u);
   return [p, d];
 } };
-function BQ(e, t, n, s) {
+function BZ(e, t, n, s) {
   let r = w.sizeFromShape(t), i = w.sizeFromShape(e.shape) / r, o = he({ inputs: { x: e }, attrs: { shape: [i, r] }, backend: s }), u = yi(o, "float32", "mean", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
   return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
 }
-var VQ = { kernelName: Ga, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var VZ = { kernelName: Ga, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { x: s } = e, { keepDims: r, axis: a } = t, i = n, o = s.shape.length, u = w.parseAxisParam(a, s.shape), l = u, c = N.getAxesPermutation(l, o), p = c != null, d = i.shouldExecuteOnCPU([s]), h = [], f = s;
   if (p) {
     if (d) {
       let x = i.texData.get(f.dataId).values, k = new Array(o);
       for (let E = 0; E < k.length; E++)
         k[E] = s.shape[c[E]];
-      let C = vv(x, s.shape, s.dtype, c, k);
+      let C = yv(x, s.shape, s.dtype, c, k);
       f = i.makeTensorInfo(k, s.dtype);
       let T = i.texData.get(f.dataId);
       T.values = C;
     } else
-      f = Xp(s, c, i);
+      f = Kp(s, c, i);
     h.push(f), l = N.getInnerMostAxes(l.length, o);
   }
   N.assertAxesAreInnerMostDims("sum", l, o);
   let [m, g] = N.computeOutAndReduceShapes(f.shape, l), b = m;
   r && (b = N.expandShapeToKeepDim(m, u));
-  let y = BQ(f, g, b, i);
+  let y = BZ(f, g, b, i);
   for (let v of h)
     i.disposeIntermediateTensorInfo(v);
   return y;
 } };
-function WQ(e) {
+function WZ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = u, c = N.getAxesPermutation(l, o), p = r;
   c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = N.getInnerMostAxes(l.length, r.shape.length)), N.assertAxesAreInnerMostDims("min", l, o);
   let [d, h] = N.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = yi(m, m.dtype, "min", n), b;
@@ -30033,19 +30034,19 @@ function WQ(e) {
     b = he({ inputs: { x: g }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(g), c != null && n.disposeIntermediateTensorInfo(p), b;
 }
-var UQ = { kernelName: Ha, backendName: "webgl", kernelFunc: WQ };
-var GQ = E1 + `
+var UZ = { kernelName: Ha, backendName: "webgl", kernelFunc: WZ };
+var GZ = E1 + `
   return min(a, b);
 `;
-var HQ = `
+var HZ = `
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  ` + jp + `
+  ` + qp + `
   return result;
 `;
-var qQ = qt({ opSnippet: GQ, packedOpSnippet: HQ, cpuKernelImpl: VK });
-var jQ = { kernelName: qa, backendName: "webgl", kernelFunc: qQ };
-var KQ = class {
+var qZ = qt({ opSnippet: GZ, packedOpSnippet: HZ, cpuKernelImpl: VK });
+var jZ = { kernelName: qa, backendName: "webgl", kernelFunc: qZ };
+var KZ = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.outputShape = t.map((l, c) => l[0] + e[c] + l[1]);
     let s = e.length, r = ot(s), a = t.map((l) => l[0]).join(","), i = t.map((l, c) => l[0] + e[c]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s), u = n === "reflect" ? 0 : 1;
@@ -30085,7 +30086,7 @@ var KQ = class {
     `;
   }
 };
-var XQ = class {
+var XZ = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.outputShape = t.map((h, f) => h[0] + e[f] + h[1]);
     let s = e.length, r = ot(s), a = t.map((h) => h[0]).join(","), i = t.map((h, f) => h[0] + e[f]).join(","), o = un("rc", s), u = un("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = n === "reflect" ? 0 : 1, d = "";
@@ -30155,22 +30156,22 @@ var XQ = class {
     `;
   }
 };
-var YQ = ({ inputs: e, backend: t, attrs: n }) => {
-  let { x: s } = e, { paddings: r, mode: a } = n, i = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new XQ(s.shape, r, a) : new KQ(s.shape, r, a);
+var YZ = ({ inputs: e, backend: t, attrs: n }) => {
+  let { x: s } = e, { paddings: r, mode: a } = n, i = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new XZ(s.shape, r, a) : new KZ(s.shape, r, a);
   return t.runWebGLProgram(i, [s], s.dtype);
 };
-var QQ = { kernelName: ja, backendName: "webgl", kernelFunc: YQ };
-var ZQ = `if (b == 0.0) return NAN;
+var QZ = { kernelName: ja, backendName: "webgl", kernelFunc: YZ };
+var ZZ = `if (b == 0.0) return NAN;
   return mod(a, b);`;
-var JQ = `
+var JZ = `
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  ` + jp + `
+  ` + qp + `
   return result;
 `;
-var eZ = qt({ opSnippet: ZQ, packedOpSnippet: JQ });
-var tZ = { kernelName: xl, backendName: "webgl", kernelFunc: eZ };
-var nZ = class {
+var e7 = qt({ opSnippet: ZZ, packedOpSnippet: JZ });
+var t7 = { kernelName: vl, backendName: "webgl", kernelFunc: e7 };
+var n7 = class {
   constructor(e, t, n) {
     this.variableNames = ["probs"], this.customUniforms = [{ name: "seed", type: "float" }], this.outputShape = [e, n], this.userCode = `
       void main() {
@@ -30195,12 +30196,12 @@ var nZ = class {
     `;
   }
 };
-var sZ = `
+var s7 = `
 if (a == b) {
   return 1.0;
 };
 return a / b;`;
-var rZ = `
+var r7 = `
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -30219,25 +30220,25 @@ var rZ = `
 
   return result;
 `;
-var Z1 = qt({ opSnippet: sZ, packedOpSnippet: rZ, checkOutOfBounds: true });
-var aZ = { kernelName: Ea, backendName: "webgl", kernelFunc: Z1 };
-var ww = "return a - b;";
-var J1 = qt({ opSnippet: ww, packedOpSnippet: ww, supportsComplex: true, cpuKernelImpl: sX });
-var iZ = { kernelName: ui, backendName: "webgl", kernelFunc: J1 };
+var Z1 = qt({ opSnippet: s7, packedOpSnippet: r7, checkOutOfBounds: true });
+var a7 = { kernelName: Ea, backendName: "webgl", kernelFunc: Z1 };
+var xw = "return a - b;";
+var J1 = qt({ opSnippet: xw, packedOpSnippet: xw, supportsComplex: true, cpuKernelImpl: sX });
+var i7 = { kernelName: ui, backendName: "webgl", kernelFunc: J1 };
 function e2(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = Q1({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = N.expandShapeToKeepDim(o.shape, i), l = he({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = J1({ inputs: { a: r, b: l }, backend: n }), p = K1({ inputs: { x: c }, backend: n }), d = Yp({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = Z1({ inputs: { a: p, b: h }, backend: n });
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = Q1({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = N.expandShapeToKeepDim(o.shape, i), l = he({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = J1({ inputs: { a: r, b: l }, backend: n }), p = K1({ inputs: { x: c }, backend: n }), d = Xp({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = Z1({ inputs: { a: p, b: h }, backend: n });
   return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(c), n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), n.disposeIntermediateTensorInfo(h), f;
 }
-var oZ = { kernelName: ii, backendName: "webgl", kernelFunc: e2 };
-function uZ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : e2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new nZ(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
+var o7 = { kernelName: ii, backendName: "webgl", kernelFunc: e2 };
+function u7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : e2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new n7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
   return o || n.disposeIntermediateTensorInfo(u), h;
 }
-var lZ = { kernelName: Sg, backendName: "webgl", kernelFunc: uZ };
-var cZ = ts + `
+var l7 = { kernelName: Ig, backendName: "webgl", kernelFunc: u7 };
+var c7 = ts + `
   return -x;
 `;
-var dZ = `
+var d7 = `
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -30248,38 +30249,38 @@ var dZ = `
 
   return result;
 `;
-function pZ(e) {
+function p7(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (n.shouldExecuteOnCPU([s])) {
     let a = n.texData.get(s.dataId), [i, o] = UK(a.values, s.shape, s.dtype);
     return n.makeTensorInfo(o, s.dtype, i);
   }
   let r;
-  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Yr(s.shape, dZ) : r = new Hs(s.shape, cZ), n.runWebGLProgram(r, [s], s.dtype);
+  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Yr(s.shape, d7) : r = new Hs(s.shape, c7), n.runWebGLProgram(r, [s], s.dtype);
 }
-var hZ = { kernelName: Io, backendName: "webgl", kernelFunc: pZ };
-var fZ = xs.nonMaxSuppressionV3Impl;
-function mZ(e) {
+var h7 = { kernelName: ko, backendName: "webgl", kernelFunc: p7 };
+var f7 = xs.nonMaxSuppressionV3Impl;
+function m7(e) {
   N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = fZ(l, c, i, o, u);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = f7(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var gZ = { kernelName: Co, backendName: "webgl", kernelFunc: mZ };
-var bZ = xs.nonMaxSuppressionV4Impl;
-function yZ(e) {
+var g7 = { kernelName: So, backendName: "webgl", kernelFunc: m7 };
+var b7 = xs.nonMaxSuppressionV4Impl;
+function y7(e) {
   N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = bZ(c, p, i, o, u, l);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), { selectedIndices: d, validOutputs: h } = b7(c, p, i, o, u, l);
   return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
 }
-var vZ = { kernelName: wl, backendName: "webgl", kernelFunc: yZ };
-var xZ = xs.nonMaxSuppressionV5Impl;
-function wZ(e) {
+var v7 = { kernelName: xl, backendName: "webgl", kernelFunc: y7 };
+var x7 = xs.nonMaxSuppressionV5Impl;
+function w7(e) {
   N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
-  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = xZ(c, p, d, h, f, m);
+  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = x7(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var kZ = { kernelName: No, backendName: "webgl", kernelFunc: wZ };
-var IZ = class {
+var k7 = { kernelName: Co, backendName: "webgl", kernelFunc: w7 };
+var I7 = class {
   constructor(e, t, n, s) {
     this.variableNames = ["indices"], this.outputShape = [e, t], this.userCode = `
       void main() {
@@ -30291,49 +30292,49 @@ var IZ = class {
     `;
   }
 };
-var SZ = (e) => {
-  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = w.sizeFromShape(r.shape), l = new IZ(u, a, i, o), c = he({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
+var S7 = (e) => {
+  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = w.sizeFromShape(r.shape), l = new I7(u, a, i, o), c = he({ inputs: { x: r }, backend: n, attrs: { shape: [u] } }), p = n.runWebGLProgram(l, [c], r.dtype);
   n.disposeIntermediateTensorInfo(c);
   let d = [...r.shape, a], h = he({ inputs: { x: p }, backend: n, attrs: { shape: d } });
   return n.disposeIntermediateTensorInfo(p), h;
 };
-var CZ = { kernelName: $o, backendName: "webgl", kernelFunc: SZ };
-function Md(e) {
+var C7 = { kernelName: To, backendName: "webgl", kernelFunc: S7 };
+function zd(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = Jl({ inputs: { input: s }, backend: n }), a = Md({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = Er({ inputs: { real: a, imag: o }, backend: n });
+    let r = Zl({ inputs: { input: s }, backend: n }), a = zd({ inputs: { x: r }, backend: n }), i = Yp({ inputs: { input: s }, backend: n }), o = zd({ inputs: { x: i }, backend: n }), u = Er({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
-    return ec({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
+    return Jl({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var NZ = { kernelName: Ho, backendName: "webgl", kernelFunc: Md };
+var N7 = { kernelName: Go, backendName: "webgl", kernelFunc: zd };
 function t2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = Jl({ inputs: { input: s }, backend: n }), a = t2({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = Er({ inputs: { real: a, imag: o }, backend: n });
+    let r = Zl({ inputs: { input: s }, backend: n }), a = t2({ inputs: { x: r }, backend: n }), i = Yp({ inputs: { input: s }, backend: n }), o = zd({ inputs: { x: i }, backend: n }), u = Er({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
   } else
-    return ec({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
+    return Jl({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var TZ = { kernelName: To, backendName: "webgl", kernelFunc: t2 };
-function $Z(e) {
+var T7 = { kernelName: No, backendName: "webgl", kernelFunc: t2 };
+function $7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Hm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return Gm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Hm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = Gm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
   }), l = W1({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
 }
-var _Z = { kernelName: _o, backendName: "webgl", kernelFunc: $Z };
-var AZ = class {
+var _7 = { kernelName: $o, backendName: "webgl", kernelFunc: $7 };
+var A7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((u, l) => u[0] + e[l] + u[1]);
     let s = e.length, r = ot(s), a = t.map((u) => u[0]).join(","), i = t.map((u, l) => u[0] + e[l]).join(","), o = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, s);
@@ -30369,7 +30370,7 @@ var AZ = class {
     `;
   }
 };
-var EZ = class {
+var E7 = class {
   constructor(e, t, n) {
     this.variableNames = ["x"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "value", type: "float" }], this.outputShape = t.map((f, m) => f[0] + e[m] + f[1]);
     let s = e.length, r = ot(s), a = t.map((f) => f[0]).join(","), i = t.map((f, m) => f[0] + e[m]).join(","), o = un("rc", s), u = un("source", s), l = `${o[s - 1]} < ${this.outputShape[s - 1]}`, c = s === 1 ? "source" : `vec2(${u.slice(-2).join()})`, p = [`${r} rc = outputLoc;`, `${o[s - 1]} += 1;
@@ -30406,13 +30407,13 @@ var n2 = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
-    return ec({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
+    return Jl({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
   }
-  let o = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new EZ(r.shape, a, i) : new AZ(r.shape, a, i), u = [[i]];
+  let o = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new E7(r.shape, a, i) : new A7(r.shape, a, i), u = [[i]];
   return n.runWebGLProgram(o, [r], r.dtype, u);
 };
-var RZ = { kernelName: Xa, backendName: "webgl", kernelFunc: n2 };
-var DZ = `
+var R7 = { kernelName: Xa, backendName: "webgl", kernelFunc: n2 };
+var D7 = `
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -30422,7 +30423,7 @@ var DZ = `
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
 `;
-var FZ = `
+var F7 = `
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -30436,12 +30437,12 @@ var FZ = `
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  ` + jp + `
+  ` + qp + `
   return result;
 `;
-var OZ = qt({ opSnippet: DZ, packedOpSnippet: FZ });
-var PZ = { kernelName: Ya, backendName: "webgl", kernelFunc: OZ };
-function zZ(e) {
+var O7 = qt({ opSnippet: D7, packedOpSnippet: F7 });
+var P7 = { kernelName: Ya, backendName: "webgl", kernelFunc: O7 };
+function z7(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = r.shape.length, u = [], l = w.parseAxisParam(a, r.shape), c = l, p = N.getAxesPermutation(c, o), d = r;
   p != null && (d = cn({ inputs: { x: r }, backend: n, attrs: { perm: p } }), c = N.getInnerMostAxes(c.length, o), u.push(d)), N.assertAxesAreInnerMostDims("prod", c, o);
   let h;
@@ -30449,7 +30450,7 @@ function zZ(e) {
     let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = HK(d.shape, d.dtype, f, c);
     h = n.makeTensorInfo(g, b, m);
   } else {
-    let [f, m] = N.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = he({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = cp(r.dtype), v = yi(b, y, "prod", n);
+    let [f, m] = N.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = he({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = lp(r.dtype), v = yi(b, y, "prod", n);
     h = he({ inputs: { x: v }, backend: n, attrs: { shape: f } }), u.push(b), u.push(v);
   }
   if (i) {
@@ -30459,19 +30460,19 @@ function zZ(e) {
   }
   return u.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
 }
-var MZ = { kernelName: Ao, backendName: "webgl", kernelFunc: zZ };
+var M7 = { kernelName: _o, backendName: "webgl", kernelFunc: z7 };
 var s2 = (e) => {
   let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = qK(s, r, a, i);
   return t.makeTensorInfo([o.length], i, o);
 };
-var LZ = { kernelName: kl, backendName: "webgl", kernelFunc: s2 };
-var BZ = "return 1.0 / x;";
-var VZ = Ke({ opSnippet: BZ });
-var WZ = { kernelName: Il, backendName: "webgl", kernelFunc: VZ };
-var UZ = ts + `
+var L7 = { kernelName: wl, backendName: "webgl", kernelFunc: s2 };
+var B7 = "return 1.0 / x;";
+var V7 = Ke({ opSnippet: B7 });
+var W7 = { kernelName: kl, backendName: "webgl", kernelFunc: V7 };
+var U7 = ts + `
   return (x < 0.0) ? 0.0 : x;
 `;
-var GZ = `
+var G7 = `
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -30482,12 +30483,12 @@ var GZ = `
 
   return result;
 `;
-var HZ = Ke({ opSnippet: UZ, packedOpSnippet: GZ });
-var qZ = { kernelName: Za, backendName: "webgl", kernelFunc: HZ };
-var jZ = ts + `
+var H7 = Ke({ opSnippet: U7, packedOpSnippet: G7 });
+var q7 = { kernelName: Za, backendName: "webgl", kernelFunc: H7 };
+var j7 = ts + `
   return (x < 0.0) ? 0.0 : min(6.0, x);
 `;
-var KZ = `
+var K7 = `
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -30498,9 +30499,9 @@ var KZ = `
 
   return result;
 `;
-var XZ = Ke({ opSnippet: jZ, packedOpSnippet: KZ });
-var YZ = { kernelName: ei, backendName: "webgl", kernelFunc: XZ };
-var QZ = class {
+var X7 = Ke({ opSnippet: j7, packedOpSnippet: K7 });
+var Y7 = { kernelName: ei, backendName: "webgl", kernelFunc: X7 };
+var Q7 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.outputShape = [];
     let [a, i, o, u] = e;
@@ -30542,7 +30543,7 @@ var QZ = class {
     `;
   }
 };
-var ZZ = class {
+var Z7 = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = [];
     let [a, i, o, u] = e;
@@ -30628,11 +30629,11 @@ var ZZ = class {
     `;
   }
 };
-function JZ(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ZZ(r.shape, u, l, a, i) : new QZ(r.shape, u, l, a, i);
+function J7(e) {
+  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Z7(r.shape, u, l, a, i) : new Q7(r.shape, u, l, a, i);
   return n.runWebGLProgram(c, [r], "float32");
 }
-var eJ = { kernelName: Ja, backendName: "webgl", kernelFunc: JZ };
+var eJ = { kernelName: Ja, backendName: "webgl", kernelFunc: J7 };
 var tJ = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
@@ -30725,7 +30726,7 @@ function nJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new tJ(a.shape, r.shape, i);
   return n.runWebGLProgram(o, [a], a.dtype);
 }
-var sJ = { kernelName: Ng, backendName: "webgl", kernelFunc: nJ };
+var sJ = { kernelName: Cg, backendName: "webgl", kernelFunc: nJ };
 var rJ = class {
   constructor(e, t, n, s, r) {
     this.variableNames = ["A"], this.outputShape = [];
@@ -30811,7 +30812,7 @@ function iJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new aJ(r.shape, u, l, a, i) : new rJ(r.shape, u, l, a, i);
   return n.runWebGLProgram(c, [r], r.dtype);
 }
-var oJ = { kernelName: Sl, backendName: "webgl", kernelFunc: iJ };
+var oJ = { kernelName: Il, backendName: "webgl", kernelFunc: iJ };
 var uJ = class {
   constructor(e, t, n) {
     this.variableNames = ["dy"], this.outputShape = [], this.outputShape = t;
@@ -30893,7 +30894,7 @@ function lJ(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new uJ(a.shape, r.shape, i);
   return n.runWebGLProgram(o, [a], a.dtype);
 }
-var cJ = { kernelName: Cg, backendName: "webgl", kernelFunc: lJ };
+var cJ = { kernelName: Sg, backendName: "webgl", kernelFunc: lJ };
 var dJ = class {
   constructor(e, t) {
     this.variableNames = ["x"];
@@ -30983,7 +30984,7 @@ function hJ(e) {
   let u = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new pJ(r.shape, o) : new dJ(r.shape, o);
   return n.runWebGLProgram(u, [r], r.dtype);
 }
-var fJ = { kernelName: Ro, backendName: "webgl", kernelFunc: hJ };
+var fJ = { kernelName: Eo, backendName: "webgl", kernelFunc: hJ };
 var mJ = class {
   constructor(e, t) {
     this.variableNames = ["Image"], this.outputShape = [], this.customUniforms = [{ name: "params", type: "vec4" }];
@@ -31012,7 +31013,7 @@ var mJ = class {
     `;
   }
 };
-var gJ = { kernelName: qo, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var gJ = { kernelName: Ho, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new mJ(s.shape, a), [l, c] = N.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
   return o.runWebGLProgram(u, [s], s.dtype, p);
 } };
@@ -31033,7 +31034,7 @@ var bJ = `
   }
 `;
 var yJ = Ke({ opSnippet: bJ });
-var vJ = { kernelName: Do, backendName: "webgl", kernelFunc: yJ };
+var vJ = { kernelName: Ro, backendName: "webgl", kernelFunc: yJ };
 var xJ = "return inversesqrt(x);";
 var wJ = Ke({ opSnippet: xJ, cpuKernelImpl: jK });
 var kJ = { kernelName: ti, backendName: "webgl", kernelFunc: wJ };
@@ -31075,7 +31076,7 @@ function IJ(e) {
   let h = he({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = he({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = n.makeTensorInfo([], "float32", new Float32Array([0])), g = new r2(u, o, h.shape.length, f.shape.length, c, d), b = n.runWebGLProgram(g, [f, h, m], f.dtype), y = he({ inputs: { x: b }, backend: n, attrs: { shape: i } });
   return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(m), y;
 }
-var SJ = { kernelName: Fo, backendName: "webgl", kernelFunc: IJ };
+var SJ = { kernelName: Do, backendName: "webgl", kernelFunc: IJ };
 var CJ = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.outputShape = t;
@@ -31108,7 +31109,7 @@ function NJ(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new CJ(s.shape.length, r.shape, r.shape.length);
   return n.runWebGLProgram(i, [s, r, a], yn(r.dtype, a.dtype));
 }
-var TJ = { kernelName: Oo, backendName: "webgl", kernelFunc: NJ };
+var TJ = { kernelName: Fo, backendName: "webgl", kernelFunc: NJ };
 var $J = `
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
@@ -31117,8 +31118,8 @@ var $J = `
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
 `;
 var _J = Ke({ opSnippet: $J });
-var AJ = { kernelName: Cl, backendName: "webgl", kernelFunc: _J };
-var EJ = ou + `
+var AJ = { kernelName: Sl, backendName: "webgl", kernelFunc: _J };
+var EJ = iu + `
   return 1.0 / (1.0 + exp(-1.0 * x));
 `;
 var RJ = `
@@ -31139,8 +31140,8 @@ var OJ = `
   return sign(x);
 `;
 var PJ = Ke({ opSnippet: OJ });
-var zJ = { kernelName: Nl, backendName: "webgl", kernelFunc: PJ };
-var MJ = ou + `
+var zJ = { kernelName: Cl, backendName: "webgl", kernelFunc: PJ };
+var MJ = iu + `
   return sin(x);
 `;
 var LJ = Ke({ opSnippet: MJ });
@@ -31150,7 +31151,7 @@ var VJ = `
   return (e2x - 1.0 / e2x) / 2.0;
 `;
 var WJ = Ke({ opSnippet: VJ });
-var UJ = { kernelName: zo, backendName: "webgl", kernelFunc: WJ };
+var UJ = { kernelName: Po, backendName: "webgl", kernelFunc: WJ };
 var GJ = `
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
@@ -31173,7 +31174,7 @@ var GJ = `
   return result;
 `;
 var HJ = Ke({ opSnippet: GJ });
-var qJ = { kernelName: Tl, backendName: "webgl", kernelFunc: HJ };
+var qJ = { kernelName: Nl, backendName: "webgl", kernelFunc: HJ };
 var jJ = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
   w.assert(r.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
@@ -31184,7 +31185,7 @@ var jJ = (e) => {
   let l = [], c = n2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = N.getReshaped(c.shape, a, o, false), d = N.getPermuted(p.length, a.length, false), h = N.getReshapedPermuted(c.shape, a, o, false), f = he({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = cn({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
 };
-var KJ = { kernelName: Mo, backendName: "webgl", kernelFunc: jJ };
+var KJ = { kernelName: zo, backendName: "webgl", kernelFunc: jJ };
 function XJ(e) {
   let { inputs: t, backend: n } = e, { indices: s, values: r, denseShape: a, defaultValue: i } = t;
   if (a.shape.length !== 1)
@@ -31202,7 +31203,7 @@ function XJ(e) {
   let o = n.readSync(s.dataId), u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = n.readSync(i.dataId)[0], [p, d, h, f, m] = YK(o, s.shape, s.dtype, u, r.dtype, l, c);
   return [n.makeTensorInfo(d, s.dtype, p), n.makeTensorInfo([d[0]], r.dtype, h), n.makeTensorInfo([f.length], "bool", new Uint8Array(f.map((g) => Number(g)))), n.makeTensorInfo([m.length], s.dtype, new Int32Array(m))];
 }
-var YJ = { kernelName: sp, backendName: "webgl", kernelFunc: XJ };
+var YJ = { kernelName: np, backendName: "webgl", kernelFunc: XJ };
 function QJ(e) {
   let { inputs: t, backend: n } = e, { inputIndices: s, inputShape: r, newShape: a } = t;
   if (s.shape.length !== 2)
@@ -31214,7 +31215,7 @@ function QJ(e) {
   let i = Array.from(n.readSync(r.dataId)), o = n.readSync(s.dataId), u = Array.from(n.readSync(a.dataId)), [l, c, p] = QK(o, s.shape, s.dtype, i, u);
   return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
 }
-var ZJ = { kernelName: $l, backendName: "webgl", kernelFunc: QJ };
+var ZJ = { kernelName: Tl, backendName: "webgl", kernelFunc: QJ };
 function JJ(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
@@ -31228,7 +31229,7 @@ function JJ(e) {
   let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = N1(i, s.shape, s.dtype, o, u, true);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var eee = { kernelName: rp, backendName: "webgl", kernelFunc: JJ };
+var eee = { kernelName: sp, backendName: "webgl", kernelFunc: JJ };
 function tee(e) {
   let { inputs: t, backend: n } = e, { data: s, indices: r, segmentIds: a } = t;
   if (s.shape.length < 1)
@@ -31242,30 +31243,30 @@ function tee(e) {
   let i = n.readSync(s.dataId), o = n.readSync(r.dataId), u = n.readSync(a.dataId), [l, c] = N1(i, s.shape, s.dtype, o, u);
   return n.makeTensorInfo(c, s.dtype, l);
 }
-var nee = { kernelName: ap, backendName: "webgl", kernelFunc: tee };
+var nee = { kernelName: rp, backendName: "webgl", kernelFunc: tee };
 function see(e) {
   let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = N.calculateShapes(a, r, o), d = false, h = new r2(l, u, r.shape.length, a.shape.length, c, [p, 1], d), f = n.runWebGLProgram(h, [a, r, i], a.dtype), m = he({ inputs: { x: f }, backend: n, attrs: { shape: o } });
   return n.disposeIntermediateTensorInfo(f), m;
 }
-var ree = { kernelName: ip, backendName: "webgl", kernelFunc: see };
+var ree = { kernelName: ap, backendName: "webgl", kernelFunc: see };
 function aee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = N.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
     h[o] = d;
-    let f = uu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    let f = ou({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
     return c[o] += d, f;
   });
 }
-var iee = { kernelName: Lo, backendName: "webgl", kernelFunc: aee };
-var kw = "return sqrt(x);";
-var oee = Ke({ opSnippet: kw, packedOpSnippet: kw, cpuKernelImpl: ZK });
+var iee = { kernelName: Mo, backendName: "webgl", kernelFunc: aee };
+var ww = "return sqrt(x);";
+var oee = Ke({ opSnippet: ww, packedOpSnippet: ww, cpuKernelImpl: ZK });
 var uee = { kernelName: ri, backendName: "webgl", kernelFunc: oee };
 var lee = "return x * x;";
 var cee = Ke({ opSnippet: lee });
-var dee = { kernelName: _l, backendName: "webgl", kernelFunc: cee };
-var Iw = "return (a - b) * (a - b);";
-var pee = qt({ opSnippet: Iw, packedOpSnippet: Iw });
+var dee = { kernelName: $l, backendName: "webgl", kernelFunc: cee };
+var kw = "return (a - b) * (a - b);";
+var pee = qt({ opSnippet: kw, packedOpSnippet: kw });
 var hee = { kernelName: oi, backendName: "webgl", kernelFunc: pee };
 function fee({ inputs: e, attrs: t, backend: n }) {
   let { x: s } = e, r = ts + `
@@ -31301,7 +31302,7 @@ function bee(e) {
     k = he({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let T = wt.computeOutShape(y, v, x), E = uu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
+    let T = wt.computeOutShape(y, v, x), E = ou({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
     k = he({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(E);
   } else if (n.shouldExecuteOnCPU([r])) {
     let E = n.readSync(r.dataId), A = De(r.shape, r.dtype, E), P = JK(h, A, x, y);
@@ -31313,12 +31314,12 @@ function bee(e) {
   let C = he({ inputs: { x: k }, backend: n, attrs: { shape: f } });
   return n.disposeIntermediateTensorInfo(k), C;
 }
-var yee = { kernelName: Bo, backendName: "webgl", kernelFunc: bee };
+var yee = { kernelName: Lo, backendName: "webgl", kernelFunc: bee };
 function vee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = eX(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var xee = { kernelName: op, backendName: "webgl", kernelFunc: vee };
+var xee = { kernelName: ip, backendName: "webgl", kernelFunc: vee };
 function wee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { skipEmpty: r } = s, { input: a, delimiter: i } = t;
   if (a.dtype !== "string")
@@ -31330,7 +31331,7 @@ function wee(e) {
   let o = n.readSync(a.dataId), u = n.readSync(i.dataId)[0], [l, c, p] = tX(o, u, r), d = c.length;
   return [n.makeTensorInfo([d, 2], "int32", l), n.makeTensorInfo([d], "string", c), n.makeTensorInfo([2], "int32", new Int32Array(p))];
 }
-var kee = { kernelName: Tg, backendName: "webgl", kernelFunc: wee };
+var kee = { kernelName: Ng, backendName: "webgl", kernelFunc: wee };
 function Iee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { numBuckets: r } = s, { input: a } = t;
   if (a.dtype !== "string")
@@ -31340,10 +31341,10 @@ function Iee(e) {
   let i = n.readSync(a.dataId), o = nX(i, r);
   return n.makeTensorInfo(a.shape, "int32", o);
 }
-var See = { kernelName: $g, backendName: "webgl", kernelFunc: Iee };
+var See = { kernelName: Tg, backendName: "webgl", kernelFunc: Iee };
 var Cee = "return tan(x);";
 var Nee = Ke({ opSnippet: Cee });
-var Tee = { kernelName: Vo, backendName: "webgl", kernelFunc: Nee };
+var Tee = { kernelName: Bo, backendName: "webgl", kernelFunc: Nee };
 var $ee = `
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
@@ -31474,7 +31475,7 @@ var Oee = class {
 function Wr(e, t) {
   t !== null && e.disposeIntermediateTensorInfo(t);
 }
-function Sw(e) {
+function Iw(e) {
   let t = 1;
   for (; t < e; )
     t *= 2;
@@ -31489,10 +31490,10 @@ function Pee(e) {
   if (a === 0)
     return l[l.length - 1] = 0, [n.makeTensorInfo(l, r.dtype, []), n.makeTensorInfo(l, "int32", [])];
   if (c === 1)
-    return [r, ec({ attrs: { shape: l, dtype: "int32", value: 0 }, backend: n })];
+    return [r, Jl({ attrs: { shape: l, dtype: "int32", value: 0 }, backend: n })];
   let p = n.texData.get(r.dataId), d = p !== null && p.isPacked, h = d ? n.unpackTensor(r) : r, m = w.sizeFromShape(l) / c, g = he({ inputs: { x: h }, attrs: { shape: [m, c] }, backend: n });
   d && Wr(n, h);
-  let b = Sw(a), y = Sw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, R, F) => {
+  let b = Iw(a), y = Iw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, R, F) => {
     let $ = x(), z = new Fee(F), q = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [R]], K = v;
     v = n.runWebGLProgram(z, $, "int32", q), Wr(n, K);
   };
@@ -31509,7 +31510,7 @@ function Pee(e) {
       k(K, Y, v.shape);
   }
   let C = v;
-  v = uu({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Wr(n, C);
+  v = ou({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Wr(n, C);
   let T = Y1({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
   Wr(n, g);
   let E = l.slice(0, -1);
@@ -31517,7 +31518,7 @@ function Pee(e) {
   let A = T;
   return T = he({ inputs: { x: T }, attrs: { shape: E }, backend: n }), Wr(n, A), [T, v];
 }
-var zee = { kernelName: Wo, backendName: "webgl", kernelFunc: Pee };
+var zee = { kernelName: Vo, backendName: "webgl", kernelFunc: Pee };
 var Mee = class {
   constructor(e, t, n, s, r, a) {
     this.variableNames = ["Image", "Transforms"], this.outputShape = a;
@@ -31658,14 +31659,14 @@ function Lee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { image: r, transforms: a } = t, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Mee(p, d, i, o, u, g);
   return n.runWebGLProgram(b, [r, a], "float32");
 }
-var Bee = { kernelName: Uo, backendName: "webgl", kernelFunc: Lee };
+var Bee = { kernelName: Wo, backendName: "webgl", kernelFunc: Lee };
 function Vee(e) {
   let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
-  nu(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
+  tu(a, "unique"), console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
   let i = s.readSync(a.dataId), { outputValues: o, outputShape: u, indices: l } = iX(i, r, a.shape, a.dtype);
   return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
 }
-var Wee = { kernelName: _g, backendName: "webgl", kernelFunc: Vee };
+var Wee = { kernelName: $g, backendName: "webgl", kernelFunc: Vee };
 function Uee(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
@@ -31677,12 +31678,12 @@ function Uee(e) {
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = uu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = ou({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = he({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
 }
-var Gee = { kernelName: Go, backendName: "webgl", kernelFunc: Uee };
+var Gee = { kernelName: Uo, backendName: "webgl", kernelFunc: Uee };
 var Hee = class {
   constructor(e, t) {
     this.variableNames = ["x", "segmentIds"];
@@ -31805,7 +31806,7 @@ function qee(e) {
   c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), u.push(p), l = N.getInnerMostAxes(1, o)[0]);
   let d = N.segment_util.computeOutShape(p.shape, l, i), h = w.sizeFromShape([p.shape[l]]), f = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, h] } });
   u.push(f);
-  let m = cp(r.dtype), g = (x, k, C, T, E) => {
+  let m = lp(r.dtype), g = (x, k, C, T, E) => {
     let A = x.shape[0], P = x.shape[1], R = N.segment_util.segOpComputeOptimalWindowSize(P, E), F = { windowSize: R, inSize: P, batchSize: A, numSegments: E }, $ = new Hee(F, k), z = n.compileAndRun($, [x, C], T);
     if (u.push(z), z.shape[1] === E)
       return z;
@@ -31819,10 +31820,10 @@ function qee(e) {
   }
   return u.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
 }
-var jee = { kernelName: up, backendName: "webgl", kernelFunc: qee };
-var Kee = [e8, n8, a8, u8, c8, h8, m8, b8, w8, I8, N8, _8, R8, P8, L8, V8, U8, j8, X8, Q8, tY, uY, cY, pY, yY, xY, SY, OX, TY, RY, PY, WY, GY, qY, KY, YY, JY, n7, a7, o7, l7, p7, f7, y7, x7, I7, N7, $7, R7, P7, B7, U7, q7, j7, X7, Q7, J7, t9, s9, o9, c9, h9, m9, y9, w9, C9, _9, FX, E9, AY, F9, z9, B9, zX, G9, K9, Y9, eQ, sQ, oQ, cQ, fQ, yQ, wQ, IQ, TQ, _Q, EQ, OQ, zQ, LQ, VQ, UQ, jQ, QQ, tZ, lZ, WX, hZ, gZ, vZ, kZ, fY, CZ, TZ, _Z, RZ, PZ, LX, MZ, LZ, mY, aZ, WZ, qZ, YZ, GX, eJ, sJ, oJ, cJ, fJ, gJ, vJ, kJ, SJ, TJ, AJ, FJ, zJ, BJ, UJ, iY, oZ, qJ, KJ, YJ, ZJ, eee, nee, ree, iee, uee, dee, hee, mee, yee, xee, kee, See, iZ, QX, Tee, Aee, Dee, zee, Bee, ZX, Wee, Gee, jee, NZ];
+var jee = { kernelName: op, backendName: "webgl", kernelFunc: qee };
+var Kee = [e8, n8, a8, u8, c8, h8, m8, b8, w8, I8, N8, _8, R8, P8, L8, V8, U8, j8, X8, Q8, tY, uY, cY, pY, yY, xY, SY, OX, TY, RY, PY, WY, GY, qY, KY, YY, JY, n9, a9, o9, l9, p9, f9, y9, x9, I9, N9, $9, R9, P9, B9, U9, q9, j9, X9, Q9, J9, tQ, sQ, oQ, cQ, hQ, mQ, yQ, wQ, CQ, _Q, FX, EQ, AY, FQ, zQ, BQ, zX, GQ, KQ, YQ, eZ, sZ, oZ, cZ, fZ, yZ, wZ, IZ, TZ, _Z, EZ, OZ, zZ, LZ, VZ, UZ, jZ, QZ, t7, l7, WX, h7, g7, v7, k7, fY, C7, T7, _7, R7, P7, LX, M7, L7, mY, a7, W7, q7, Y7, GX, eJ, sJ, oJ, cJ, fJ, gJ, vJ, kJ, SJ, TJ, AJ, FJ, zJ, BJ, UJ, iY, o7, qJ, KJ, YJ, ZJ, eee, nee, ree, iee, uee, dee, hee, mee, yee, xee, kee, See, i7, QX, Tee, Aee, Dee, zee, Bee, ZX, Wee, Gee, jee, N7];
 for (let e of Kee)
-  Al(e);
+  _l(e);
 var zs = X();
 zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
 zs.registerFlag("WEBGPU_CPU_FORWARD", () => true);
@@ -31854,17 +31855,17 @@ function Wt(e) {
     return "vec4<i32>";
   throw Error(`GPU for rank ${e} is not yet supported`);
 }
-function cd(e, t) {
+function ld(e, t) {
   return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
 }
-function kv() {
+function wv() {
   return `
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `;
 }
 function Rr() {
   return `
-  ${kv()}
+  ${wv()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -31909,7 +31910,7 @@ function Yee(e, t, n, s = false) {
   `), s === true)
     return r.push(`
       struct Matrix0 {
-        numbers: array<${cd(t.dtype, n.isVec4)}>;
+        numbers: array<${ld(t.dtype, n.isVec4)}>;
       };
       struct Uniform {
         size            : i32;
@@ -31920,8 +31921,8 @@ function Yee(e, t, n, s = false) {
 
       @group(0) @binding(0) var<storage, write> result : Matrix0;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `), [Cw, r.join(`
-`), Nw(t.shape), n.getUserCode()].join(`
+    `), [Sw, r.join(`
+`), Cw(t.shape), n.getUserCode()].join(`
 `);
   let a = "struct Uniforms { NAN : f32; ";
   n.variableNames.forEach((p, d) => {
@@ -31937,22 +31938,22 @@ function Yee(e, t, n, s = false) {
     @group(0) @binding(0) var<storage, read_write> result : Matrix0;
   `) : r.push(`
     struct Matrix0 {
-        numbers: array<${cd(t.dtype, n.isVec4)}>;
+        numbers: array<${ld(t.dtype, n.isVec4)}>;
     };
 
     @group(0) @binding(0) var<storage, write> result : Matrix0;
   `), n.variableNames.forEach((p, d) => {
     r.push(`
     struct Matrix${1 + d} {
-      numbers: array<${cd(e[d].dtype, n.isVec4)}>;
+      numbers: array<${ld(e[d].dtype, n.isVec4)}>;
     };
     @group(0) @binding(${1 + d}) var<storage, read> ${p} : Matrix${1 + d};
     `);
   }), a !== "" && r.push(`
     @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms : Uniforms;
     `);
-  let [o, u] = nte(t.shape, n.dispatchLayout), l = [Cw, r.join(`
-`), Nw(t.shape), o, Qee(t.shape.length)];
+  let [o, u] = nte(t.shape, n.dispatchLayout), l = [Sw, r.join(`
+`), Cw(t.shape), o, Qee(t.shape.length)];
   if (n.atomic || l.push(Zee(t.shape, t.dtype, n.isVec4)), u === t.shape.length) {
     let p = e.map((d) => Jee(d, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
 `);
@@ -31961,7 +31962,7 @@ function Yee(e, t, n, s = false) {
   return l.push(n.getUserCode()), l.join(`
 `);
 }
-var Cw = `
+var Sw = `
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -31996,20 +31997,17 @@ var Cw = `
     return res;
   }
 
-  fn isNanCustom(val : f32) -> bool {
-    if (val > 0.0) {
-      return false;
-    }
-    if (val < 0.0) {
-      return false;
-    }
-    if (val == 0.0) {
-      return false;
-    }
-    return true;
+  // NaN defination in IEEE 754-1985 is :
+  //   - sign = either 0 or 1.
+  //   - biased exponent = all 1 bits.
+  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
+  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
+  fn isnan(val: f32) -> bool {
+    let floatToUint: u32 = bitcast<u32>(val);
+    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
   }
-  fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
-    return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
+  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
+    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
 `;
 function Qee(e) {
@@ -32052,7 +32050,7 @@ function Qee(e) {
   return t;
 }
 function Zee(e, t, n) {
-  let s = e.length, r = cd(t, n), a;
+  let s = e.length, r = ld(t, n), a;
   if (n ? a = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result.numbers[flatIndex] = ${r}(value);
     }
@@ -32221,7 +32219,7 @@ function nte(e, t) {
   `;
   return l.length === 0 ? p += `return ${c}(0); }` : p += `return ${c}(${l.join(",")}); }`, [p, u];
 }
-function Nw(e) {
+function Cw(e) {
   let t = e.length;
   if (t <= 1)
     return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
@@ -32245,8 +32243,7 @@ function Nw(e) {
   `;
 }
 var i2 = {};
-Ae(i2, { ArrayBufferToTypedArray: () => u2, GPUBytesPerElement: () => qm, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => Iv, computeWorkGroupSizeForMatMul: () => o2, computeWorkPerThreadForConv2d: () => Sv, flatDispatchLayout: () => Be, isWebGPUSupported: () => Cv, tilesFitEvenlyIntoShape: () => Ks });
-var Mi = 65535;
+Ae(i2, { ArrayBufferToTypedArray: () => u2, GPUBytesPerElement: () => Hm, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => kv, computeWorkGroupSizeForMatMul: () => o2, computeWorkPerThreadForConv2d: () => Iv, flatDispatchLayout: () => Be, isWebGPUSupported: () => Sv, tilesFitEvenlyIntoShape: () => Ks });
 var ea = (e) => {
   let t = 1;
   for (let n = 0; n < e.length; n++)
@@ -32259,28 +32256,24 @@ function Ks(e, t) {
   return t.every((n, s) => n % e[s] === 0);
 }
 function _e(e, t, n = [1, 1, 1], s = [1, 1, 1]) {
-  let [r, a, i] = [Math.ceil(ea(e.x.map((u) => t[u])) / (n[0] * s[0])), e.y ? Math.ceil(ea(e.y.map((u) => t[u])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(ea(e.z.map((u) => t[u])) / (n[2] * s[2])) : 1];
-  if (r <= Mi && a <= Mi && i <= Mi)
-    return [r, a, i];
-  w.assert(r > Mi && e.y === void 0 && e.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
-  let o = Math.ceil(Math.sqrt(r));
-  return o > Mi ? (o = Math.ceil(Math.cbrt(r)), w.assert(o <= Mi, () => "Total dispatch size exceeds WebGPU maximum."), [o, o, o]) : [o, o, 1];
+  let [r, a, i] = [Math.ceil(ea(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(ea(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(ea(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
+  return [r, a, i];
 }
-function Iv(e, t) {
+function kv(e, t) {
   let n = ea(e.x.map((r) => t[r])), s = ea(e.y.map((r) => t[r]));
   return n <= 4 ? [4, 16, 1] : s <= 4 ? [16, 4, 1] : [16, 16, 1];
 }
 function o2(e, t, n) {
   return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
 }
-function Sv(e, t) {
+function Iv(e, t) {
   let n = ea(e.x.map((r) => t[r])), s = ea(e.y.map((r) => t[r]));
   return n <= 4 ? [1, 2, 1] : s <= 4 ? [2, 1, 1] : [2, 2, 1];
 }
 function Be(e) {
   return { x: e.map((t, n) => n) };
 }
-function qm(e) {
+function Hm(e) {
   if (e === "float32" || e === "int32" || e === "bool" || e === "string")
     return 4;
   if (e === "complex64")
@@ -32296,7 +32289,7 @@ function u2(e, t) {
     return Uint8Array.from(new Int32Array(e));
   throw new Error(`Unknown dtype ${t}`);
 }
-function Cv() {
+function Sv() {
   return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
 }
 var ste = "return a + b;";
@@ -32320,8 +32313,8 @@ var xte = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
 var wte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`;
 var kte = `
-  if (isNanCustom(a)) { return a; }
-  if (isNanCustom(b)) { return b; }
+  if (isnan(a)) { return a; }
+  if (isnan(b)) { return b; }
   `;
 var l2 = `
   if (isNaN.r) {
@@ -32408,18 +32401,18 @@ var Ate = `
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
   `;
-function Tw(e, t) {
+function Nw(e, t) {
   let n = t ? l2 : kte;
   return t ? `
     var resultTemp = vec4<f32>(${e}(a, b));
-    let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
+    let isNaN = isnanVec4(a) | isnanVec4(b);
     ` + n + `
     return resultTemp;
   ` : n + `
     return ${e}(a, b);
   `;
 }
-function tc(e, t) {
+function ec(e, t) {
   switch (e) {
     case 0:
       return ote;
@@ -32450,9 +32443,9 @@ function tc(e, t) {
     case 14:
       return t ? Ate : _te;
     case 15:
-      return Tw("max", t);
+      return Nw("max", t);
     case 16:
-      return Tw("min", t);
+      return Nw("min", t);
     case 13:
       return t ? $te : Tte;
     case 17:
@@ -32502,7 +32495,7 @@ var jte = "return clamp(a, 0.0, 6.0);";
 var Kte = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
 var Xte = `
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
-  let isNaN = isNanCustomVec4(a);
+  let isNaN = isnanVec4(a);
 
   if (isNaN.r) {
     resFloat.r = a.r;
@@ -32532,7 +32525,7 @@ var nne = `
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
 `;
 var sne = "return f32(i32((a)));";
-function Gi(e, t) {
+function Ui(e, t) {
   switch (e) {
     case 0:
       return Ete;
@@ -32590,17 +32583,17 @@ function Zs(e, t = false) {
   if (e === null)
     return null;
   if (e === "linear")
-    return Gi(8);
+    return Ui(8);
   if (e === "relu")
-    return Gi(13, t);
+    return Ui(13, t);
   if (e === "elu")
-    return Gi(4, t);
+    return Ui(4, t);
   if (e === "relu6")
-    return Gi(14, t);
+    return Ui(14, t);
   if (e === "prelu")
-    return tc(14, t);
+    return ec(14, t);
   if (e === "sigmoid")
-    return Gi(19);
+    return Ui(19);
   throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
 }
 function c2(e, t, n, s) {
@@ -32731,7 +32724,7 @@ var rne = class {
     `;
   }
 };
-function Nv(e, t) {
+function Cv(e, t) {
   let n = t[1] * e[1], s = t[0] * e[0], r = n > s ? n : s;
   return `
     var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
@@ -32934,7 +32927,7 @@ var d2 = class {
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1] > 1 ? Nv([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : ane(this.workGroupSize)}
+      ${this.outputShape[1] > 1 ? Cv([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : ane(this.workGroupSize)}
     `;
   }
 };
@@ -33168,9 +33161,9 @@ function Me(e) {
   let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
   return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
 }
-var cne = { kernelName: Eo, backendName: "webgpu", kernelFunc: Me };
-function Tv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
-  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = jo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
+var cne = { kernelName: Ao, backendName: "webgpu", kernelFunc: Me };
+function Nv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
+  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
   w.assert(p === d, () => `Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);
   let k = n ? [b, p, h] : [b, h, p], C = s ? [y, f, d] : [y, d, f], T = Me({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = Me({ inputs: { x: t }, backend: r, attrs: { shape: C } }), A = [T, E], P = Math.max(b, y), R = p % 4 === 0 && f % 4 === 0 && !n && !s && f >= 32, F;
   h * f <= 32 ? F = new one([P, h, f], n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? F = new lne(k, C, [P, h, f], a, u, i) : R ? F = new rne(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), a, u, i) : F = new d2(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), n, s, a, u, i);
@@ -33184,10 +33177,10 @@ function Tv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = nu
 }
 function dne(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
-  return Tv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
+  return Nv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
 }
 var pne = { kernelName: na, backendName: "webgpu", kernelFunc: dne };
-var $w = class {
+var Tw = class {
   constructor(e, t, n) {
     this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = N.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
   }
@@ -33195,7 +33188,7 @@ var $w = class {
     return `
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
-        ${tc(this.op, false)}
+        ${ec(this.op, false)}
       }
 
       ${Ue()}
@@ -33222,7 +33215,7 @@ var hne = class {
          let b = getBByOutputCoords(coords);`;
     return `
         fn binaryOperation(a : f32, b : f32) -> f32 {
-          ${tc(this.op, false)}
+          ${ec(this.op, false)}
         }
         var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
         ${Ue()}
@@ -33257,7 +33250,7 @@ var fne = class {
   getUserCode() {
     return `
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
-        ${tc(this.op, this.isVec4)}
+        ${ec(this.op, this.isVec4)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -33278,7 +33271,7 @@ var p2 = class {
   getUserCode() {
     return `
       fn binaryOperation(a : f32, b : f32) -> f32 {
-        ${tc(this.op, false)}
+        ${ec(this.op, false)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -33290,7 +33283,7 @@ var p2 = class {
       `;
   }
 };
-function _w(e, t, n) {
+function $w(e, t, n) {
   if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
     return new fne(e, t, n);
   let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
@@ -33301,12 +33294,12 @@ function Zn(e) {
   return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
 }
 var mne = { kernelName: Ma, backendName: "webgpu", kernelFunc: Zn };
-function lu(e) {
+function uu(e) {
   let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = Zn({ inputs: { x: s }, backend: n }), u = Zn({ inputs: { x: r }, backend: n });
   return i.complexTensorInfos = { real: o, imag: u }, a;
 }
-var gne = { kernelName: jd, backendName: "webgpu", kernelFunc: lu };
-var nc = class {
+var gne = { kernelName: qd, backendName: "webgpu", kernelFunc: uu };
+var tc = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.size = true;
     let n = 128;
@@ -33315,7 +33308,7 @@ var nc = class {
   getUserCode() {
     return `
       fn unaryOperation(a : f32) -> f32 {
-        ${Gi(this.op, false)}
+        ${Ui(this.op, false)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -33333,7 +33326,7 @@ function jt({ opType: e, cpuKernelImpl: t, dtype: n }) {
       let l = i.tensorMap.get(a.dataId), c = t(l.values, o);
       return i.makeTensorInfo(a.shape, o, c);
     }
-    let u = new nc(a.shape, e);
+    let u = new tc(a.shape, e);
     return i.runWebGPUProgram(u, [a], o);
   };
 }
@@ -33344,14 +33337,14 @@ function fn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype:
       let p = u.tensorMap.get(i.dataId), d = u.tensorMap.get(o.dataId), h, f;
       if (e !== 0)
         [h, f] = [[p.complexTensorInfos.real, d.complexTensorInfos.real], [p.complexTensorInfos.imag, d.complexTensorInfos.imag]].map((g) => {
-          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: o.shape }, k = _w(e, i.shape, o.shape);
+          let [b, y] = g, v = { dataId: b.dataId, dtype: b.dtype, shape: i.shape }, x = { dataId: y.dataId, dtype: y.dtype, shape: o.shape }, k = $w(e, i.shape, o.shape);
           return u.runWebGPUProgram(k, [v, x], yn(b.dtype, y.dtype));
         });
       else {
-        let g = new $w(17, i.shape, o.shape), b = new $w(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
+        let g = new Tw(17, i.shape, o.shape), b = new Tw(18, i.shape, o.shape), y = [{ dataId: p.complexTensorInfos.real.dataId, dtype: p.complexTensorInfos.real.dtype, shape: i.shape }, { dataId: p.complexTensorInfos.imag.dataId, dtype: p.complexTensorInfos.imag.dtype, shape: i.shape }, { dataId: d.complexTensorInfos.real.dataId, dtype: d.complexTensorInfos.real.dtype, shape: o.shape }, { dataId: d.complexTensorInfos.imag.dataId, dtype: d.complexTensorInfos.imag.dtype, shape: o.shape }];
         h = u.runWebGPUProgram(g, y, "float32"), f = u.runWebGPUProgram(b, y, "float32");
       }
-      let m = lu({ inputs: { real: h, imag: f }, backend: u });
+      let m = uu({ inputs: { real: h, imag: f }, backend: u });
       return u.disposeData(h.dataId), u.disposeData(f.dataId), m;
     }
     let l = s || yn(i.dtype, o.dtype);
@@ -33359,13 +33352,13 @@ function fn({ opSnippet: e, cpuKernelImpl: t, supportsComplex: n = false, dtype:
       let p = u.tensorMap.get(i.dataId).values, d = u.tensorMap.get(o.dataId).values, h = i.dtype === "string" ? N.fromUint8ToStringArray(p) : p, f = i.dtype === "string" ? N.fromUint8ToStringArray(d) : d, [m, g] = t(i.shape, o.shape, h, f, l);
       return u.makeTensorInfo(g, l, m);
     }
-    let c = _w(e, i.shape, o.shape);
+    let c = $w(e, i.shape, o.shape);
     return u.runWebGPUProgram(c, [i, o], l);
   };
 }
-var { addImpl: bne, ceilImpl: yne, concatImpl: vne, equalImpl: xne, expImpl: wne, expm1Impl: kne, floorImpl: Ine, gatherNdImpl: Sne, gatherV2Impl: Cne, greaterEqualImpl: Nne, greaterImpl: Tne, lessEqualImpl: $ne, lessImpl: _ne, logImpl: Ane, maxImpl: Ene, maximumImpl: Rne, minimumImpl: Dne, multiplyImpl: Fne, negImpl: One, notEqualImpl: Pne, prodImpl: zne, rangeImpl: Mne, rsqrtImpl: Lne, simpleAbsImpl: Bne, sliceImpl: Vne, stridedSliceImpl: Wne, stringNGramsImpl: Une, subImpl: Gne, tileImpl: Hne, topKImpl: qne, transposeImpl: jne, uniqueImpl: kpe } = Qy;
+var { addImpl: bne, ceilImpl: yne, concatImpl: vne, equalImpl: xne, expImpl: wne, expm1Impl: kne, floorImpl: Ine, gatherNdImpl: Sne, gatherV2Impl: Cne, greaterEqualImpl: Nne, greaterImpl: Tne, lessEqualImpl: $ne, lessImpl: _ne, logImpl: Ane, maxImpl: Ene, maximumImpl: Rne, minimumImpl: Dne, multiplyImpl: Fne, negImpl: One, notEqualImpl: Pne, prodImpl: zne, rangeImpl: Mne, rsqrtImpl: Lne, simpleAbsImpl: Bne, sliceImpl: Vne, stridedSliceImpl: Wne, stringNGramsImpl: Une, subImpl: Gne, tileImpl: Hne, topKImpl: qne, transposeImpl: jne, uniqueImpl: kpe } = Yy;
 var Kne = jt({ opType: 0, cpuKernelImpl: Bne });
-var Xne = { kernelName: io, backendName: "webgpu", kernelFunc: Kne };
+var Xne = { kernelName: ao, backendName: "webgpu", kernelFunc: Kne };
 var Yne = fn({ opSnippet: 1, cpuKernelImpl: bne, supportsComplex: true });
 var Qne = { kernelName: kr, backendName: "webgpu", kernelFunc: Yne };
 var Zne = class {
@@ -33462,7 +33455,7 @@ var h2 = class {
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
-          if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {
+          if (!isnan(candidate) && candidate ${this.op} bestValue) {
             bestValue = candidate;
             bestIndex = k;
           }
@@ -33506,7 +33499,7 @@ var tse = class {
     return `
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;
-      ${kv()}
+      ${wv()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -33592,7 +33585,7 @@ function ose(e) {
   let c = new h2(u.shape, i[0], "min"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
   return l.forEach((h) => n.disposeData(h.dataId)), d;
 }
-var use = { kernelName: ol, backendName: "webgpu", kernelFunc: ose };
+var use = { kernelName: il, backendName: "webgpu", kernelFunc: ose };
 var f2 = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.uniforms = "stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;", this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `pool2D_${t}`, this.poolType = t;
@@ -33670,7 +33663,7 @@ function lse(e) {
 var cse = { kernelName: ka, backendName: "webgpu", kernelFunc: lse };
 function dse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
-  return Tv({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
+  return Nv({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
 }
 var pse = { kernelName: Ia, backendName: "webgpu", kernelFunc: dse };
 var hse = class {
@@ -33679,7 +33672,7 @@ var hse = class {
   }
   getUserCode() {
     let e = Wt(this.rank), t = fse(this.rank), n;
-    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${jm[a]} = uniforms.start[${a}] + coords.${jm[a]};`), `
+    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${qm[a]} = uniforms.start[${a}] + coords.${qm[a]};`), `
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -33692,15 +33685,15 @@ var hse = class {
     `;
   }
 };
-var jm = ["x", "y", "z", "w", "u", "v"];
+var qm = ["x", "y", "z", "w", "u", "v"];
 function fse(e) {
   if (e === 1)
     return "sourceLoc";
   if (e <= 6)
-    return jm.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
+    return qm.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
   throw Error(`Slicing for rank ${e} is not yet supported`);
 }
-function cu(e) {
+function lu(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
   if (wt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
     let p = n.tensorMap.get(r.dataId), d = Vne(p.values, o, u, r.shape, r.dtype);
@@ -33711,35 +33704,35 @@ function cu(e) {
   let l = new hse(o, u), c = [{ type: "int32", data: o }];
   return n.runWebGPUProgram(l, [r], r.dtype, c);
 }
-var mse = { kernelName: Po, backendName: "webgpu", kernelFunc: cu };
+var mse = { kernelName: Oo, backendName: "webgpu", kernelFunc: lu };
 var gse = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
   w.assert(r.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");
-  let o = a.reduce((y, v) => y * v), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = vi({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = cu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
+  let o = a.reduce((y, v) => y * v), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = vi({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = lu({ inputs: { x: g }, backend: n, attrs: { begin: p, size: d } });
   return h.push(f), h.push(m), h.push(g), h.forEach((y) => n.disposeData(y.dataId)), b;
 };
-var bse = { kernelName: oo, backendName: "webgpu", kernelFunc: gse };
+var bse = { kernelName: io, backendName: "webgpu", kernelFunc: gse };
 var g2 = fn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Pne });
-var yse = { kernelName: So, backendName: "webgpu", kernelFunc: g2 };
-function sc(e) {
+var yse = { kernelName: Io, backendName: "webgpu", kernelFunc: g2 };
+function nc(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
   return Zn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
 }
-var vse = { kernelName: np, backendName: "webgpu", kernelFunc: sc };
+var vse = { kernelName: tp, backendName: "webgpu", kernelFunc: nc };
 function xse(e, t) {
-  let n = new nc(e.shape, 23), s = t.runWebGPUProgram(n, [e], "int32");
+  let n = new tc(e.shape, 23), s = t.runWebGPUProgram(n, [e], "int32");
   return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
 }
-function Km(e) {
+function jm(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
   if (a === "complex64") {
     if (r.dtype === "complex64")
       return Zn({ inputs: { x: r }, backend: n });
-    let i = $t(r.shape), o = Km({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = lu({ inputs: { real: o, imag: i }, backend: n });
+    let i = $t(r.shape), o = jm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = uu({ inputs: { real: o, imag: i }, backend: n });
     return i.dispose(), n.disposeData(o.dataId), u;
   }
   if (r.dtype === "complex64") {
-    let i = sc({ inputs: { input: r }, backend: n }), o = Km({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
+    let i = nc({ inputs: { input: r }, backend: n }), o = jm({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
     return n.disposeData(i.dataId), o;
   }
   if (!w.hasEncodingLoss(r.dtype, a)) {
@@ -33754,7 +33747,7 @@ function Km(e) {
   }
   throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
 }
-var wse = { kernelName: Sa, backendName: "webgpu", kernelFunc: Km };
+var wse = { kernelName: Sa, backendName: "webgpu", kernelFunc: jm };
 var kse = jt({ opType: 1, cpuKernelImpl: yne });
 var Ise = { kernelName: Ca, backendName: "webgpu", kernelFunc: kse };
 var Sse = class {
@@ -33768,7 +33761,7 @@ var Sse = class {
           let value = getAByOutputIndex(index);
           var clampedValue : vec4<f32>;
           for (var i = 0; i < 4; i = i + 1) {
-            if (isNanCustom(value[i])) {
+            if (isnan(value[i])) {
               clampedValue[i] = value[i];
             } else {
               clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
@@ -33790,7 +33783,7 @@ var Cse = class {
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
-          if (isNanCustom(value)) {
+          if (isnan(value)) {
             setOutputAtIndex(index, value);
             return;
           }
@@ -33839,15 +33832,15 @@ var $se = class {
     `;
   }
 };
-function Zp(e) {
+function Qp(e) {
   let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
   return Zn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
 }
-var _se = { kernelName: Zd, backendName: "webgpu", kernelFunc: Zp };
-function Xm(e, t, n) {
+var _se = { kernelName: Qd, backendName: "webgpu", kernelFunc: Qp };
+function Km(e, t, n) {
   let s = e[0].dtype;
   if (s === "complex64") {
-    let h = e.map((y) => sc({ inputs: { input: y }, backend: n })), f = e.map((y) => Zp({ inputs: { input: y }, backend: n })), m = Xm(h, t, n), g = Xm(f, t, n), b = lu({ inputs: { real: m, imag: g }, backend: n });
+    let h = e.map((y) => nc({ inputs: { input: y }, backend: n })), f = e.map((y) => Qp({ inputs: { input: y }, backend: n })), m = Km(h, t, n), g = Km(f, t, n), b = uu({ inputs: { real: m, imag: g }, backend: n });
     return h.forEach((y) => n.disposeData(y.dataId)), f.forEach((y) => n.disposeData(y.dataId)), n.disposeData(m.dataId), n.disposeData(g.dataId), b;
   }
   let r = n.shouldExecuteOnCPU(e);
@@ -33881,9 +33874,9 @@ function b2(e) {
   if (o.length === 1)
     return Zn({ inputs: { x: o[0] }, backend: n });
   let u = o.map((l) => l.shape);
-  return N.assertParamsConsistent(u, a), Xm(o, a, n);
+  return N.assertParamsConsistent(u, a), Km(o, a, n);
 }
-var Ese = { kernelName: uo, backendName: "webgpu", kernelFunc: b2 };
+var Ese = { kernelName: oo, backendName: "webgpu", kernelFunc: b2 };
 var Rse = class {
   constructor(e, t) {
     this.variableNames = ["A"], this.uniforms = `pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
@@ -34052,7 +34045,7 @@ var Dse = class {
 };
 var Fse = class {
   constructor(e, t = false, n = null, s = false) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Iv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Sv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`;
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = kv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Iv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`;
   }
   getShapeFit() {
     let e = this.workGroupSize[1] * this.elementsPerThread[1], t = this.workGroupSize[0] * this.elementsPerThread[0], n = e > t ? e : t;
@@ -34061,7 +34054,7 @@ var Fse = class {
     return [Ks(s, [a, o]), Ks(r, [o, i])];
   }
   getUserCode() {
-    let e = Nv(this.elementsPerThread, this.workGroupSize), t = `
+    let e = Cv(this.elementsPerThread, this.workGroupSize), t = `
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -34203,7 +34196,7 @@ function Pse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluAc
     let b = l ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3];
     h = Me({ inputs: { x: e }, backend: s, attrs: { shape: [1, b, n.inChannels] } }), f = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
   }
-  let m = Tv({ a: h, b: f, transposeA: c, transposeB: p, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
+  let m = Nv({ a: h, b: f, transposeA: c, transposeB: p, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
   return s.disposeData(h.dataId), s.disposeData(f.dataId), s.disposeData(m.dataId), g;
 }
 function zse({ x: e, filter: t, convInfo: n, backend: s, bias: r = null, preluActivationWeights: a = null, leakyreluAlpha: i = 0, activation: o = null }) {
@@ -34243,7 +34236,7 @@ function Mse(e) {
 var Lse = { kernelName: Na, backendName: "webgpu", kernelFunc: Mse };
 var Bse = class {
   constructor(e) {
-    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = Iv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Sv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
+    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.outputShape = e.inShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = kv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Iv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), this.shaderKey = `conv2DDerInputMM_${this.elementsPerThread}`;
   }
   getUserCode() {
     return `
@@ -34299,7 +34292,7 @@ var Bse = class {
       result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${Nv(this.elementsPerThread, this.workGroupSize)}
+    ${Cv(this.elementsPerThread, this.workGroupSize)}
   `;
   }
 };
@@ -34448,7 +34441,7 @@ var Xse = (e) => {
   let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, c = new Kse(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
   return n.runWebGPUProgram(c, [r, a, i], "float32", p);
 };
-var Yse = { kernelName: co, backendName: "webgpu", kernelFunc: Xse };
+var Yse = { kernelName: lo, backendName: "webgpu", kernelFunc: Xse };
 var Qse = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.uniforms = "blockSize : i32;", this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `depthToSpace_${t}`, this.dataFormat = t;
@@ -34496,7 +34489,7 @@ function Zse(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new Qse(f, i);
   return n.runWebGPUProgram(g, [r], r.dtype, m);
 }
-var Jse = { kernelName: po, backendName: "webgpu", kernelFunc: Zse };
+var Jse = { kernelName: co, backendName: "webgpu", kernelFunc: Zse };
 var v2 = class {
   constructor(e, t = false, n = null, s = false) {
     this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
@@ -34518,7 +34511,7 @@ var v2 = class {
     return `
       ${e}
 
-      ${kv()}
+      ${wv()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -34697,9 +34690,9 @@ var sre = class {
   getUserCode() {
     let e = "", t = "0.0";
     this.reduceType === "min" || this.reduceType === "max" ? (e = `
-         if (isNanCustom(candidate)) {
+         if (isnan(candidate)) {
           bestValue = uniforms.NAN;
-         } else if (!isNanCustom(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue)
+         } else if (!isnan(bestValue) && candidate ${this.reduceType === "min" ? "<" : ">"} bestValue)
            {  bestValue = candidate; }`, t = "f32(x.numbers[offset])") : this.reduceType === "sum" || this.reduceType === "mean" ? e = " bestValue = bestValue + candidate; " : this.reduceType === "prod" && (e = " bestValue = bestValue * candidate; ", t = "1.0");
     let n = this.reduceType === "mean" ? "setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));" : "setOutputAtIndex(outputIndex, bestValue);";
     return `
@@ -34749,7 +34742,7 @@ var sre = class {
      `;
   }
 };
-function rc(e, t, n, s, r) {
+function sc(e, t, n, s, r) {
   let a = e.shape.length, i = [], o = w.parseAxisParam(t, e.shape), u = o, l = N.getAxesPermutation(u, a), c = e;
   l != null && (c = vi({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = N.getInnerMostAxes(u.length, a), i.push(c)), N.assertAxesAreInnerMostDims(s, u, a);
   let [p, d] = N.computeOutAndReduceShapes(c.shape, u), h = p;
@@ -34770,16 +34763,16 @@ function rc(e, t, n, s, r) {
         throw new Error(`${s} CPU implementation is not yet supported.`);
     }
   } else {
-    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : cp(e.dtype), x = [{ type: "int32", data: [m] }], k = new sre(y, s), C = r.runWebGPUProgram(k, [c], v, x);
+    let m = w.sizeFromShape(d), b = w.sizeFromShape(c.shape) / m, y = { windowSize: m, inSize: m, batchSize: b, outSize: 1 }, v = s === "mean" ? "float32" : lp(e.dtype), x = [{ type: "int32", data: [m] }], k = new sre(y, s), C = r.runWebGPUProgram(k, [c], v, x);
     i.push(C), f = Me({ inputs: { x: C }, attrs: { shape: h }, backend: r });
   }
   return i.forEach((m) => r.disposeData(m.dataId)), f;
 }
-function $v(e) {
+function Tv(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return rc(r, a, i, "sum", n);
+  return sc(r, a, i, "sum", n);
 }
-var rre = { kernelName: ai, backendName: "webgpu", kernelFunc: $v };
+var rre = { kernelName: ai, backendName: "webgpu", kernelFunc: Tv };
 function are(e) {
   let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = N.decodeEinsumEquation(r, a.length);
   N.checkEinsumDimSizes(i.length, u, a);
@@ -34793,26 +34786,26 @@ function are(e) {
         x.splice(y[k], 0, 1);
       w.arraysEqual(v.shape, x) || (v = Me({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = w2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
     }
-    m < p - 1 && (l[m] >= 0 && (d = $v({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
+    m < p - 1 && (l[m] >= 0 && (d = Tv({ inputs: { x: d }, backend: n, attrs: { axis: l[m] - (i.length - h), keepDims: false } }), f.push(d)), h--);
   }
   for (let m of f)
     m !== d && n.disposeData(m.dataId);
   return d;
 }
-var ire = { kernelName: Qd, backendName: "webgpu", kernelFunc: are };
+var ire = { kernelName: Yd, backendName: "webgpu", kernelFunc: are };
 var ore = jt({ opType: 4 });
 var ure = { kernelName: Ra, backendName: "webgpu", kernelFunc: ore };
 var lre = fn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: xne });
-var cre = { kernelName: ho, backendName: "webgpu", kernelFunc: lre };
+var cre = { kernelName: po, backendName: "webgpu", kernelFunc: lre };
 var k2 = jt({ opType: 5, cpuKernelImpl: wne, dtype: "float32" });
 var dre = { kernelName: Da, backendName: "webgpu", kernelFunc: k2 };
-function Ym(e) {
+function Xm(e) {
   let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
   return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), Me({ inputs: { x: a }, backend: s, attrs: { shape: o } });
 }
-var pre = { kernelName: fo, backendName: "webgpu", kernelFunc: Ym };
+var pre = { kernelName: ho, backendName: "webgpu", kernelFunc: Xm };
 var hre = jt({ opType: 6, cpuKernelImpl: kne });
-var fre = { kernelName: mo, backendName: "webgpu", kernelFunc: hre };
+var fre = { kernelName: fo, backendName: "webgpu", kernelFunc: hre };
 var mre = class {
   constructor(e) {
     this.variableNames = [], this.outputShape = [], this.uniforms = "value : f32;", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "fill";
@@ -34827,7 +34820,7 @@ var mre = class {
   `;
   }
 };
-function du(e) {
+function cu(e) {
   let { backend: t, attrs: n } = e, { shape: s, value: r } = n, { dtype: a } = n;
   if (a = a || w.inferDtype(r), a === "string") {
     let i = w.getArrayFromDType(a, w.sizeFromShape(s));
@@ -34837,7 +34830,7 @@ function du(e) {
     return t.runWebGPUProgram(i, [], a, o);
   }
 }
-var gre = { kernelName: fl, backendName: "webgpu", kernelFunc: du };
+var gre = { kernelName: hl, backendName: "webgpu", kernelFunc: cu };
 var bre = class {
   constructor(e) {
     this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "flipLeftRight";
@@ -34855,7 +34848,7 @@ var bre = class {
     `;
   }
 };
-var yre = { kernelName: go, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+var yre = { kernelName: mo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
   let { image: n } = e, s = t, r = new bre(n.shape);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
@@ -34874,7 +34867,7 @@ var I2 = (e, t, n, s, r, a = false) => {
 function S2(e, t, n, s = "", r = "") {
   return e.shaderKey + "_" + (e.workGroupSize ? e.workGroupSize.join(",") : "") + t.map((i) => i.length).join(",") + n.join(",") + e.variableNames.join(",") + s + r;
 }
-function Aw(e) {
+function _w(e) {
   let { externalImage: t, backend: n, attrs: s, outShape: r, useImport: a } = e, { numChannels: i } = s, o = w.sizeFromShape(r), u = w.computeStrides(r), l = n.makeTensorInfo(r, "int32"), c = n.getFromPixelsProgram(a ? "import" : "copyExternal");
   c.updateOutputShape(r);
   let p = [l.shape], d = [l.dtype, a ? "import" : "copyExternal"], h = S2(c, p, d), f = c.getLayout(n.device), m = n.getAndSavePipeline(h, () => I2(n.device, c, f.pipelineLayout, [], l, true));
@@ -34891,17 +34884,17 @@ function Aw(e) {
     y = c.inputTexture.createView();
   return n.runFromPixelsProgram(c, g.bufferInfo.buffer, f, y, l.dataId), l;
 }
-var Sre = { kernelName: fd, backendName: "webgpu", kernelFunc: Cre };
-var Li;
+var Sre = { kernelName: hd, backendName: "webgpu", kernelFunc: Cre };
+var Mi;
 function Cre(e) {
   let { inputs: t, backend: n, attrs: s } = e, { pixels: r } = t, { numChannels: a } = s;
   if (r == null)
     throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
   let i = typeof HTMLVideoElement != "undefined" && r instanceof HTMLVideoElement, o = typeof HTMLImageElement != "undefined" && r instanceof HTMLImageElement, u = typeof HTMLCanvasElement != "undefined" && r instanceof HTMLCanvasElement || typeof OffscreenCanvas != "undefined" && r instanceof OffscreenCanvas, l = typeof ImageBitmap != "undefined" && r instanceof ImageBitmap, [c, p] = i ? [r.videoWidth, r.videoHeight] : [r.width, r.height], d = [p, c, a];
   if (X().getBool("WEBGPU_USE_IMPORT") && i)
-    return Aw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
-  if ((i || o) && (Li == null && (Li = document.createElement("canvas").getContext("2d")), Li.canvas.width = c, Li.canvas.height = p, Li.drawImage(r, 0, 0, c, p), r = Li.canvas), l || u || i || o)
-    return Aw({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
+    return _w({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
+  if ((i || o) && (Mi == null && (Mi = document.createElement("canvas").getContext("2d")), Mi.canvas.width = c, Mi.canvas.height = p, Mi.drawImage(r, 0, 0, c, p), r = Mi.canvas), l || u || i || o)
+    return _w({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: false });
   let h = r.data, f = h;
   if (a != null && a !== 4) {
     f = new Uint8Array(r.width * r.height * a);
@@ -34990,7 +34983,7 @@ function Dre(e) {
   let f = new Rre(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
   return n.disposeData(d.dataId), n.disposeData(h.dataId), n.disposeData(g.dataId), b;
 }
-var Fre = { kernelName: yo, backendName: "webgpu", kernelFunc: Dre };
+var Fre = { kernelName: bo, backendName: "webgpu", kernelFunc: Dre };
 var Ore = class {
   constructor(e, t) {
     this.variableNames = ["A", "indices"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.slice(), this.aShape = e, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "gather";
@@ -35026,29 +35019,29 @@ function C2(e) {
   let b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
   return p.forEach((y) => n.disposeData(y.dataId)), b;
 }
-var zre = { kernelName: bo, backendName: "webgpu", kernelFunc: C2 };
+var zre = { kernelName: go, backendName: "webgpu", kernelFunc: C2 };
 var Mre = fn({ opSnippet: 5, cpuKernelImpl: Tne, dtype: "bool" });
-var Lre = { kernelName: vo, backendName: "webgpu", kernelFunc: Mre };
+var Lre = { kernelName: yo, backendName: "webgpu", kernelFunc: Mre };
 var Bre = fn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: Nne });
 var Vre = { kernelName: za, backendName: "webgpu", kernelFunc: Bre };
 function Wre(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new nc(r.shape, 15);
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new tc(r.shape, 15);
   return o.uniforms = "alpha : f32;", n.runWebGPUProgram(o, [r], "float32", i);
 }
 var Ure = { kernelName: La, backendName: "webgpu", kernelFunc: Wre };
 var Gre = fn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: _ne });
-var Hre = { kernelName: xo, backendName: "webgpu", kernelFunc: Gre };
+var Hre = { kernelName: vo, backendName: "webgpu", kernelFunc: Gre };
 var qre = fn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: $ne });
-var jre = { kernelName: wo, backendName: "webgpu", kernelFunc: qre };
+var jre = { kernelName: xo, backendName: "webgpu", kernelFunc: qre };
 var Kre = jt({ opType: 9, cpuKernelImpl: Ane });
 var Xre = { kernelName: Ba, backendName: "webgpu", kernelFunc: Kre };
 var Yre = fn({ opSnippet: 9, dtype: "bool" });
-var Qre = { kernelName: ko, backendName: "webgpu", kernelFunc: Yre };
+var Qre = { kernelName: wo, backendName: "webgpu", kernelFunc: Yre };
 var Zre = jt({ opType: 10 });
-var Jre = { kernelName: vl, backendName: "webgpu", kernelFunc: Zre };
+var Jre = { kernelName: yl, backendName: "webgpu", kernelFunc: Zre };
 function N2(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
-  return rc(r, a, i, "max", n);
+  return sc(r, a, i, "max", n);
 }
 var eae = { kernelName: Va, backendName: "webgpu", kernelFunc: N2 };
 var tae = fn({ opSnippet: 15, cpuKernelImpl: Rne });
@@ -35066,12 +35059,12 @@ function sae(e) {
 var rae = { kernelName: Ua, backendName: "webgpu", kernelFunc: sae };
 function aae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
-  return rc(r, i, a, "mean", n);
+  return sc(r, i, a, "mean", n);
 }
 var iae = { kernelName: Ga, backendName: "webgpu", kernelFunc: aae };
 function oae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return rc(r, a, i, "min", n);
+  return sc(r, a, i, "min", n);
 }
 var uae = { kernelName: Ha, backendName: "webgpu", kernelFunc: oae };
 var lae = fn({ opSnippet: 16, cpuKernelImpl: Dne });
@@ -35114,57 +35107,57 @@ function hae(e) {
     let a = n.tensorMap.get(s.dataId), [i, o] = One(a.values, s.shape, s.dtype);
     return n.makeTensorInfo(o, s.dtype, i);
   }
-  let r = new nc(s.shape, 11);
+  let r = new tc(s.shape, 11);
   return n.runWebGPUProgram(r, [s], s.dtype);
 }
-var fae = { kernelName: Io, backendName: "webgpu", kernelFunc: hae };
+var fae = { kernelName: ko, backendName: "webgpu", kernelFunc: hae };
 function mae(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s, l = n.readSync(r.dataId), c = n.readSync(a.dataId), { selectedIndices: p } = xs.nonMaxSuppressionV3Impl(l, c, i, o, u);
   return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
 }
-var gae = { kernelName: Co, backendName: "webgpu", kernelFunc: mae };
+var gae = { kernelName: So, backendName: "webgpu", kernelFunc: mae };
 function bae(e) {
   console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
   let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s, c = n.readSync(r.dataId), p = n.readSync(a.dataId), d = i, h = o, f = u, m = l, { selectedIndices: g, selectedScores: b } = xs.nonMaxSuppressionV5Impl(c, p, d, h, f, m);
   return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
 }
-var yae = { kernelName: No, backendName: "webgpu", kernelFunc: bae };
-function Ld(e) {
+var yae = { kernelName: Co, backendName: "webgpu", kernelFunc: bae };
+function Md(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "complex64") {
-    let r = sc({ inputs: { input: s }, backend: n }), a = Ld({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = lu({ inputs: { real: a, imag: o }, backend: n });
+    let r = nc({ inputs: { input: s }, backend: n }), a = Md({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = uu({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
   } else
-    return du({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
+    return cu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
 }
-var vae = { kernelName: Ho, backendName: "webgpu", kernelFunc: Ld };
+var vae = { kernelName: Go, backendName: "webgpu", kernelFunc: Md };
 function T2(e) {
   let { inputs: t, backend: n } = e, { x: s } = t;
   if (s.dtype === "string")
     throw new Error("onesLike is not supported under string dtype");
   if (s.dtype === "complex64") {
-    let r = sc({ inputs: { input: s }, backend: n }), a = T2({ inputs: { x: r }, backend: n }), i = Zp({ inputs: { input: s }, backend: n }), o = Ld({ inputs: { x: i }, backend: n }), u = lu({ inputs: { real: a, imag: o }, backend: n });
+    let r = nc({ inputs: { input: s }, backend: n }), a = T2({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = uu({ inputs: { real: a, imag: o }, backend: n });
     return n.disposeData(r.dataId), n.disposeData(a.dataId), n.disposeData(i.dataId), n.disposeData(o.dataId), u;
   } else
-    return du({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
+    return cu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
 }
-var xae = { kernelName: To, backendName: "webgpu", kernelFunc: T2 };
+var xae = { kernelName: No, backendName: "webgpu", kernelFunc: T2 };
 function wae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Ym({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return Xm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Ym({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = Xm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
   }), l = b2({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var kae = { kernelName: _o, backendName: "webgpu", kernelFunc: wae };
+var kae = { kernelName: $o, backendName: "webgpu", kernelFunc: wae };
 var Iae = class {
   constructor(e, t) {
     this.variableNames = ["x"], this.uniforms = "constantValue : f32;", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
@@ -35197,7 +35190,7 @@ var $2 = (e) => {
     return Zn({ inputs: { x: r }, backend: n });
   if (w.sizeFromShape(r.shape) === 0) {
     let l = a.map((c, p) => c[0] + r.shape[p] + c[1]);
-    return du({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
+    return cu({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
   }
   let o = [{ type: "float32", data: [i] }];
   a.map((l) => o.push({ type: "int32", data: [l[0], l[1]] }));
@@ -35214,14 +35207,14 @@ function Tae(e) {
 var $ae = { kernelName: Qa, backendName: "webgpu", kernelFunc: Tae };
 function _ae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
-  return rc(r, a, i, "prod", n);
+  return sc(r, a, i, "prod", n);
 }
-var Aae = { kernelName: Ao, backendName: "webgpu", kernelFunc: _ae };
+var Aae = { kernelName: _o, backendName: "webgpu", kernelFunc: _ae };
 var Eae = (e) => {
   let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = Mne(s, r, a, i);
   return t.makeTensorInfo([o.length], i, o);
 };
-var Rae = { kernelName: kl, backendName: "webgpu", kernelFunc: Eae };
+var Rae = { kernelName: wl, backendName: "webgpu", kernelFunc: Eae };
 var _2 = fn({ opSnippet: 3 });
 var Dae = { kernelName: Ea, backendName: "webgpu", kernelFunc: _2 };
 var Fae = jt({ opType: 13 });
@@ -35328,7 +35321,7 @@ function Wae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = a && u > 1 ? 1 : 0, p = a && l > 1 ? 1 : 0, h = [{ type: "float32", data: [c, p] }, { type: "float32", data: [a ? 0.5 : 0] }], f = new Vae(r.shape, u, l, i);
   return n.runWebGPUProgram(f, [r], r.dtype, h);
 }
-var Uae = { kernelName: Sl, backendName: "webgpu", kernelFunc: Wae };
+var Uae = { kernelName: Il, backendName: "webgpu", kernelFunc: Wae };
 var Gae = class {
   constructor(e, t) {
     this.outputShape = [], this.variableNames = ["x"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.uniforms = `centerX : f32; centerY : f32; sinRadians : f32;
@@ -35359,7 +35352,7 @@ var Gae = class {
       `;
   }
 };
-var Hae = { kernelName: qo, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
+var Hae = { kernelName: Ho, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
   let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new Gae(s.shape, a), [l, c] = N.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
   return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
 } };
@@ -35421,10 +35414,10 @@ function Xae(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r, updates: a } = t, { shape: i } = s, { sliceRank: o, numUpdates: u, sliceSize: l, strides: c, outputSize: p } = N.calculateShapes(a, r, i), d = [p / l, l];
   if (p === 0)
     return n.makeTensorInfo(i, r.dtype);
-  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = du({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new Kae(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
+  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = cu({ backend: n, attrs: { shape: d, value: 0, dtype: m } }), b = w.sizeFromShape(f.shape), y = [{ type: "int32", data: [o] }, { type: "int32", data: c }, { type: "int32", data: [b] }], v = new Kae(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(x.dataId), k;
 }
-var Yae = { kernelName: Fo, backendName: "webgpu", kernelFunc: Xae };
+var Yae = { kernelName: Do, backendName: "webgpu", kernelFunc: Xae };
 var Qae = class {
   constructor(e, t, n) {
     this.variableNames = ["c", "a", "b"], this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.cRank = e, this.rank = n, this.shaderKey = "select";
@@ -35460,17 +35453,17 @@ function Zae(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new Qae(s.shape.length, r.shape, r.shape.length);
   return n.runWebGPUProgram(i, [s, r, a], yn(r.dtype, a.dtype));
 }
-var Jae = { kernelName: Oo, backendName: "webgpu", kernelFunc: Zae };
+var Jae = { kernelName: Fo, backendName: "webgpu", kernelFunc: Zae };
 var eie = jt({ opType: 19 });
 var tie = { kernelName: si, backendName: "webgpu", kernelFunc: eie };
 var nie = jt({ opType: 17 });
 var sie = { kernelName: ni, backendName: "webgpu", kernelFunc: nie };
 var rie = jt({ opType: 18 });
-var aie = { kernelName: zo, backendName: "webgpu", kernelFunc: rie };
+var aie = { kernelName: Po, backendName: "webgpu", kernelFunc: rie };
 var A2 = fn({ opSnippet: 2, cpuKernelImpl: Gne, supportsComplex: true });
 var iie = { kernelName: ui, backendName: "webgpu", kernelFunc: A2 };
 function oie(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = N2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = N.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = A2({ inputs: { a: r, b: l }, backend: n }), p = k2({ inputs: { x: c }, backend: n }), d = $v({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = _2({ inputs: { a: p, b: h }, backend: n });
+  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = N2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = N.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = A2({ inputs: { a: r, b: l }, backend: n }), p = k2({ inputs: { x: c }, backend: n }), d = Tv({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = _2({ inputs: { a: p, b: h }, backend: n });
   return n.disposeData(o.dataId), n.disposeData(l.dataId), n.disposeData(c.dataId), n.disposeData(p.dataId), n.disposeData(d.dataId), n.disposeData(h.dataId), f;
 }
 var uie = { kernelName: ii, backendName: "webgpu", kernelFunc: oie };
@@ -35484,7 +35477,7 @@ var lie = (e) => {
   let l = [], c = $2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = N.getReshaped(c.shape, a, o, false), d = N.getPermuted(p.length, a.length, false), h = N.getReshapedPermuted(c.shape, a, o, false), f = Me({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = vi({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: h } });
   return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeData(b.dataId)), g;
 };
-var cie = { kernelName: Mo, backendName: "webgpu", kernelFunc: lie };
+var cie = { kernelName: zo, backendName: "webgpu", kernelFunc: lie };
 var die = class {
   constructor(e, t, n, s, r, a, i = true) {
     this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
@@ -35535,21 +35528,21 @@ function pie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = N.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new die(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: o } });
   return n.disposeData(m.dataId), g;
 }
-var hie = { kernelName: ip, backendName: "webgpu", kernelFunc: pie };
+var hie = { kernelName: ap, backendName: "webgpu", kernelFunc: pie };
 function fie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = s, o = w.parseAxisParam(i, r.shape)[0], u = N.prepareSplitSize(r, a, o), l = r.shape.length, c = new Array(l).fill(0), p = r.shape.slice();
   return u.map((d) => {
     let h = [...p];
     h[o] = d;
-    let f = cu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
+    let f = lu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
     return c[o] += d, f;
   });
 }
-var mie = { kernelName: Lo, backendName: "webgpu", kernelFunc: fie };
+var mie = { kernelName: Mo, backendName: "webgpu", kernelFunc: fie };
 var gie = jt({ opType: 20 });
 var bie = { kernelName: ri, backendName: "webgpu", kernelFunc: gie };
-var yie = { kernelName: _l, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
-  let { x: n } = e, s = t, r = new nc(n.shape, 21);
+var yie = { kernelName: $l, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
+  let { x: n } = e, s = t, r = new tc(n.shape, 21);
   return s.runWebGPUProgram(r, [n], n.dtype);
 } };
 var vie = fn({ opSnippet: 11 });
@@ -35584,7 +35577,7 @@ function kie(e) {
     k = Me({ inputs: { x: r }, backend: n, attrs: { shape: f } });
   else if (g || b) {
     w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
-    let C = wt.computeOutShape(y, v, x), T = cu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: C } });
+    let C = wt.computeOutShape(y, v, x), T = lu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: C } });
     k = Me({ inputs: { x: T }, backend: n, attrs: { shape: f } }), n.disposeData(T.dataId);
   } else if (n.shouldExecuteOnCPU([r])) {
     let T = n.readSync(r.dataId), E = De(r.shape, r.dtype, T), A = Wne(h, E, x, y);
@@ -35595,12 +35588,12 @@ function kie(e) {
   }
   return k;
 }
-var Iie = { kernelName: Bo, backendName: "webgpu", kernelFunc: kie };
+var Iie = { kernelName: Lo, backendName: "webgpu", kernelFunc: kie };
 function Sie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { separator: r, nGramWidths: a, leftPad: i, rightPad: o, padWidth: u, preserveShortSequences: l } = s, { data: c, dataSplits: p } = t, d = n.readSync(c.dataId), h = n.readSync(p.dataId), [f, m] = Une(d, h, r, a, i, o, u, l);
   return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
 }
-var Cie = { kernelName: op, backendName: "webgpu", kernelFunc: Sie };
+var Cie = { kernelName: ip, backendName: "webgpu", kernelFunc: Sie };
 var Nie = jt({ opType: 22 });
 var Tie = { kernelName: li, backendName: "webgpu", kernelFunc: Nie };
 var $ie = class {
@@ -35786,10 +35779,10 @@ var Die = class {
       `;
   }
 };
-function Bi(e, t) {
+function Li(e, t) {
   t !== null && e.disposeData(t.dataId);
 }
-function Ew(e) {
+function Aw(e) {
   let t = 1;
   for (; t < e; )
     t *= 2;
@@ -35804,10 +35797,10 @@ function Fie(e) {
   if (a === 0)
     return o[o.length - 1] = 0, [n.makeTensorInfo(o, r.dtype, []), n.makeTensorInfo(o, "int32", [])];
   if (u === 1)
-    return [r, du({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
-  let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Ew(a), h = Ew(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, C, T) => {
+    return [r, cu({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
+  let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Aw(a), h = Aw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, C, T) => {
     let E = m(), A = new Rie(T), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [C] }], F = f;
-    f = n.runWebGPUProgram(A, E, "int32", R), Bi(n, F);
+    f = n.runWebGPUProgram(A, E, "int32", R), Li(n, F);
   };
   for (let k = 1; k < d; k *= 2) {
     let C = k * 2;
@@ -35816,21 +35809,21 @@ function Fie(e) {
   }
   for (let k = h; k > d; k /= 2) {
     let C = m(), T = new Die([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
-    f = n.runWebGPUProgram(T, C, "int32", A), Bi(n, P);
+    f = n.runWebGPUProgram(T, C, "int32", A), Li(n, P);
     let R = d / 2, F = R * 2;
     for (let $ = R; $ >= 1; $ /= 2)
       g(F, $, f.shape);
   }
   let b = f;
-  f = cu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Bi(n, b);
+  f = lu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Li(n, b);
   let y = C2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
-  Bi(n, p);
+  Li(n, p);
   let v = o.slice(0, -1);
-  v.push(a), b = f, f = Me({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Bi(n, b);
+  v.push(a), b = f, f = Me({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Li(n, b);
   let x = y;
-  return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Bi(n, x), [y, f];
+  return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Li(n, x), [y, f];
 }
-var Oie = { kernelName: Wo, backendName: "webgpu", kernelFunc: Fie };
+var Oie = { kernelName: Vo, backendName: "webgpu", kernelFunc: Fie };
 var Pie = class {
   constructor(e) {
     this.variableNames = ["Image", "Transforms"], this.uniforms = "interpolationModeId : i32; fillModeId : i32; fillValue : f32;", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = "transform";
@@ -35977,7 +35970,7 @@ function zie(e) {
   let x = [{ type: "int32", data: [y] }, { type: "int32", data: [v] }, { type: "float32", data: [u] }];
   return n.runWebGPUProgram(b, [r, a], "float32", x);
 }
-var Mie = { kernelName: Uo, backendName: "webgpu", kernelFunc: zie };
+var Mie = { kernelName: Wo, backendName: "webgpu", kernelFunc: zie };
 function Lie(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
@@ -35989,15 +35982,15 @@ function Lie(e) {
   let f = new Array(u);
   for (let m = 0; m < f.length; m++) {
     d[a] = m;
-    let g = cu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
+    let g = lu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
     f[m] = b, p.push(g);
   }
   return p.forEach((m) => n.disposeData(m.dataId)), f;
 }
-var Bie = { kernelName: Go, backendName: "webgpu", kernelFunc: Lie };
+var Bie = { kernelName: Uo, backendName: "webgpu", kernelFunc: Lie };
 var Vie = [pne, Xne, Qne, ese, ise, use, cse, pse, bse, wse, Ise, Tse, gne, Ese, Lse, Use, Hse, jse, Yse, Jse, tre, ire, ure, cre, dre, pre, fre, gre, yre, Sre, xre, kre, Tre, _re, Ere, Fre, zre, Lre, Vre, mne, _se, Ure, Hre, jre, Xre, Qre, Jre, eae, nae, rae, iae, uae, cae, pae, nre, fae, gae, yae, yse, xae, kae, Sae, Nae, $ae, Aae, Rae, vse, Dae, Oae, zae, cne, Bae, Uae, Hae, jae, Yae, Jae, tie, sie, aie, mse, Iie, Cie, uie, cie, hie, mie, bie, yie, xie, iie, rre, Tie, Eie, Oie, Mie, rse, Bie, vae];
 for (let e of Vie)
-  Al(e);
+  _l(e);
 var Wie = class {
   constructor(e) {
     this.device = e, this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numBytesUsed = 0, this.numBytesAllocated = 0;
@@ -36006,7 +35999,7 @@ var Wie = class {
     return this.acquireBuffer(e, t, true);
   }
   acquireBuffer(e, t, n = false) {
-    let s = Rw(e, t);
+    let s = Ew(e, t);
     if (this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.usedBuffers.has(s) || this.usedBuffers.set(s, []), this.numBytesUsed += e, this.numUsedBuffers++, this.freeBuffers.get(s).length > 0) {
       this.numFreeBuffers--;
       let a = this.freeBuffers.get(s).shift();
@@ -36019,7 +36012,7 @@ var Wie = class {
   releaseBuffer(e, t, n) {
     if (this.freeBuffers.size === 0)
       return;
-    let s = Rw(t, n);
+    let s = Ew(t, n);
     this.freeBuffers.has(s) || this.freeBuffers.set(s, []), this.freeBuffers.get(s).push(e), this.numFreeBuffers++, this.numUsedBuffers--;
     let r = this.usedBuffers.get(s), a = r.indexOf(e);
     if (a < 0)
@@ -36050,7 +36043,7 @@ var Wie = class {
     }), this.freeBuffers = /* @__PURE__ */ new Map(), this.usedBuffers = /* @__PURE__ */ new Map(), this.numUsedBuffers = 0, this.numFreeBuffers = 0, this.numBytesUsed = 0, this.numBytesAllocated = 0;
   }
 };
-function Rw(e, t) {
+function Ew(e, t) {
   return `${e}_${t}`;
 }
 var E2 = class {
@@ -36126,12 +36119,20 @@ var Uie = class extends E2 {
   }
 };
 var Gie = X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
-var R2 = class extends nl {
+var Rw = (e, t) => {
+  let n = e.limits.maxComputeWorkgroupsPerDimension, s = t.dispatchLayout, r = t.dispatch;
+  if (r.every((i) => i <= n))
+    return r;
+  w.assert(r[0] > n && s.y === void 0 && s.z === void 0, () => "Dispatch size exceeds WebGPU limits in Y or Z dimension.");
+  let a = Math.ceil(Math.sqrt(r[0]));
+  return a > n ? (a = Math.ceil(Math.cbrt(r[0])), w.assert(a <= n, () => "Total dispatch size exceeds WebGPU maximum."), [a, a, a]) : [a, a, 1];
+};
+var R2 = class extends tl {
   constructor(e, t = false) {
     super();
-    if (this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !Cv())
+    if (this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !Sv())
       throw new Error("WebGPU is not supported on this device");
-    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new Wie(this.device), this.tensorMap = new Ud(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
+    this.layoutCache = {}, this.pipelineCache = {}, this.device = e, this.queue = e.queue, this.currentCommandEncoder = null, this.currentComputePass = null, this.supportTimeQuery = t, this.bufferManager = new Wie(this.device), this.tensorMap = new Wd(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (this.dummyCanvas = document.createElement("canvas"), this.dummyCanvas.width = 1, this.dummyCanvas.height = 1, this.dummyContext = this.dummyCanvas.getContext("webgpu"), this.dummyContext.configure({ device: e, format: "bgra8unorm" }), document.body.appendChild(this.dummyCanvas));
   }
   nextDataId() {
     return R2.nextDataId++;
@@ -36189,13 +36190,13 @@ var R2 = class extends nl {
   write(e, t, n) {
     if (n === "complex64" && e != null)
       throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
-    let s = { id: this.nextDataId() }, r = w.sizeFromShape(t) * qm(n);
+    let s = { id: this.nextDataId() }, r = w.sizeFromShape(t) * Hm(n);
     return this.tensorMap.set(s, { dtype: n, values: e, bufferInfo: { byteSize: r, usage: this.defaultGpuBufferUsage() }, refCount: 1 }), s;
   }
   move(e, t, n, s, r) {
     if (s === "complex64")
       throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
-    let a = w.sizeFromShape(n) * qm(s);
+    let a = w.sizeFromShape(n) * Hm(s);
     this.tensorMap.set(e, { dtype: s, values: t, bufferInfo: { byteSize: a, usage: this.defaultGpuBufferUsage() }, refCount: r });
   }
   submitQueue() {
@@ -36357,6 +36358,7 @@ var R2 = class extends nl {
       }
       this.uploadToGPU(r.dataId);
     }
+    e.dispatch = Rw(this.device, e);
     let a = [{ type: "float32", data: [NaN] }], i = t.concat(r).map((T) => T.shape), o = "int32";
     i.map((T) => {
       a.push({ type: o, data: T });
@@ -36381,6 +36383,7 @@ var R2 = class extends nl {
     return this.uniformDisposalQueue.push(C), X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), v && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), r;
   }
   runFromPixelsProgram(e, t, n, s, r) {
+    e.dispatch = Rw(this.device, e);
     let a = this.device.createBindGroup({ layout: n.bindGroupLayout, entries: [{ binding: 0, resource: { buffer: t } }, { binding: 1, resource: s }, { binding: 2, resource: { buffer: e.uniform } }] });
     this.ensureCommandEncoderReady();
     let i = this.getComputePass(), o = this.activeTimers != null;
@@ -36402,19 +36405,19 @@ var R2 = class extends nl {
     this.disposed || (this.bufferManager.dispose(), this.fromPixelProgram && this.fromPixelProgram.dispose(), this.fromPixelImportProgram && this.fromPixelImportProgram.dispose(), this.disposed = true);
   }
 };
-var _v = R2;
-_v.nextDataId = 0;
+var $v = R2;
+$v.nextDataId = 0;
 var Hie = {};
-Ae(Hie, { WebGPUBackend: () => _v, webgpu_util: () => i2 });
-Cv() && pp("webgpu", async () => {
+Ae(Hie, { WebGPUBackend: () => $v, webgpu_util: () => i2 });
+Sv() && dp("webgpu", async () => {
   X().set("CHECK_COMPUTATION_FOR_ERRORS", false);
-  let e = { powerPreference: X().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t = await navigator.gpu.requestAdapter(e), n = {}, s = t.features.has("timestamp-query");
-  s ? n = { requiredFeatures: ["timestamp-query"] } : console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");
-  let r = await t.requestDevice(n);
-  return new _v(r, s);
+  let e = { powerPreference: X().get("WEBGPU_USE_LOW_POWER_GPU") ? "low-power" : "high-performance" }, t = await navigator.gpu.requestAdapter(e), n = t.limits, s = {}, r = t.features.has("timestamp-query");
+  s.requiredLimits = { maxComputeWorkgroupStorageSize: n.maxComputeWorkgroupStorageSize, maxComputeWorkgroupsPerDimension: n.maxComputeWorkgroupsPerDimension }, r ? s.requiredFeatures = ["timestamp-query"] : console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");
+  let a = await t.requestDevice(s);
+  return new $v(a, r);
 }, 3);
 var Ct = ((e) => (e[e.float32 = 0] = "float32", e[e.int32 = 1] = "int32", e[e.bool = 2] = "bool", e[e.string = 3] = "string", e[e.complex64 = 4] = "complex64", e))(Ct || {});
-var Jp = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(Jp || {});
+var Zp = ((e) => (e[e.linear = 0] = "linear", e[e.relu = 1] = "relu", e[e.relu6 = 2] = "relu6", e[e.prelu = 3] = "prelu", e[e.leakyrelu = 4] = "leakyrelu", e[e.sigmoid = 5] = "sigmoid", e[e.elu = 6] = "elu", e))(Zp || {});
 var D2;
 function qie(e) {
   D2 = e.wasm.cwrap(na, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
@@ -36430,10 +36433,10 @@ function jie(e) {
       throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);
     f = E.id;
   }
-  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = Jp[c];
+  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = Zp[c];
   if (g == null)
     throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);
-  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, C = new Uint8Array(new Int32Array(r.shape).buffer), T = new Uint8Array(new Int32Array(a.shape).buffer);
+  let b = u ? r.shape[2] : r.shape[1], y = l ? a.shape[1] : a.shape[2], v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)), x = n.makeOutput([...v, b, y], r.dtype), k = n.dataIdMap.get(x.dataId).id, C = new Uint8Array(new Int32Array(r.shape).buffer), T = new Uint8Array(new Int32Array(a.shape).buffer);
   return D2(d, C, r.shape.length, h, T, a.shape.length, u, l, g, f, m, p || 0, k), x;
 }
 var Kie = { kernelName: na, backendName: "wasm", setupFunc: qie, kernelFunc: jie };
@@ -36448,7 +36451,7 @@ function Kt(e, t) {
   }
   return { kernelName: e, backendName: "wasm", setupFunc: s, kernelFunc: r };
 }
-var Xie = Kt(io);
+var Xie = Kt(ao);
 function mn(e, t, n) {
   let s;
   function r(i) {
@@ -36477,22 +36480,22 @@ function Jie(e) {
   return F2(a, r.length, Ct[s.dtype], i), s;
 }
 var eoe = { kernelName: xa, backendName: "wasm", setupFunc: Zie, kernelFunc: Jie };
-function eh(e) {
+function Jp(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype), r = n.typedArrayFromHeap(t);
   return n.typedArrayFromHeap(s).set(r), s;
 }
-var toe = { kernelName: Ma, backendName: "wasm", kernelFunc: eh };
+var toe = { kernelName: Ma, backendName: "wasm", kernelFunc: Jp };
 var O2;
 function noe(e) {
   O2 = e.wasm.cwrap(ci, null, ["number", "array", "number", "number", "number", "array", "number"]);
 }
-function ro(e) {
+function so(e) {
   let { inputs: t, backend: n, attrs: s } = e, [r, a] = roe(t.x.shape, s.perm), i = true;
   for (let f = 0; f < a.length; f++)
     a[f] !== f && (i = false);
   let o = soe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
   if (i) {
-    let f = eh({ inputs: t, backend: n });
+    let f = Jp({ inputs: t, backend: n });
     return f.shape = o, f;
   }
   let l = n.makeOutput(o, u.dtype), c = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(l.dataId).id, d = new Uint8Array(new Int32Array(a).buffer), h = new Uint8Array(new Int32Array(u.shape).buffer);
@@ -36516,14 +36519,14 @@ function roe(e, t) {
   }
   return [n, s];
 }
-var aoe = { kernelName: ci, backendName: "wasm", kernelFunc: ro, setupFunc: noe };
+var aoe = { kernelName: ci, backendName: "wasm", kernelFunc: so, setupFunc: noe };
 function Dr(e, t, n) {
   let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), i = a, o = N.getAxesPermutation(i, r), u = null, l = false;
   if (o != null) {
     let c = new Array(r);
     for (let h = 0; h < c.length; h++)
       c[h] = s[o[h]];
-    i = N.getInnerMostAxes(i.length, r), u = ro({ inputs: { x: e }, attrs: { perm: o }, backend: n });
+    i = N.getInnerMostAxes(i.length, r), u = so({ inputs: { x: e }, attrs: { perm: o }, backend: n });
     let p = n.dataIdMap.get(e.dataId).id;
     n.dataIdMap.get(u.dataId).id !== p && (l = true);
   }
@@ -36531,7 +36534,7 @@ function Dr(e, t, n) {
 }
 var P2;
 function ioe(e) {
-  P2 = e.wasm.cwrap(al, null, ["number, number, number"]);
+  P2 = e.wasm.cwrap(rl, null, ["number, number, number"]);
 }
 function ooe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Dr(i, r, t);
@@ -36552,10 +36555,10 @@ function ooe(e) {
   }
   return y;
 }
-var uoe = { kernelName: al, backendName: "wasm", setupFunc: ioe, kernelFunc: ooe };
+var uoe = { kernelName: rl, backendName: "wasm", setupFunc: ioe, kernelFunc: ooe };
 var z2;
 function loe(e) {
-  z2 = e.wasm.cwrap(il, null, ["number, number, number"]);
+  z2 = e.wasm.cwrap(al, null, ["number, number, number"]);
 }
 function coe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, u = t.dataIdMap.get(i.dataId).id, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Dr(i, r, t);
@@ -36576,7 +36579,7 @@ function coe(e) {
   }
   return y;
 }
-var doe = { kernelName: il, backendName: "wasm", setupFunc: loe, kernelFunc: coe };
+var doe = { kernelName: al, backendName: "wasm", setupFunc: loe, kernelFunc: coe };
 var M2;
 function poe(e) {
   M2 = e.wasm.cwrap(wa, null, ["number", "number", "number", "number", "number"]);
@@ -36609,7 +36612,7 @@ function bn(e) {
   let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a);
   return w.assert(a === w.sizeFromShape(i), () => `new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
 }
-var yoe = { kernelName: Eo, backendName: "wasm", kernelFunc: bn };
+var yoe = { kernelName: Ao, backendName: "wasm", kernelFunc: bn };
 var B2;
 function voe(e) {
   B2 = e.wasm.cwrap(Ia, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
@@ -36618,7 +36621,7 @@ function xoe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
   if (r.dtype !== "float32" || a.dtype !== "float32")
     throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
-  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = jo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
+  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
   w.assert(c === p, () => `Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);
   let x = i ? [g, c, d] : [g, d, c], k = o ? [b, h, p] : [b, p, h], C = bn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), T = bn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = n.dataIdMap.get(C.dataId).id, A = n.dataIdMap.get(T.dataId).id, P = i ? C.shape[2] : C.shape[1], R = o ? T.shape[1] : T.shape[2], F = Math.max(g, b), $ = n.makeOutput([F, P, R], C.dtype), z = n.dataIdMap.get($.dataId).id, W = new Uint8Array(new Int32Array(C.shape).buffer), q = new Uint8Array(new Int32Array(T.shape).buffer);
   return B2(E, W, C.shape.length, A, q, T.shape.length, i, o, z), n.disposeData(C.dataId), n.disposeData(T.dataId), $.shape = v, $;
@@ -36631,7 +36634,7 @@ function ba(e) {
     return t.dtype === "string" ? p.stringBytes = u.slice(f, f + w.sizeFromShape(i)) : r.typedArrayFromHeap(l).set(u.subarray(f, f + w.sizeFromShape(i))), l;
   }
   if (t.dtype === "string") {
-    let f = Fd(u, a, i, t.shape, t.dtype);
+    let f = Dd(u, a, i, t.shape, t.dtype);
     return p.stringBytes = f, l;
   }
   let d = r.typedArrayFromHeap(l), h = t.shape.length;
@@ -36642,7 +36645,7 @@ function ba(e) {
   else if (h === 4)
     Soe(u, c[0], c[1], c[2], d, a, i);
   else {
-    let f = Fd(u, a, i, t.shape, t.dtype);
+    let f = Dd(u, a, i, t.shape, t.dtype);
     d.set(f);
   }
   return l;
@@ -36671,17 +36674,17 @@ function Soe(e, t, n, s, r, a, i) {
         r.set(e.subarray(y, y + i[3]), o), o += i[3];
       }
 }
-var Coe = { kernelName: Po, backendName: "wasm", kernelFunc: ba };
+var Coe = { kernelName: Oo, backendName: "wasm", kernelFunc: ba };
 function Noe(e) {
-  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = bn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = ro({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = bn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
+  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s, o = a.reduce((b, y) => b * y), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = bn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = so({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = bn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
   return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(h.dataId), g;
 }
-var Toe = { kernelName: oo, backendName: "wasm", kernelFunc: Noe };
-function ac(e) {
+var Toe = { kernelName: io, backendName: "wasm", kernelFunc: Noe };
+function rc(e) {
   let { inputs: { x: t }, attrs: { dtype: n }, backend: s } = e, r = s.makeOutput(t.shape, n), a = s.typedArrayFromHeap(t);
   return s.typedArrayFromHeap(r).set(a), r;
 }
-var $oe = { kernelName: Sa, backendName: "wasm", kernelFunc: ac };
+var $oe = { kernelName: Sa, backendName: "wasm", kernelFunc: rc };
 var _oe = Kt(Ca);
 var V2;
 function Aoe(e) {
@@ -36695,7 +36698,7 @@ var Roe = { kernelName: Ir, backendName: "wasm", setupFunc: Aoe, kernelFunc: Eoe
 function W2(e) {
   let { inputs: t, backend: n } = e, s = w.parseAxisParam(e.attrs.axis, t[0].shape)[0], r = N.computeOutShape(t.map((h) => h.shape), s), a = t.filter((h) => w.sizeFromShape(h.shape) > 0);
   if (a.length === 1)
-    return eh({ inputs: { x: a[0] }, backend: n });
+    return Jp({ inputs: { x: a[0] }, backend: n });
   let i = n.makeOutput(r, t[0].dtype);
   if (w.sizeFromShape(r) === 0)
     return i;
@@ -36706,7 +36709,7 @@ function W2(e) {
       return bn({ inputs: { x: v }, backend: n, attrs: { shape: [-1, x] } });
     }), f = h.map((v) => ({ vals: n.readSync(v.dataId), shape: v.shape }));
     r = N.computeOutShape(h.map((v) => v.shape), 1);
-    let m = h[0].shape[0] === 1, g = ev(f, r, t[0].dtype, m), b = N.computeOutShape(a.map((v) => v.shape), s);
+    let m = h[0].shape[0] === 1, g = Jy(f, r, t[0].dtype, m), b = N.computeOutShape(a.map((v) => v.shape), s);
     i.shape = b;
     let y = n.dataIdMap.get(i.dataId);
     return y.stringBytes = N.fromStringArrayToUint8(g), h.forEach((v) => n.disposeData(v.dataId)), i;
@@ -36724,7 +36727,7 @@ function W2(e) {
   }
   return i;
 }
-var Doe = { kernelName: uo, backendName: "wasm", kernelFunc: W2 };
+var Doe = { kernelName: oo, backendName: "wasm", kernelFunc: W2 };
 var U2;
 function Foe(e) {
   U2 = e.wasm.cwrap(Na, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
@@ -36751,24 +36754,24 @@ var Voe = Kt(_a);
 var H2 = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(H2 || {});
 var q2;
 function Woe(e) {
-  q2 = e.wasm.cwrap(co, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+  q2 = e.wasm.cwrap(lo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
 }
 function Uoe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { method: r, extrapolationValue: a, cropSize: i } = s, { image: o, boxes: u, boxInd: l } = n, c = u.shape[0], [p, d] = i, h = [c, p, d, o.shape[3]], f = t.dataIdMap.get(o.dataId), m;
-  o.dtype !== "float32" && (m = ac({ backend: t, inputs: { x: o }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
+  o.dtype !== "float32" && (m = rc({ backend: t, inputs: { x: o }, attrs: { dtype: "float32" } }), f = t.dataIdMap.get(m.dataId));
   let g = f.id, b = t.dataIdMap.get(u.dataId).id, y = t.dataIdMap.get(l.dataId).id, v = t.makeOutput(h, "float32"), x = t.dataIdMap.get(v.dataId).id, k = new Uint8Array(new Int32Array(o.shape).buffer);
   return q2(g, b, y, c, k, p, d, H2[r], a, x), m != null && t.disposeData(m.dataId), v;
 }
-var Goe = { kernelName: co, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
+var Goe = { kernelName: lo, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
 var j2;
 function Hoe(e) {
-  j2 = e.wasm.cwrap(lo, null, ["number", "number", "number", "number", "number", "number"]);
+  j2 = e.wasm.cwrap(uo, null, ["number", "number", "number", "number", "number", "number"]);
 }
 function qoe(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length;
   w.assert(r.dtype === "float32" || r.dtype === "int32", () => `cumsum does not support ${r.dtype} tensors in the WASM backend`);
   let l = N.getAxesPermutation([a], u), c = r;
-  l !== null && (c = ro({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
+  l !== null && (c = so({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
   let p = N.getInnerMostAxes(1, u)[0];
   N.assertAxesAreInnerMostDims("cumsum", [p], u);
   let d = n.makeOutput(c.shape, c.dtype), h = c.shape[p], f = n.dataIdMap.get(c.dataId).id, m = n.dataIdMap.get(d.dataId).id;
@@ -36776,20 +36779,20 @@ function qoe(e) {
   let g = d;
   if (l !== null) {
     let b = N.getUndoAxesPermutation(l);
-    g = ro({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
+    g = so({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
   }
   return g;
 }
-var joe = { kernelName: lo, backendName: "wasm", setupFunc: Hoe, kernelFunc: qoe };
+var joe = { kernelName: uo, backendName: "wasm", setupFunc: Hoe, kernelFunc: qoe };
 var K2;
 function Koe(e) {
-  K2 = e.wasm.cwrap(po, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
+  K2 = e.wasm.cwrap(co, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
 }
 function Xoe(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = t.makeOutput(f, "float32"), b = t.dataIdMap.get(r.dataId).id, y = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), v = new Uint8Array(new Int32Array(f).buffer), x = new Uint8Array(new Int32Array(w.computeStrides(f)).buffer), k = t.dataIdMap.get(m.dataId).id;
   return K2(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
 }
-var Yoe = { kernelName: po, backendName: "wasm", setupFunc: Koe, kernelFunc: Xoe };
+var Yoe = { kernelName: co, backendName: "wasm", setupFunc: Koe, kernelFunc: Xoe };
 var X2;
 function Qoe(e) {
   X2 = e.wasm.cwrap(Aa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
@@ -36804,27 +36807,27 @@ function Zoe(e) {
 var Joe = { kernelName: Aa, backendName: "wasm", setupFunc: Qoe, kernelFunc: Zoe };
 var eue = Kt(Ra);
 var tue = false;
-var nue = mn(ho, tue, "bool");
+var nue = mn(po, tue, "bool");
 var sue = Kt(Da, "float32");
-function Qm(e) {
+function Ym(e) {
   let { inputs: t, attrs: n, backend: s } = e, { input: r } = t, { dim: a } = n, i = r.shape.length, o = r.shape.slice(), u = a;
   return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), bn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
 }
-var rue = { kernelName: fo, backendName: "wasm", kernelFunc: Qm };
+var rue = { kernelName: ho, backendName: "wasm", kernelFunc: Ym };
 function Y2(e) {
   let { attrs: { shape: t, value: n, dtype: s }, backend: r } = e, a = r.makeOutput(t, s);
   return r.typedArrayFromHeap(a).fill(n), a;
 }
-var aue = { kernelName: fl, backendName: "wasm", kernelFunc: Y2 };
+var aue = { kernelName: hl, backendName: "wasm", kernelFunc: Y2 };
 var Q2;
 function iue(e) {
-  Q2 = e.wasm.cwrap(go, null, ["number", "number", "number", "number", "number", "number"]);
+  Q2 = e.wasm.cwrap(mo, null, ["number", "number", "number", "number", "number", "number"]);
 }
 function oue(e) {
   let { inputs: t, backend: n } = e, { image: s } = t, r = n.makeOutput(s.shape, s.dtype), a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, [o, u, l, c] = s.shape;
   return Q2(a, o, u, l, c, i), r;
 }
-var uue = { kernelName: go, backendName: "wasm", kernelFunc: oue, setupFunc: iue };
+var uue = { kernelName: mo, backendName: "wasm", kernelFunc: oue, setupFunc: iue };
 var lue = Kt(Fa);
 var cue = false;
 var due = mn(Oa, cue);
@@ -36845,7 +36848,7 @@ function mue(e) {
   J2 = e.wasm.cwrap(sa, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
 function gue(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = N.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = Jp[h];
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = N.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = Zp[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);
   let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
@@ -36869,7 +36872,7 @@ function yue(e) {
   eN = e.wasm.cwrap(ra, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
 }
 function vue(e) {
-  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = N.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = Jp[h];
+  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = N.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = Zp[h];
   if (g == null)
     throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);
   let b = s.dataIdMap.get(r.dataId).id, y = s.dataIdMap.get(a.dataId).id, v = m.outChannels, x = 0;
@@ -36890,7 +36893,7 @@ function vue(e) {
 var xue = { kernelName: ra, backendName: "wasm", setupFunc: yue, kernelFunc: vue };
 var tN;
 function wue(e) {
-  tN = e.wasm.cwrap(yo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
+  tN = e.wasm.cwrap(bo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
 }
 function kue(e) {
   let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = kk.prepareAndValidate(s, r), l = t.makeOutput(a, s.dtype);
@@ -36899,7 +36902,7 @@ function kue(e) {
   let c = r.shape, p = c[c.length - 1], h = t.dataIdMap.get(s.dataId).id, m = t.dataIdMap.get(r.dataId).id, g = new Uint8Array(new Int32Array(u).buffer), b = t.dataIdMap.get(l.dataId).id;
   return tN(h, Ct[s.dtype], m, i, p, o, g, b), l;
 }
-var Iue = { kernelName: yo, backendName: "wasm", setupFunc: wue, kernelFunc: kue };
+var Iue = { kernelName: bo, backendName: "wasm", setupFunc: wue, kernelFunc: kue };
 var nN;
 function Sue(e) {
   nN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
@@ -36916,9 +36919,9 @@ function Cue(e) {
   let b = d.shape.length - 1, v = t.dataIdMap.get(d.dataId).id, k = t.dataIdMap.get(f.dataId).id, C = t.dataIdMap.get(g.dataId).id, T = new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer), E = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
   return nN(v, Ct[r.dtype], T, b, k, p.batchSize, E, C), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
 }
-var Nue = { kernelName: bo, backendName: "wasm", setupFunc: Sue, kernelFunc: Cue };
+var Nue = { kernelName: go, backendName: "wasm", setupFunc: Sue, kernelFunc: Cue };
 var Tue = false;
-var $ue = mn(vo, Tue, "bool");
+var $ue = mn(yo, Tue, "bool");
 var _ue = false;
 var Aue = mn(za, _ue, "bool");
 var sN;
@@ -36935,12 +36938,12 @@ function Rue(e) {
 }
 var Due = { kernelName: La, backendName: "wasm", setupFunc: Eue, kernelFunc: Rue };
 var Fue = false;
-var Oue = mn(xo, Fue, "bool");
+var Oue = mn(vo, Fue, "bool");
 var Pue = false;
-var zue = mn(wo, Pue, "bool");
+var zue = mn(xo, Pue, "bool");
 var Mue = Kt(Ba);
 var Lue = false;
-var Bue = mn(ko, Lue, "bool");
+var Bue = mn(wo, Lue, "bool");
 var rN;
 function Vue(e) {
   rN = e.wasm.cwrap(Va, null, ["number", "number", "number", "number"]);
@@ -36993,7 +36996,7 @@ function Yue(e) {
   }
   N.assertAxesAreInnerMostDims("mean", f, l.shape.length);
   let [m, g] = N.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = l;
-  l.dtype !== "float32" && (y = ac({ backend: t, inputs: { x: l }, attrs: { dtype: "float32" } }), u = t.dataIdMap.get(y.dataId).id);
+  l.dtype !== "float32" && (y = rc({ backend: t, inputs: { x: l }, attrs: { dtype: "float32" } }), u = t.dataIdMap.get(y.dataId).id);
   let v = t.makeOutput(m, "float32");
   if (w.sizeFromShape(l.shape) !== 0) {
     let x = t.dataIdMap.get(v.dataId).id;
@@ -37044,73 +37047,73 @@ function rle(e) {
 var ale = { kernelName: ja, backendName: "wasm", kernelFunc: rle, setupFunc: sle };
 var ile = true;
 var ole = mn(Ka, ile);
-var ule = Kt(Io);
-function Av(e, t) {
+var ule = Kt(ko);
+function _v(e, t) {
   let n = new Int32Array(e.wasm.HEAPU8.buffer, t, 4), s = n[0], r = n[1], a = n[2], i = n[3];
   return e.wasm._free(t), { pSelectedIndices: s, selectedSize: r, pSelectedScores: a, pValidOutputs: i };
 }
 var cN;
 function lle(e) {
-  cN = e.wasm.cwrap(Co, "number", ["number", "number", "number", "number", "number"]);
+  cN = e.wasm.cwrap(So, "number", ["number", "number", "number", "number", "number"]);
 }
 function cle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = cN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = Av(t, p);
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i } = s, { boxes: o, scores: u } = n, l = t.dataIdMap.get(o.dataId).id, c = t.dataIdMap.get(u.dataId).id, p = cN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = _v(t, p);
   return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
 }
-var dle = { kernelName: Co, backendName: "wasm", setupFunc: lle, kernelFunc: cle };
+var dle = { kernelName: So, backendName: "wasm", setupFunc: lle, kernelFunc: cle };
 var dN;
 function ple(e) {
-  dN = e.wasm.cwrap(wl, "number", ["number", "number", "number", "number", "number", "bool"]);
+  dN = e.wasm.cwrap(xl, "number", ["number", "number", "number", "number", "number", "bool"]);
 }
 function hle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = dN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Av(t, d);
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, padToMaxOutputSize: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = dN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = _v(t, d);
   t.wasm._free(m);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
   return [b, y];
 }
-var fle = { kernelName: wl, backendName: "wasm", setupFunc: ple, kernelFunc: hle };
+var fle = { kernelName: xl, backendName: "wasm", setupFunc: ple, kernelFunc: hle };
 var pN;
 function mle(e) {
-  pN = e.wasm.cwrap(No, "number", ["number", "number", "number", "number", "number", "number"]);
+  pN = e.wasm.cwrap(Co, "number", ["number", "number", "number", "number", "number", "number"]);
 }
 function gle(e) {
-  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = pN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = Av(t, d);
+  let { backend: t, inputs: n, attrs: s } = e, { iouThreshold: r, maxOutputSize: a, scoreThreshold: i, softNmsSigma: o } = s, { boxes: u, scores: l } = n, c = t.dataIdMap.get(u.dataId).id, p = t.dataIdMap.get(l.dataId).id, d = pN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = _v(t, d);
   t.wasm._free(g);
   let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
   return [b, y];
 }
-var ble = { kernelName: No, backendName: "wasm", setupFunc: mle, kernelFunc: gle };
+var ble = { kernelName: Co, backendName: "wasm", setupFunc: mle, kernelFunc: gle };
 var yle = false;
-var vle = mn(So, yle, "bool");
+var vle = mn(Io, yle, "bool");
 var hN;
 function xle(e) {
-  hN = e.wasm.cwrap($o, null, ["number", "number", "number", "number", "number"]);
+  hN = e.wasm.cwrap(To, null, ["number", "number", "number", "number", "number"]);
 }
 function wle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s, u = n.makeOutput([...r.shape, a], "int32"), l = n.dataIdMap.get(u.dataId).id, p = n.dataIdMap.get(r.dataId).id;
   return hN(p, a, i, o, l), u;
 }
-var kle = { kernelName: $o, backendName: "wasm", setupFunc: xle, kernelFunc: wle };
+var kle = { kernelName: To, backendName: "wasm", setupFunc: xle, kernelFunc: wle };
 function Ile(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(1), s;
 }
-var Sle = { kernelName: To, backendName: "wasm", kernelFunc: Ile };
+var Sle = { kernelName: No, backendName: "wasm", kernelFunc: Ile };
 function Cle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
   if (t.length === 1)
-    return Qm({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
+    return Ym({ inputs: { input: t[0] }, backend: n, attrs: { dim: r } });
   let a = t[0].shape, i = t[0].dtype;
   t.forEach((c) => {
     w.assertShapesMatch(a, c.shape, "All tensors passed to stack must have matching shapes"), w.assert(i === c.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   let o = [], u = t.map((c) => {
-    let p = Qm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
+    let p = Ym({ inputs: { input: c }, backend: n, attrs: { dim: r } });
     return o.push(p), p;
   }), l = W2({ inputs: u, backend: n, attrs: { axis: r } });
   return o.forEach((c) => n.disposeData(c.dataId)), l;
 }
-var Nle = { kernelName: _o, backendName: "wasm", kernelFunc: Cle };
+var Nle = { kernelName: $o, backendName: "wasm", kernelFunc: Cle };
 var fN;
 function Tle(e) {
   fN = e.wasm.cwrap(Xa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
@@ -37131,14 +37134,14 @@ function Ele(e) {
 }
 function Rle(e) {
   let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = n.dataIdMap.get(s.dataId).id, i = n.dataIdMap.get(r.dataId).id, o = a, u = s, l = u;
-  u.dtype !== "float32" && (l = ac({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), o = n.dataIdMap.get(l.dataId).id);
+  u.dtype !== "float32" && (l = rc({ backend: n, inputs: { x: s }, attrs: { dtype: "float32" } }), o = n.dataIdMap.get(l.dataId).id);
   let c = n.makeOutput(s.shape, "float32"), p = n.dataIdMap.get(c.dataId).id;
   return gN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
 }
 var Dle = { kernelName: Qa, backendName: "wasm", setupFunc: Ele, kernelFunc: Rle };
 var bN;
 function Fle(e) {
-  bN = e.wasm.cwrap(Ao, null, ["number", "number", "number", "number"]);
+  bN = e.wasm.cwrap(_o, null, ["number", "number", "number", "number"]);
 }
 function Ole(e) {
   let { backend: t, inputs: n, attrs: s } = e, { axis: r, keepDims: a } = s, { x: i } = n, o = t.dataIdMap.get(i.dataId).id, u = o, l = i, { transposed: c, axes: p, originalAxes: d, inputWasTransposed: h } = Dr(i, r, t), f = p;
@@ -37158,12 +37161,12 @@ function Ole(e) {
   }
   return y;
 }
-var Ple = { kernelName: Ao, backendName: "wasm", setupFunc: Fle, kernelFunc: Ole };
+var Ple = { kernelName: _o, backendName: "wasm", setupFunc: Fle, kernelFunc: Ole };
 var zle = (e) => {
-  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = sv(s, r, a, i), u = t.makeOutput([o.length], i);
+  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = nv(s, r, a, i), u = t.makeOutput([o.length], i);
   return t.typedArrayFromHeap(u).set(o), u;
 };
-var Mle = { kernelName: kl, backendName: "wasm", kernelFunc: zle };
+var Mle = { kernelName: wl, backendName: "wasm", kernelFunc: zle };
 var Lle = true;
 var Ble = mn(Ea, Lle);
 var Vle = Kt(Za);
@@ -37174,7 +37177,7 @@ function Ule(e) {
 }
 function Gle(e) {
   let { backend: t, inputs: n, attrs: s } = e, { images: r } = n, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, [c, p, d, h] = r.shape, f = [c, u, l, h], m = t.dataIdMap.get(r.dataId), g;
-  m.dtype !== "float32" && (g = ac({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
+  m.dtype !== "float32" && (g = rc({ backend: t, inputs: { x: r }, attrs: { dtype: "float32" } }), m = t.dataIdMap.get(g.dataId));
   let b = m.id, y = t.makeOutput(f, "float32");
   if (w.sizeFromShape(r.shape) === 0)
     return y;
@@ -37184,32 +37187,32 @@ function Gle(e) {
 var Hle = { kernelName: Ja, backendName: "wasm", setupFunc: Ule, kernelFunc: Gle };
 var vN;
 function qle(e) {
-  vN = e.wasm.cwrap(Ro, null, ["number", "array", "number", "array", "number", "number"]);
+  vN = e.wasm.cwrap(Eo, null, ["number", "array", "number", "array", "number", "number"]);
 }
 function jle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s, i = w.parseAxisParam(a, r.shape);
   if (r.shape.length === 0)
-    return eh({ inputs: { x: r }, backend: n });
+    return Jp({ inputs: { x: r }, backend: n });
   let o = n.makeOutput(r.shape, r.dtype), u = n.dataIdMap.get(r.dataId).id, l = n.dataIdMap.get(o.dataId).id, c = new Uint8Array(new Int32Array(i).buffer), p = new Uint8Array(new Int32Array(r.shape).buffer);
   vN(u, c, i.length, p, r.shape.length, l);
   let d = bn({ inputs: { x: o }, attrs: { shape: r.shape }, backend: n });
   return n.disposeData(o.dataId), d;
 }
-var Kle = { kernelName: Ro, backendName: "wasm", kernelFunc: jle, setupFunc: qle };
+var Kle = { kernelName: Eo, backendName: "wasm", kernelFunc: jle, setupFunc: qle };
 var xN;
 function Xle(e) {
-  xN = e.wasm.cwrap(qo, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+  xN = e.wasm.cwrap(Ho, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
 }
 function Yle(e) {
   let { inputs: t, backend: n, attrs: s } = e, { image: r } = t, { radians: a, fillValue: i, center: o } = s, u = n.makeOutput(r.shape, r.dtype), l = n.dataIdMap.get(r.dataId).id, c = n.dataIdMap.get(u.dataId).id, [p, d, h, f] = r.shape, [m, g] = N.getImageCenter(o, d, h), b = i === 0, y = 255, v = typeof i == "number" ? [i, i, i, b ? 0 : y] : [...i, y], x = new Uint8Array(new Int32Array(v).buffer);
   return xN(l, p, d, h, f, a, m, g, x, v.length, c), u;
 }
-var Qle = { kernelName: qo, backendName: "wasm", kernelFunc: Yle, setupFunc: Xle };
-var Zle = Kt(Do);
+var Qle = { kernelName: Ho, backendName: "wasm", kernelFunc: Yle, setupFunc: Xle };
+var Zle = Kt(Ro);
 var Jle = Kt(ti);
 var wN;
 function ece(e) {
-  wN = e.wasm.cwrap(Fo, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
+  wN = e.wasm.cwrap(Do, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
 }
 function tce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { indices: r, updates: a } = n, { shape: i } = s, o = t.makeOutput(i, a.dtype);
@@ -37218,7 +37221,7 @@ function tce(e) {
   let { sliceRank: u, numUpdates: l, sliceSize: c, strides: p, outputSize: d } = Sk.calculateShapes(a, r, i), f = t.dataIdMap.get(r.dataId).id, g = t.dataIdMap.get(a.dataId).id, b = new Uint8Array(new Int32Array(p).buffer), y = t.dataIdMap.get(o.dataId).id;
   return wN(f, g, Ct[a.dtype], u, l, c, b, d, y), o;
 }
-var nce = { kernelName: Fo, backendName: "wasm", setupFunc: ece, kernelFunc: tce };
+var nce = { kernelName: Do, backendName: "wasm", setupFunc: ece, kernelFunc: tce };
 var kN;
 function sce(e) {
   kN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
@@ -37227,7 +37230,7 @@ function rce(e) {
   let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = n.dataIdMap.get(s.dataId).id, o = n.dataIdMap.get(r.dataId).id, u = n.dataIdMap.get(a.dataId).id, l = n.makeOutput(r.shape, r.dtype), c = n.dataIdMap.get(l.dataId).id, p = s.shape.length, d = r.shape.length, h = p === 0 || p > 1 || d === 1 ? 1 : w.sizeFromShape(r.shape.slice(1));
   return kN(i, o, u, h, c), l;
 }
-var ace = { kernelName: Oo, backendName: "wasm", kernelFunc: rce, setupFunc: sce };
+var ace = { kernelName: Fo, backendName: "wasm", kernelFunc: rce, setupFunc: sce };
 var IN;
 function ice(e) {
   IN = e.wasm.cwrap(si, null, ["number", "number"]);
@@ -37252,10 +37255,10 @@ function hce(e) {
   u.push(...i);
   for (let C = 1 + a.length; C < r.shape.length; ++C)
     u.push([0, 0]);
-  let l = mN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = N.getReshaped(l.shape, a, o, false), p = N.getPermuted(c.length, a.length, false), d = N.getReshapedPermuted(l.shape, a, o, false), m = bn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = ro({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = bn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
+  let l = mN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = N.getReshaped(l.shape, a, o, false), p = N.getPermuted(c.length, a.length, false), d = N.getReshapedPermuted(l.shape, a, o, false), m = bn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = so({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = bn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
   return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
 }
-var fce = { kernelName: Mo, backendName: "wasm", kernelFunc: hce };
+var fce = { kernelName: zo, backendName: "wasm", kernelFunc: hce };
 var CN;
 function mce(e) {
   CN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
@@ -37282,10 +37285,10 @@ function gce(e) {
   let R = f, F = g;
   return E !== c[0] && (R = ba({ inputs: { x: f }, attrs: { begin: 0, size: [E, u] }, backend: t }), F = ba({ inputs: { x: g }, attrs: { begin: 0, size: E }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [R, F, y, x];
 }
-var bce = { kernelName: sp, backendName: "wasm", setupFunc: mce, kernelFunc: gce };
+var bce = { kernelName: np, backendName: "wasm", setupFunc: mce, kernelFunc: gce };
 var NN;
 function yce(e) {
-  NN = e.wasm.cwrap($l, null, ["number", "number", "number", "number", "number", "number", "number"]);
+  NN = e.wasm.cwrap(Tl, null, ["number", "number", "number", "number", "number", "number", "number"]);
 }
 function vce(e) {
   let { backend: t, inputs: n } = e, { inputIndices: s, inputShape: r, newShape: a } = n;
@@ -37329,7 +37332,7 @@ function vce(e) {
     throw t.disposeData(p.dataId), t.disposeData(h.dataId), new Error(y);
   return [p, h];
 }
-var xce = { kernelName: $l, backendName: "wasm", setupFunc: yce, kernelFunc: vce };
+var xce = { kernelName: Tl, backendName: "wasm", setupFunc: yce, kernelFunc: vce };
 var TN;
 function $N(e) {
   TN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
@@ -37368,11 +37371,11 @@ function _N(e, t) {
 function wce(e) {
   return _N(e, true);
 }
-var kce = { kernelName: rp, backendName: "wasm", setupFunc: $N, kernelFunc: wce };
+var kce = { kernelName: sp, backendName: "wasm", setupFunc: $N, kernelFunc: wce };
 function Ice(e) {
   return _N(e, false);
 }
-var Sce = { kernelName: ap, backendName: "wasm", setupFunc: $N, kernelFunc: Ice };
+var Sce = { kernelName: rp, backendName: "wasm", setupFunc: $N, kernelFunc: Ice };
 function Cce(e) {
   let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { numOrSizeSplits: a, axis: i } = n, o = w.parseAxisParam(i, r.shape)[0], u = N.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
   return u.map((p) => {
@@ -37382,9 +37385,9 @@ function Cce(e) {
     return l[o] += p, h;
   });
 }
-var Nce = { kernelName: Lo, backendName: "wasm", kernelFunc: Cce };
+var Nce = { kernelName: Mo, backendName: "wasm", kernelFunc: Cce };
 var Tce = Kt(ri);
-var $ce = Kt(_l);
+var $ce = Kt($l);
 var _ce = true;
 var Ace = mn(oi, _ce);
 var AN;
@@ -37398,7 +37401,7 @@ function Rce(e) {
 var Dce = { kernelName: di, backendName: "wasm", setupFunc: Ece, kernelFunc: Rce };
 var EN;
 function Fce(e) {
-  EN = e.wasm.cwrap(Bo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
+  EN = e.wasm.cwrap(Lo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
 }
 function Oce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
@@ -37414,7 +37417,7 @@ function Oce(e) {
   }
   return k;
 }
-var Pce = { kernelName: Bo, backendName: "wasm", setupFunc: Fce, kernelFunc: Oce };
+var Pce = { kernelName: Lo, backendName: "wasm", setupFunc: Fce, kernelFunc: Oce };
 var zce = true;
 var Mce = mn(ui, zce);
 var RN;
@@ -37440,7 +37443,7 @@ function Bce(e) {
   return y;
 }
 var Vce = { kernelName: ai, backendName: "wasm", setupFunc: Lce, kernelFunc: Bce };
-var Wce = Kt(Vo);
+var Wce = Kt(Bo);
 var Uce = Kt(li);
 var DN;
 function Gce(e) {
@@ -37456,7 +37459,7 @@ function Hce(e) {
 var qce = { kernelName: Sr, backendName: "wasm", setupFunc: Gce, kernelFunc: Hce };
 var FN;
 function jce(e) {
-  FN = e.wasm.cwrap(Wo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
+  FN = e.wasm.cwrap(Vo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
 }
 var Kce = ({ inputs: e, backend: t, attrs: n }) => {
   let { x: s } = e, { k: r, sorted: a } = n, i = t.dataIdMap.get(s.dataId).id, o = new Uint8Array(new Int32Array(s.shape).buffer), u = s.shape.slice();
@@ -37464,10 +37467,10 @@ var Kce = ({ inputs: e, backend: t, attrs: n }) => {
   let l = t.makeOutput(u, s.dtype), c = t.dataIdMap.get(l.dataId).id, p = t.makeOutput(u, "int32"), d = t.dataIdMap.get(p.dataId).id;
   return FN(i, o, s.shape.length, Ct[s.dtype], r, a, c, d), [l, p];
 };
-var Xce = { kernelName: Wo, backendName: "wasm", setupFunc: jce, kernelFunc: Kce };
+var Xce = { kernelName: Vo, backendName: "wasm", setupFunc: jce, kernelFunc: Kce };
 var ON;
 function Yce(e) {
-  ON = e.wasm.cwrap(Uo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
+  ON = e.wasm.cwrap(Wo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
 }
 function Qce(e) {
   let { backend: t, inputs: n, attrs: s } = e, { image: r, transforms: a } = n, { interpolation: i, fillMode: o, fillValue: u, outputShape: l } = s, [c, p, d, h] = r.shape, [f, m] = l != null ? l : [p, d], g = [c, f, m, h], b = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), y = t.makeOutput(g, r.dtype), v = t.dataIdMap.get(y.dataId).id, k = t.dataIdMap.get(r.dataId).id, T = t.dataIdMap.get(a.dataId).id, E = i === "nearest" ? 1 : 2, A;
@@ -37490,7 +37493,7 @@ function Qce(e) {
   }
   return ON(k, T, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, E, A, u, v), y;
 }
-var Zce = { kernelName: Uo, backendName: "wasm", setupFunc: Yce, kernelFunc: Qce };
+var Zce = { kernelName: Wo, backendName: "wasm", setupFunc: Yce, kernelFunc: Qce };
 function Jce(e) {
   let { inputs: t, backend: n, attrs: s } = e, { value: r } = t, { axis: a } = s;
   a < 0 && (a += r.shape.length);
@@ -37503,19 +37506,19 @@ function Jce(e) {
     p[a] = h, c[h] = ba({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
   return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
 }
-var ede = { kernelName: Go, backendName: "wasm", kernelFunc: Jce };
+var ede = { kernelName: Uo, backendName: "wasm", kernelFunc: Jce };
 function tde(e) {
   let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
   return n.typedArrayFromHeap(s).fill(0), s;
 }
-var nde = { kernelName: Ho, backendName: "wasm", kernelFunc: tde };
+var nde = { kernelName: Go, backendName: "wasm", kernelFunc: tde };
 var sde = [Kie, Xie, Qie, eoe, uoe, doe, foe, boe, woe, Toe, $oe, _oe, Roe, Doe, Poe, Loe, Boe, Voe, Goe, joe, Yoe, Joe, eue, nue, sue, rue, aue, uue, lue, due, fue, bue, xue, Iue, Nue, $ue, Aue, toe, Due, Oue, zue, Mue, Bue, Uue, Hue, Kue, Que, ele, nle, ale, ole, ule, dle, fle, ble, vle, kle, Sle, Nle, mN, Ale, Dle, Ple, Mle, Ble, Vle, Wle, yoe, Hle, Kle, Qle, Zle, Jle, nce, ace, uce, lce, Coe, pce, fce, bce, xce, kce, Sce, Nce, Tce, $ce, Ace, Dce, Pce, Mce, Vce, Wce, Uce, qce, Xce, Zce, aoe, ede, nde];
 for (let e of sde)
-  Al(e);
-var Zm = X();
-Zm.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
-Zm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
-  if (Zm.get("IS_NODE"))
+  _l(e);
+var Qm = X();
+Qm.registerFlag("WASM_HAS_SIMD_SUPPORT", async () => WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 10, 9, 1, 7, 0, 65, 0, 253, 15, 26, 11])));
+Qm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
+  if (Qm.get("IS_NODE"))
     return false;
   try {
     return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)), WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 4, 1, 96, 0, 0, 3, 2, 1, 0, 5, 4, 1, 3, 1, 1, 10, 11, 1, 9, 0, 65, 0, 254, 16, 2, 0, 26, 11]));
@@ -37527,10 +37530,10 @@ var Dw = ya(YT());
 var rde = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"
 ");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInit();try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(Module["keepRuntimeAlive"]()){Module["PThread"].setExitStatus(result)}else{Module["__emscripten_thread_exit"](result)}}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else if(e.data.cmd==="processProxyingQueue"){if(Module["_pthread_self"]()){Module["_emscripten_proxy_execute_queue"](e.data.queue)}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}});`;
 var ade = ya(QT());
-var ide = class extends nl {
+var ide = class extends tl {
   constructor(e) {
     super();
-    this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(PN), Jm = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Ud(this, Ss());
+    this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(PN), Zm = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Wd(this, Ss());
   }
   write(e, t, n) {
     let s = { id: this.dataIdNextNumber++ };
@@ -37628,10 +37631,10 @@ function ode(e) {
   }), {});
 }
 function Fw(e, t, n) {
-  if (Bd != null)
-    return Bd;
+  if (Ld != null)
+    return Ld;
   let s = "tfjs-backend-wasm.wasm";
-  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Lu != null && Lu[s] != null ? Lu[s] : n + s;
+  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Mu != null && Mu[s] != null ? Mu[s] : n + s;
 }
 async function ude() {
   let [e, t] = await Promise.all([X().getAsync("WASM_HAS_SIMD_SUPPORT"), X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
@@ -37642,17 +37645,17 @@ async function ude() {
         let l = rde, c = new Blob([l], { type: "application/javascript" });
         return URL.createObjectURL(c);
       }
-      return o.endsWith(".wasm") ? Fw(e, t, Pu != null ? Pu : u) : u + o;
-    }, Ev && (r.instantiateWasm = ode(Fw(e, t, Pu != null ? Pu : "")));
+      return o.endsWith(".wasm") ? Fw(e, t, Ou != null ? Ou : u) : u + o;
+    }, Av && (r.instantiateWasm = ode(Fw(e, t, Ou != null ? Ou : "")));
     let a = false;
     r.onAbort = () => {
-      if (a || Bu)
+      if (a || Lu)
         return;
-      Bu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
+      Lu = true, s({ message: "Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers" });
     };
     let i;
-    t && e && Bd == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Dw.default.toString()], { type: "text/javascript" }), i = (0, Dw.default)(r)) : i = (0, ade.default)(r), i.then((o) => {
-      a = true, Bu = false;
+    t && e && Ld == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Dw.default.toString()], { type: "text/javascript" }), i = (0, Dw.default)(r)) : i = (0, ade.default)(r), i.then((o) => {
+      a = true, Lu = false;
       let u = null;
       o.tfjs = { init: o.cwrap("init", null, []), initWithThreadsCount: o.cwrap("init_with_threads_count", null, ["number"]), getThreadsCount: o.cwrap("get_threads_count", "number", []), registerTensor: o.cwrap("register_tensor", null, ["number", "number", "number"]), disposeData: o.cwrap("dispose_data", u, ["number"]), dispose: o.cwrap("dispose", u, []) }, n({ wasm: o });
     });
@@ -37671,46 +37674,46 @@ function lde(e, t) {
   }
 }
 var cde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
-var Bd = null;
-var Pu = null;
-var Lu = {};
-var Bu = false;
-var Ev = false;
+var Ld = null;
+var Ou = null;
+var Mu = {};
+var Lu = false;
+var Av = false;
 function Ipe(e, t = false) {
-  if (Mk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Bu)
+  if (Mk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Lu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
-  Bd = e, Ev = t;
+  Ld = e, Av = t;
 }
 function Spe(e, t = false) {
-  if (Bu)
+  if (Lu)
     throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");
   if (typeof e == "string")
-    Pu = e;
+    Ou = e;
   else {
-    Lu = e;
-    let n = cde.filter((s) => Lu[s] == null);
+    Mu = e;
+    let n = cde.filter((s) => Mu[s] == null);
     if (n.length > 0)
       throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);
   }
-  Ev = t;
+  Av = t;
 }
 var PN = -1;
-var Jm = -1;
+var Zm = -1;
 function Cpe(e) {
   PN = e;
 }
 function Npe() {
-  if (Jm === -1)
+  if (Zm === -1)
     throw new Error("WASM backend not initialized.");
-  return Jm;
+  return Zm;
 }
 var Tpe = "0.0.0";
 var dde = 2;
-pp("wasm", async () => {
+dp("wasm", async () => {
   let { wasm: e } = await ude();
   return new ide(e);
 }, dde);
-var sr = "3.13.0-20220217";
+var sr = "3.14.0-20220307";
 var $pe = { tfjs: sr, "tfjs-core": sr, "tfjs-data": sr, "tfjs-layers": sr, "tfjs-converter": sr, "tfjs-backend-cpu": sr, "tfjs-backend-webgl": sr, "tfjs-backend-wasm": sr };
 
 // src/image/imagefxshaders.ts
@@ -38422,7 +38425,7 @@ function GLImageFilter() {
 async function histogramEqualization(inputImage) {
   const squeeze = inputImage.shape.length === 4 ? mr(inputImage) : inputImage;
   const channels = Ln(squeeze, 3, 2);
-  const min = [hm(channels[0]), hm(channels[1]), hm(channels[2])];
+  const min = [pm(channels[0]), pm(channels[1]), pm(channels[2])];
   const max = [As(channels[0]), As(channels[1]), As(channels[2])];
   const absMax = await Promise.all(max.map((channel) => channel.data()));
   const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
@@ -38495,7 +38498,7 @@ async function process2(input, config3, getTensor = true) {
       if (input.shape[2] === 3) {
         tensor = On(input, 0);
       } else if (input.shape[2] === 4) {
-        const rgb2 = ib(input, [0, 0, 0], [-1, -1, 3]);
+        const rgb2 = ab(input, [0, 0, 0], [-1, -1, 3]);
         tensor = On(rgb2, 0);
         Re(rgb2);
       }
@@ -38503,7 +38506,7 @@ async function process2(input, config3, getTensor = true) {
       if (input.shape[3] === 3) {
         tensor = lr(input);
       } else if (input.shape[3] === 4) {
-        tensor = kd(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
+        tensor = wd(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
       }
     }
     if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3)
@@ -38646,7 +38649,7 @@ async function process2(input, config3, getTensor = true) {
       }
     }
     if (depth === 4) {
-      const rgb2 = ib(pixels, [0, 0, 0], [-1, -1, 3]);
+      const rgb2 = ab(pixels, [0, 0, 0], [-1, -1, 3]);
       Re(pixels);
       pixels = rgb2;
     }
@@ -38791,7 +38794,7 @@ var Env = class {
       this.webgpu.supported = false;
     }
     try {
-      this.kernels = Zf(Cde()).map((kernel) => kernel.kernelName.toLowerCase());
+      this.kernels = Qf(Cde()).map((kernel) => kernel.kernelName.toLowerCase());
     } catch (e) {
     }
   }
@@ -38855,7 +38858,7 @@ async function loadModel(modelPath) {
 }
 
 // package.json
-var version = "2.6.3";
+var version = "2.6.4";
 
 // src/gear/gear.ts
 var model;
@@ -42542,7 +42545,7 @@ async function load5(config3) {
     log("cached model:", model5["modelUrl"]);
   inputSize = model5.inputs[0].shape ? model5.inputs[0].shape[2] : 0;
   inputSizeT = Ie(inputSize, "int32");
-  anchors = Ki(generateAnchors(inputSize));
+  anchors = ji(generateAnchors(inputSize));
   return model5;
 }
 function decodeBounds(boxOutputs) {
@@ -43130,7 +43133,7 @@ async function max2d(inputs, minScore) {
   const newScore = (await max.data())[0];
   Re([reshaped, max]);
   if (newScore > minScore) {
-    const coordinates = Hu(reshaped, 0);
+    const coordinates = Gu(reshaped, 0);
     const mod = dD(coordinates, width);
     const x = (await mod.data())[0];
     const div = xe(coordinates, Ie(width, "int32"));
@@ -43603,7 +43606,7 @@ async function predict11(image, config3, idx, count2) {
         obj.gender = gender[0] <= 0.5 ? "female" : "male";
         obj.genderScore = Math.min(0.99, confidence);
       }
-      const argmax = Hu(resT.find((t) => t.shape[1] === 100), 1);
+      const argmax = Gu(resT.find((t) => t.shape[1] === 100), 1);
       const age = (await argmax.data())[0];
       Re(argmax);
       const ageT = resT.find((t) => t.shape[1] === 100);
@@ -46691,7 +46694,7 @@ var HandDetector = class {
     __publicField(this, "doubleInputSizeTensor");
     this.model = model18;
     this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
-    this.anchorsTensor = Ki(this.anchors);
+    this.anchorsTensor = ji(this.anchors);
     this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
     this.inputSizeTensor = Qt([this.inputSize, this.inputSize]);
     this.doubleInputSizeTensor = Qt([this.inputSize * 2, this.inputSize * 2]);
@@ -47469,7 +47472,7 @@ async function detectHands(input, config3) {
   t.filtered = Qn(classScores, 1);
   Re(classScores);
   t.max = As(t.filtered, 1);
-  t.argmax = Hu(t.filtered, 1);
+  t.argmax = Gu(t.filtered, 1);
   let id2 = 0;
   t.nms = await ds.nonMaxSuppressionAsync(t.boxes, t.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
   const nms = await t.nms.data();
@@ -48368,7 +48371,7 @@ async function process5(input, background, config3) {
   t.res = model17.execute(t.norm);
   t.squeeze = mr(t.res, 0);
   if (t.squeeze.shape[2] === 2) {
-    t.softmax = ob(t.squeeze);
+    t.softmax = ib(t.squeeze);
     [t.bg, t.fg] = Fs(t.softmax, 2);
     t.expand = On(t.fg, 2);
     t.pad = On(t.expand, 0);
@@ -48608,17 +48611,17 @@ async function register(instance) {
       return;
     }
     try {
-      const ctx = new jf(config2.gl);
-      pp(config2.name, () => new A1(ctx), config2.priority);
+      const ctx = new qf(config2.gl);
+      dp(config2.name, () => new A1(ctx), config2.priority);
     } catch (err) {
       log("error: cannot register WebGL backend:", err);
       return;
     }
     try {
-      const kernels = Zf("webgl");
+      const kernels = Qf("webgl");
       kernels.forEach((kernelConfig) => {
         const newKernelConfig = { ...kernelConfig, backendName: config2.name };
-        Al(newKernelConfig);
+        _l(newKernelConfig);
       });
     } catch (err) {
       log("error: cannot update WebGL backend registration:", err);
@@ -48650,7 +48653,7 @@ function registerCustomOps() {
       backendName: Cde(),
       kernelFunc: (op2) => j(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
     };
-    Al(kernelMod);
+    _l(kernelMod);
     env.kernels.push("mod");
   }
   if (!env.kernels.includes("floormod")) {
@@ -48659,7 +48662,7 @@ function registerCustomOps() {
       backendName: Cde(),
       kernelFunc: (op2) => j(() => Lk(op2.inputs.a / op2.inputs.b) * op2.inputs.b + dD(op2.inputs.a, op2.inputs.b))
     };
-    Al(kernelMod);
+    _l(kernelMod);
     env.kernels.push("floormod");
   }
 }
@@ -48764,9 +48767,9 @@ function fakeOps(kernelNames, config3) {
           log("kernelFunc", kernelName, config3.backend);
       }
     };
-    Al(kernelConfig);
+    _l(kernelConfig);
   }
-  env.kernels = Zf(Cde()).map((kernel) => kernel.kernelName.toLowerCase());
+  env.kernels = Qf(Cde()).map((kernel) => kernel.kernelName.toLowerCase());
 }
 
 // src/util/draw.ts
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index e68a6307..9a7a1abd 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
   "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/tfjs/load.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/iris.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/models.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/util/draw.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendType = ['cpu', 'wasm', 'webgl', 'humangl', 'tensorflow', 'webgpu'];\n\n/** Possible values for `human.warmup` */\nexport type WarmupType = ['' | 'none' | 'face' | 'full' | 'body'];\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `humangl` for browser and `tensorflow` for nodejs\n  */\n  backend: '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu',\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: '' | 'none' | 'face' | 'full' | 'body',\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar AT=Object.create,Vd=Object.defineProperty,ET=Object.getOwnPropertyDescriptor,Ow=Object.getOwnPropertyNames,RT=Object.getPrototypeOf,DT=Object.prototype.hasOwnProperty,FT=e=>Vd(e,\"__esModule\",{value:!0}),zt=(e,t)=>function(){return t||(0,e[Ow(e)[0]])((t={exports:{}}).exports,t),t.exports},Ae=(e,t)=>{for(var n in t)Vd(e,n,{get:t[n],enumerable:!0})},OT=(e,t,n,s)=>{if(t&&typeof t==\"object\"||typeof t==\"function\")for(let r of Ow(t))!DT.call(e,r)&&(n||r!==\"default\")&&Vd(e,r,{get:()=>t[r],enumerable:!(s=ET(t,r))||s.enumerable});return e},ya=(e,t)=>OT(FT(Vd(e!=null?AT(RT(e)):{},\"default\",!t&&e&&e.__esModule?{get:()=>e.default,enumerable:!0}:{value:e,enumerable:!0})),e),PT=zt({\"src/node_modules/long/src/long.js\"(e,t){t.exports=s;var n=null;try{n=new WebAssembly.Instance(new WebAssembly.Module(new 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Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return us().makeTensor(this.values,this.shape,this.dtype)}},us=null,Vi=null,B$=null;function V$(e){us=e}function W$(e){Vi=e}function U$(e){B$=e}var et=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=pt(e),this.strides=ao(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():\"higher\"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Vi.buffer(this.shape,this.dtype,e)}bufferSync(){return Vi.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Hi(this.shape,e,this.dtype===\"complex64\")}arraySync(){return Hi(this.shape,this.dataSync(),this.dtype===\"complex64\")}async data(){this.throwIfDisposed();let e=us().read(this.dataId);if(this.dtype===\"string\"){let t=await e;try{return t.map(n=>md(n))}catch(n){throw new Error(\"Failed to decode the string bytes into utf-8. 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ar(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new D$(this.backendInstance),!0}setupRegisteredKernels(){Zf(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Zf(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof nl)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return em.nextTensorId++}nextVariableId(){return em.nextVariableId++}clone(e){let t=M.runKernel(Ma,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return M.runKernel(Sa,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Qf(e,this.backendName)!=null))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){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Ff(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Ff(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Qf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:C,dtype:T}=x;return this.makeTensorFromDataId(k,C,T)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Ff(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=ex(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&ir(e[0])&&(r=e.map(o=>Rl(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=Uw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new gd(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*Kf(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 gd||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;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*Kf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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4 or rank 1 but got rank ${o.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),ju(i,o,u,c,l,a)}var IE=L({batchNorm4d_:kE});function SE(e,t,n){let s=_(e,\"x\",\"bincount\"),r=_(t,\"weights\",\"bincount\");O(s.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},i={size:n};return M.runKernel(og,a,i)}var Hk=L({bincount_:SE});function CE(e,t){let n=_(e,\"s0\",\"broadcastArgs\",\"int32\"),s=_(t,\"s1\",\"broadcastArgs\",\"int32\");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};mb.className=\"Adam\";Nr(mb);var gb=class extends $r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=Tr(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(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)}};gb.className=\"Adamax\";Nr(gb);var Ip=class extends $r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};bb.className=\"Momentum\";Nr(bb);var yb=class extends $r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),ln(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),ln(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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adagrad(e,t=.1){return new fb(e,t)}},Fi={sgd:Vr.sgd,momentum:Vr.momentum,adadelta:Vr.adadelta,adagrad:Vr.adagrad,rmsprop:Vr.rmsprop,adamax:Vr.adamax,adam:Vr.adam},EO=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function RO(){return new Promise(e=>EO(()=>e()))}var 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function aB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,\"input layer has >1 nodes\"),Cs(x===0,\"input layer has >1 tensors\"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Qo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,C)=>{(x==null||k==null||C==null)&&(x=b.sourceLayer,k=b.nodeIndex,C=b.tensorIndex);let T=x.inboundNodes[k];if(v.indexOf(T)!==-1)throw new ps(`The tensor ${b.name} at layer \"${x.name}\" is part of a cycle.`);if(y.indexOf(T)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(T)===-1&&v.push(T);let E=T.inboundLayers.length;for(let A=0;A<E;A++){let P=T.inputTensors[A],R=T.inboundLayers[A],F=T.nodeIndices[A],$=T.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(T);v.indexOf(T)>=0;)v.splice(v.indexOf(T),1);i.push(T)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],C=b.nodeIndices[x],T=k.inboundNodes[C],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(y+1,E),n[T.id]=T}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],C=a[x.id];return k<C?-1:k>C?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new ps(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${v.name}\". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new ps(`The name \"${b}\" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Ob(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${t0}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=xm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Xr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Au(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=pa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(gn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return gn(r)}runInternalGraph(e,t){t==null&&(t=pa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],C=y[v];n[x.id]=[k,C]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],C=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let T=r[x];if(T.inboundNodes.length<=k){i(m,g);return}let E=T.inboundNodes[k];b.push(E.outputTensors[C])}b.length>0&&m.apply(gn(b),y)}function u(m){let g=m.name,b=fs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!AM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function iB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function n0(e,t){return iB(e,t,\"classWeight\")}async function s0(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Hu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,\"float32\")}else return null}function oB(e,t){return V(e,t)}var uB=32;function r0(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Fx(\"input\",e.inputNames,n),i=Fx(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  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Use LayersModel.compile(modelCompileArgs).\");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=zx(e,this.feedInputNames,this.feedInputShapes,!1,\"input\"),t=zx(t,this.feedOutputNames,r,!1,\"target\"),bB(e,t,null),yB(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error(\"sample weight is not supported yet.\");let u=null;if(s!=null){let l=n0(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await s0(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,\"steps\"),i=[];if(s>0)throw new Fe(\"Verbose mode is not implemented yet.\");if(r!=null)throw new Fe(\"steps mode in testLoop() is not implemented yet\");{let o=km(a,n),u=Qt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=Jr(u,c,p-c),h=Vb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(Ie(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;kx(e,s)>1&&(r+=`_${kx(e.slice(0,n),s)}`),t.push(r)}return 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t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Xr(a),o=Au(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=It(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=It(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return mB(this,e,t,n)}async fitDataset(e,t){return cB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ls(n[0],e),ls(n[1],t),gn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=cm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-cm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss==\"string\")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!=\"string\")throw new Error(\"Serialization of non-string loss is not supported.\");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]==\"string\")e[s]=Ws(n[s]);else throw new Error(\"Serialization of non-string loss is not supported.\")}return e}getMetricIdentifiers(){if(typeof this.metrics==\"string\"||typeof this.metrics==\"function\")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error(\"Loading weight_metrics is not supported yet.\");if(e.loss_weights!=null)throw new Error(\"Loading loss_weights is not supported yet.\");if(e.sample_weight_mode!=null)throw new Error(\"Loading sample_weight_mode is not supported yet.\");let t=Zu(e.optimizer_config),n=fs(t),s;if(typeof e.loss==\"string\")s=qr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>qr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=qr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>qr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=qr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=_n.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await _n.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:xB,generatedBy:`TensorFlow.js tfjs-layers v${t0}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await _n.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=_n.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Ex(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Ex(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className=\"Model\";ae.registerClass(pr);var i0=class extends pr{};i0.className=\"Functional\";ae.registerClass(i0);async function wB(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zu(n),r=fs(s,t);if(e.weightsManifest!=null){let a=await _n.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function kB(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return IB(e,void 0,t)}async function IB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=fs(Zu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");let{modelWeights:l,optimizerWeights:c}=SB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function SB(e,t){let n=_n.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group===\"optimizer\"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Sm=class extends pr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp(\"sequential_\"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Sm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");if(n.inputs.length!==1)throw new U(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");let s=HI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+\"_input\"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. 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Add some layers first.\");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className===\"Merge\")throw new U(\"Legacy serialization format not supported yet.\");r=t}else w.assert(t.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Sm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=fs(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Wb=Sm;Wb.className=\"Sequential\";ae.registerClass(Wb);function cpe(e){return new pr(e)}function dpe(e){return new Wb(e)}function ppe(e,t){return t==null&&(t={}),kB(e,t)}function CB(e){return HI(e)}function hpe(e,t){Pb.registerCallbackConstructor(e,t)}var wn=class extends ae.Serializable{getConfig(){return{}}},o0=class extends wn{apply(e,t=1){return XM(e,t)}};o0.className=\"elu\";ae.registerClass(o0);var u0=class extends wn{apply(e){return cI(e)}};u0.className=\"selu\";ae.registerClass(u0);var l0=class extends wn{apply(e){return Xs(e)}};l0.className=\"relu\";ae.registerClass(l0);var c0=class extends wn{apply(e){return j(()=>bp(6,Xs(e)))}};c0.className=\"relu6\";ae.registerClass(c0);var d0=class extends wn{apply(e){return e}};d0.className=\"linear\";ae.registerClass(d0);var p0=class extends wn{apply(e){return qs(e)}};p0.className=\"sigmoid\";ae.registerClass(p0);var h0=class extends wn{apply(e){return QM(e)}};h0.className=\"hardSigmoid\";ae.registerClass(h0);var f0=class extends wn{apply(e){return Pl(e)}};f0.className=\"softplus\";ae.registerClass(f0);var m0=class extends wn{apply(e){return YM(e)}};m0.className=\"softsign\";ae.registerClass(m0);var g0=class extends wn{apply(e){return qu(e)}};g0.className=\"tanh\";ae.registerClass(g0);var Ub=class extends wn{apply(e,t=-1){return ob(e,t)}};Ub.className=\"softmax\";ae.registerClass(Ub);var b0=class extends wn{apply(e,t=-1){return eI(e,t)}};b0.className=\"logSoftmax\";ae.registerClass(b0);var y0=class extends wn{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};y0.className=\"swish\";ae.registerClass(y0);var v0=class extends wn{apply(e){return j(()=>V(e,qu(Pl(e))))}};v0.className=\"mish\";ae.registerClass(v0);function br(e){return e.getClassName()}function Uf(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,\"activation\")}function yr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Uf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Uf(t)}else return e instanceof wn?e:Uf(e)}function Gb(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var x0=class extends ae.Serializable{},Ul=class extends x0{constructor(e){super();Gb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ye(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ye(V(this.l2,Vl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ul.className=\"L1L2\";ae.registerClass(Ul);function NB(e){return Gb(e),new Ul({l1:e!=null?e.l1:null,l2:0})}function TB(e){return Gb(e),new Ul({l2:e!=null?e.l2:null,l1:0})}var Lx={l1l2:\"L1L2\"};function at(e){return xb(e)}function Bx(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ft(e){if(e==null)return null;if(typeof e==\"string\"){let n={className:e in Lx?Lx[e]:e,config:{}};return Bx(n)}else return e instanceof x0?e:Bx(e)}var Hb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Bn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Hb.className=\"ReLU\";ae.registerClass(Hb);var qb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Kg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};qb.className=\"LeakyReLU\";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes==\"number\")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight(\"alpha\",t,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),nb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:at(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};jb.className=\"PReLU\";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Kb.className=\"ELU\";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Xb.className=\"ThresholdedReLU\";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ub().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Yb.className=\"Softmax\";ae.registerClass(Yb);function Xi(e,t,n){if(typeof e==\"number\")return pa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!HM(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function ms(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+gr([n-t,0]);else if(s===\"same\")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Qb(e,t){return j(()=>(St(t),t===\"channelsFirst\"?qe(e,[0,2,3,1]):e))}function w0(e,t){return j(()=>(St(t),t===\"channelsFirst\"?qe(e,[0,2,3,4,1]):e))}function $B(e,t,n,s=1,r=\"valid\",a,i=1){return j(()=>{if(a==null&&(a=bs()),St(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=qe(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=qk(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ws(o,n)),o})}function Vx(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Qb(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=da.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=qe(u,[0,3,1,2])),u})}function _B(e,t,n,s=[1,1,1],r=\"valid\",a,i){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=w0(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=Kk(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ws(o,n)),a===\"channelsFirst\"&&(o=qe(o,[0,4,1,2,3])),o})}var Zb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Zb.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,\"kernelSize\"),this.strides=Xi(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,St(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!wb(e.kernelSize,\"number\",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Gl=class extends Zb{constructor(e,t){super(e,t);this.kernel=null,Gl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=PI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Vx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$B(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Vx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_B(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=ms(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:at(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},k0=class extends Gl{constructor(e){super(2,e);k0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!wb(e.kernelSize,\"number\",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=k0;zp.className=\"Conv2D\";ae.registerClass(zp);var I0=class extends Gl{constructor(e){super(3,e);I0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=I0;Mp.className=\"Conv3D\";ae.registerClass(Mp);var Jb=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,1]));let g=jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jb.className=\"Conv2DTranspose\";ae.registerClass(Jb);var ey=class extends Mp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,4,1]));let k=qE(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className=\"Conv3DTranspose\";ae.registerClass(ey);var S0=class extends Gl{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=qe(e,[0,2,3,1])),n=JD(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.pointwiseRegularizer=at(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};S0.className=\"SeparableConv\";var ty=class extends S0{constructor(e){super(2,e)}};ty.className=\"SeparableConv2D\";ae.registerClass(ty);var C0=class extends Gl{constructor(e){super(1,e);C0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!wb(e.kernelSize,\"number\",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ny=C0;ny.className=\"Conv1D\";ae.registerClass(ny);var sy=class extends Ge{constructor(e){super(e);typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sy.className=\"Cropping2D\";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,WM(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className=\"UpSampling2D\";ae.registerClass(ry);function AB(e,t,n=[1,1],s=\"valid\",r,a){return j(()=>{r==null&&(r=bs()),St(r);let i=Qb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}var ay=class extends Zb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=AB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=ms(t,this.kernelSize[0],this.padding,this.strides[0]),a=ms(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};ay.className=\"DepthwiseConv2D\";ae.registerClass(ay);function N0(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function T0(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r!=null&&(r=ce(ce(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=On(r,-1)),r=qe(r,l)),s&&(t=Yn(t,0),r!=null&&(r=Yn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],C=ge(Xn(k),k),T=ie(V(v[0],k),V(d[0],C)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,C)));return{output:T,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Qn(c,1)),[p,g,d]})}var $0=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){bm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");bm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=T0((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ye(t,[1,2]),t=Bl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?mm(t,[1,n]):t):this.cell.stateSize>1?[mm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$0.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=fs(s,n);return new e(Object.assign(t,{cell:r}))}},_r=$0;_r.className=\"RNN\";ae.registerClass(_r);var Hl=class extends Ge{},Lp=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className=\"SimpleRNNCell\";ae.registerClass(Lp);var iy=class extends _r{constructor(e){e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};iy.className=\"SimpleRNN\";ae.registerClass(iy);var Bp=class extends Hl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new U(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Ln(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Ln(l,3,l.rank-1),[b,y]=Ln(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className=\"GRUCell\";ae.registerClass(Bp);var oy=class extends _r{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Bp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};oy.className=\"GRU\";ae.registerClass(oy);var ql=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends es{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return Nx(Nx(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Ln(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};ql.className=\"LSTMCell\";ae.registerClass(ql);var uy=class extends _r{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new ql(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className=\"LSTM\";ae.registerClass(uy);var Vp=class extends Hl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){bm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Zr(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(fs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ym(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Ob(t)}};Vp.className=\"StackedRNNCells\";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):WI(t(),n),o=()=>Wl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _0=class extends _r{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=ms(u,s[0],r,a[0],i[0]),p=ms(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};_0.className=\"ConvRNN2D\";var Wp=class extends ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Xi(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Xi(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Un(this.padding),this.dataFormat=a||\"channelsLast\",St(this.dataFormat),this.dilationRate=Xi(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends es{apply(c,p){let d=u.apply([l]),h=zn([l]),f=u.apply([l*2]);return Tb([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,C]=Ln(this.kernel.read(),i,y),[T,E,A,P]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,T,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,C,P,this.padding);let[R,F,$,z]=Ln(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ua(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ua(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Wp.className=\"ConvLSTM2DCell\";ae.registerClass(Wp);var ly=class extends _0{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ly.className=\"ConvLSTM2D\";ae.registerClass(ly);var Up=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wl(()=>WI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className=\"Dropout\";ae.registerClass(Up);var cy=class extends Up{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};cy.className=\"SpatialDropout1D\";ae.registerClass(cy);var dy=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=PI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};dy.className=\"Dense\";ae.registerClass(dy);var py=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return KM(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};py.className=\"Flatten\";ae.registerClass(py);var hy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};hy.className=\"Activation\";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),qM(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"RepeatVector\";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U(\"Can only specifiy one unknown dimension.\");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};my.className=\"Reshape\";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};gy.className=\"Permute\";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return dm(Xu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=dm(Xu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};by.className=\"Masking\";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Xu(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Sp(n,\"int32\"));let s=VI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:at(this.embeddingsRegularizer),activityRegularizer:at(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};yy.className=\"Embedding\";ae.registerClass(yy);var mi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Bl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an Array\");if(!Array.isArray(e))throw new U(\"`inputs` should be an Array\");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:On(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},vy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};vy.className=\"Add\";ae.registerClass(vy);var xy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};xy.className=\"Multiply\";ae.registerClass(xy);var wy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};wy.className=\"Average\";ae.registerClass(wy);var ky=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Tr(t,e[n]);return t})}};ky.className=\"Maximum\";ae.registerClass(ky);var Iy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Iy.className=\"Minimum\";ae.registerClass(Iy);var Sy=class extends mi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}mergeFunction(e){return j(()=>Tb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U(\"A `Concatenate` layer should be called on a list of inputs.\");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an array for Concatenate\");if(!Array.isArray(e))throw new U(\"`inputs` should be an array for Concatenate\");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Xn(e[a]),\"bool\")):t[a].rank<e[a].rank?s.push(On(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return Bk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Sy.className=\"Concatenate\";ae.registerClass(Sy);function Cu(e,t){for(;e<0;)e+=t;return e}function EB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ye(V(e,t),a[0]):o=ye(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=On(o,1)),o})}var Cy=class extends mi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Cu(r,e[a].shape.length)):s=[Cu(this.axes,t.shape.length),Cu(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),EB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cu(this.axes,e.length),Cu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Cy.className=\"Dot\";ae.registerClass(Cy);var Ny=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Wl(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ny.className=\"GaussianNoise\";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Wl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ty.className=\"GaussianDropout\";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ko(zl(n),this.rate);u=Sp(u,\"float32\");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};$y.className=\"AlphaDropout\";ae.registerClass($y);function Ju(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=vE(e,t,n,s,r,a);else if(e.rank===3)i=wE(e,t,n,s,r,a);else if(e.rank===4)i=IE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function RB(e,t,n,s,r=.001){return j(()=>{let a=eb(e,s),i=a.mean,o=a.variance;return[Ju(e,i,o,n,t,r),i,o]})}function DB(e,t,n,s,r=.001){return j(()=>{let a=eb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Ju(e,l,c,d,p,r),i,o]})}function FB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?RB(e,t,n,s,r):DB(e,t,n,s,r)}var _y=class extends Ge{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ht(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=pa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Ju(s,b,y,v,x,this.epsilon)}else return Ju(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=FB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),C=V(ge(k,y),x);b.write(ge(k,C))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};_y.className=\"BatchNormalization\";ae.registerClass(_y);var Ay=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=eb(n,this.axis,!0),u=pa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=cs(i,d),o=cs(o,d),c=cs(c,h),p=cs(p,h),Ju(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className=\"LayerNormalization\";ae.registerClass(Ay);function OB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=bs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n===\"channelsFirst\"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],pi(e,s)})}var Ey=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding==\"number\")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]==\"number\")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>OB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ey.className=\"ZeroPadding2D\";ae.registerClass(Ey);function Gp(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=Qb(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=Jg(e,t,n,o):i=Ug(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}function A0(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=w0(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=aI(e,t,n,o):i=Gk(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,4,1,2,3])),i})}var E0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=ms(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Bl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ry=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Ry.className=\"MaxPooling1D\";ae.registerClass(Ry);var Dy=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Dy.className=\"AveragePooling1D\";ae.registerClass(Dy);var R0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Fy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,\"max\")}};Fy.className=\"MaxPooling2D\";ae.registerClass(Fy);var Oy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,\"avg\")}};Oy.className=\"AveragePooling2D\";ae.registerClass(Oy);var D0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),s=ms(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Py=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,\"max\")}};Py.className=\"MaxPooling3D\";ae.registerClass(Py);var zy=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,\"avg\")}};zy.className=\"AveragePooling3D\";ae.registerClass(zy);var F0=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},My=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return It(n,1)})}};My.className=\"GlobalAveragePooling1D\";ae.registerClass(My);var Ly=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};Ly.className=\"GlobalMaxPooling1D\";ae.registerClass(Ly);var O0=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},By=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?It(n,[1,2]):It(n,[2,3])})}};By.className=\"GlobalAveragePooling2D\";ae.registerClass(By);var Vy=class extends 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s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"lengths\",e,t,n),i=jW(s,a,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jx(e,t,n){let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=!a,o=r===\"prelu\",u=s===\"fusedbatchnorm\",l=I(\"numArgs\",e,t,n);if(a){if(o&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!o&&a&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",e,t,n),p=sd(e,t,n),d=I(\"dataFormat\",e,t,n).toUpperCase(),h=I(\"dilations\",e,t,n),[f,m]=I(\"args\",e,t,n);i&&(m=f,f=void 0);let g=I(\"leakyreluAlpha\",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var XW=(e,t,n)=>{switch(e.op){case\"Conv1D\":{let s=I(\"stride\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilation\",e,t,n);return[qk(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,r,a,i)]}case\"Conv2D\":{let s=I(\"strides\",e,t,n),r=sd(e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[ua(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=jx(e,t,n);return[da.conv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"FusedDepthwiseConv2dNative\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=jx(e,t,n);return[da.depthwiseConv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let s=I(\"outputShape\",e,t,n),r=I(\"strides\",e,t,n),a=sd(e,t,n);return[jk(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,[r[1],r[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let s=I(\"strides\",e,t,n),r=sd(e,t,n),a=I(\"dilations\",e,t,n),i=I(\"dataFormat\",e,t,n).toUpperCase();return[hp(I(\"input\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,i,[a[1],a[2]])]}case\"Conv3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[Kk(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2],s[3]],r,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Ug(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Jg(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPoolWithArgmax\":{let 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s=I(\"logits\",e,t,n),r=I(\"numSamples\",e,t,n),a=I(\"seed\",e,t,n);return[gD(s,r,a)]}case\"OneHot\":{let s=I(\"indices\",e,t,n),r=I(\"depth\",e,t,n),a=I(\"onValue\",e,t,n),i=I(\"offValue\",e,t,n);return[vd(s,r,a,i)]}case\"Ones\":return[zn(I(\"shape\",e,t,n),I(\"dtype\",e,t,n))];case\"OnesLike\":return[Xn(I(\"x\",e,t,n))];case\"RandomUniform\":return[zl(I(\"shape\",e,t,n),I(\"minval\",e,t,n),I(\"maxval\",e,t,n),I(\"dtype\",e,t,n))];case\"Range\":{let s=I(\"start\",e,t,n),r=I(\"stop\",e,t,n),a=I(\"step\",e,t,n);return[Yu(s,r,a,I(\"dtype\",e,t,n))]}case\"TruncatedNormal\":{let s=I(\"shape\",e,t,n),r=I(\"mean\",e,t,n),a=I(\"stdDev\",e,t,n),i=I(\"seed\",e,t,n);return[cb(s,r,a,I(\"dtype\",e,t,n),i)]}case\"Zeros\":return[$t(I(\"shape\",e,t,n),I(\"dtype\",e,t,n))];case\"ZerosLike\":return[je(I(\"x\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Gf(e,t,n){let s=I(\"boxes\",e,t,n),r=I(\"scores\",e,t,n),a=I(\"maxOutputSize\",e,t,n),i=I(\"iouThreshold\",e,t,n),o=I(\"scoreThreshold\",e,t,n),u=I(\"softNmsSigma\",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}}var QW=async(e,t,n)=>{switch(e.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}=Gf(e,t,n),l=await ds.nonMaxSuppressionWithScoreAsync(s,r,a,i,o,u);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o}=Gf(e,t,n),u=I(\"padToMaxOutputSize\",e,t,n),l=await ds.nonMaxSuppressionPaddedAsync(s,r,a,i,o,u);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o}=Gf(e,t,n);return[await ds.nonMaxSuppressionAsync(s,r,a,i,o)]}case\"Where\":{let s=ce(I(\"condition\",e,t,n),\"bool\"),r=[await 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r=I(\"x\",e,t,n);return[Us(r)];case\"Shape\":return[Qt(I(\"x\",e,t,n).shape,\"int32\")];case\"ShapeN\":return I(\"x\",e,t,n).map(l=>Qt(l.shape));case\"Size\":return[Ie(I(\"x\",e,t,n).size,\"int32\")];case\"Rank\":return[Ie(I(\"x\",e,t,n).rank,\"int32\")];case\"NoOp\":return[Ie(1)];case\"Print\":let a=I(\"x\",e,t,n),i=I(\"data\",e,t,n),o=I(\"message\",e,t,n),u=I(\"summarize\",e,t,n);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(o);for(let l=0;l<i.length;l++)console.log(Array.prototype.slice.call(i[l].dataSync()).slice(0,u));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},e4=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ie(0),this.tensorMap=new Map,Ht(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ie(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),j(()=>{let s=Fs(t),r=n.length,a=s.length;w.assert(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let i=0;i<r;i++){let o=n[i],u=s[i];Ht(u),this.tensorMap.set(o,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return j(()=>{let s=[];for(let r=0;r<n.length;r++){let a=n[r],i=this.findWithDefault(a,t);s.push(i)}return Qn(s)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},t4=async(e,t,n,s)=>{switch(e.op){case\"HashTable\":case\"HashTableV2\":{let r=I(\"keyDType\",e,t,n),a=I(\"valueDType\",e,t,n),i=new e4(r,a);return s.addHashTable(e.name,i),[i.handle]}case\"LookupTableImport\":case\"LookupTableImportV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"values\",e,t,n);return[await s.getHashTableById(r.id).import(a,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"defaultValue\",e,t,n);return[await s.getHashTableById(r.id).find(a,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let r=I(\"tableHandle\",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},n4=(e,t,n)=>{switch(e.op){case\"ResizeBilinear\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[ds.resizeBilinear(s,[r[0],r[1]],a,i)]}case\"ResizeNearestNeighbor\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[ds.resizeNearestNeighbor(s,[r[0],r[1]],a,i)]}case\"CropAndResize\":{let s=I(\"image\",e,t,n),r=I(\"boxes\",e,t,n),a=I(\"boxInd\",e,t,n),i=I(\"cropSize\",e,t,n),o=I(\"method\",e,t,n),u=I(\"extrapolationValue\",e,t,n);return[ds.cropAndResize(s,r,a,i,o,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},s4=(e,t,n)=>{switch(e.op){case\"Equal\":return[qn(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"NotEqual\":return[Xu(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Greater\":return[Wn(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"GreaterEqual\":return[Ko(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Less\":return[Jk(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LessEqual\":return[Xo(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LogicalAnd\":return[Ds(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LogicalNot\":return[Zg(I(\"a\",e,t,n))];case\"LogicalOr\":return[rI(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Select\":case\"SelectV2\":return[vn(I(\"condition\",e,t,n),I(\"a\",e,t,n),I(\"b\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},r4=(e,t,n)=>{switch(e.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[We(I(\"a\",e,t,n),I(\"b\",e,t,n),I(\"transposeA\",e,t,n),I(\"transposeB\",e,t,n))];case\"Einsum\":return[dR(I(\"equation\",e,t,n),...I(\"tensors\",e,t,n))];case\"Transpose\":return[qe(I(\"x\",e,t,n),I(\"perm\",e,t,n))];case\"_FusedMatMul\":let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=r===\"prelu\",o=I(\"numArgs\",e,t,n),u=I(\"leakyreluAlpha\",e,t,n);if(a){if(i&&o!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&o!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[l,c]=I(\"args\",e,t,n);return[da.matMul({a:I(\"a\",e,t,n),b:I(\"b\",e,t,n),transposeA:I(\"transposeA\",e,t,n),transposeB:I(\"transposeB\",e,t,n),bias:l,activation:r,preluActivationWeights:c,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a4=(e,t,n)=>{switch(e.op){case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[ju(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"FusedBatchNormV3\":return[ju(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"LRN\":return[OR(I(\"x\",e,t,n),I(\"radius\",e,t,n),I(\"bias\",e,t,n),I(\"alpha\",e,t,n),I(\"beta\",e,t,n))];case\"Softmax\":return[ob(I(\"x\",e,t,n))];case\"LogSoftmax\":return[eI(I(\"x\",e,t,n))];case\"SparseToDense\":return[xI(I(\"sparseIndices\",e,t,n),I(\"outputShape\",e,t,n),I(\"sparseValues\",e,t,n),I(\"defaultValue\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i4=(e,t,n)=>{switch(e.op){case\"Max\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[As(I(\"x\",e,t,n),i,o)]}case\"Mean\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[It(I(\"x\",e,t,n),i,o)]}case\"Min\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[hm(I(\"x\",e,t,n),i,o)]}case\"Sum\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[ye(I(\"x\",e,t,n),i,o)]}case\"All\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[Bk(I(\"x\",e,t,n),i,o)]}case\"Any\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[dm(I(\"x\",e,t,n),i,o)]}case\"ArgMax\":{let i=I(\"axis\",e,t,n);return[Hu(I(\"x\",e,t,n),i)]}case\"ArgMin\":{let i=I(\"axis\",e,t,n);return[GA(I(\"x\",e,t,n),i)]}case\"Prod\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[iI(I(\"x\",e,t,n),i,o)]}case\"Cumsum\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[Qk(I(\"x\",e,t,n),i,o,u)]}case\"Bincount\":let s=I(\"x\",e,t,n),r=I(\"weights\",e,t,n),a=I(\"size\",e,t,n);return[Hk(s,r,a)];case\"DenseBincount\":{let i=I(\"x\",e,t,n),o=I(\"weights\",e,t,n),u=I(\"size\",e,t,n),l=I(\"binaryOutput\",e,t,n);return[QE(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},o4=(e,t,n)=>{switch(e.op){case\"ConcatV2\":case\"Concat\":{let s=I(\"n\",e,t,n),r=I(\"axis\",e,t,n),a=I(\"tensors\",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case\"Gather\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[Ku(s,ce(r,\"int32\"),0)]}case\"GatherV2\":{let s=I(\"axis\",e,t,n),r=I(\"batchDims\",e,t,n),a=I(\"x\",e,t,n),i=I(\"indices\",e,t,n);return[Ku(a,ce(i,\"int32\"),s,r)]}case\"Reverse\":{let s=I(\"dims\",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I(\"x\",e,t,n);return[Yn(a,r)]}case\"ReverseV2\":{let s=I(\"axis\",e,t,n),r=I(\"x\",e,t,n);return[Yn(r,s)]}case\"Slice\":{let s=I(\"begin\",e,t,n),r=I(\"size\",e,t,n);return[He(I(\"x\",e,t,n),s,r)]}case\"StridedSlice\":{let s=I(\"begin\",e,t,n),r=I(\"end\",e,t,n),a=I(\"strides\",e,t,n),i=I(\"beginMask\",e,t,n),o=I(\"endMask\",e,t,n),u=I(\"ellipsisMask\",e,t,n),l=I(\"newAxisMask\",e,t,n),c=I(\"shrinkAxisMask\",e,t,n),p=I(\"x\",e,t,n);return[k3(p,s,r,a,i,o,u,l,c)]}case\"Pack\":return j(()=>{let s=I(\"axis\",e,t,n),r=I(\"tensors\",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error(\"the input tensors shape does not match\");return l?u:G(u,a)});return[Qn(o,s)]});case\"Unpack\":{let s=I(\"axis\",e,t,n),r=I(\"tensor\",e,t,n);return Fs(r,s)}case\"Tile\":{let s=I(\"reps\",e,t,n);return[cs(I(\"x\",e,t,n),s)]}case\"Split\":case\"SplitV\":{let s=I(\"axis\",e,t,n),r=I(\"numOrSizeSplits\",e,t,n),a=I(\"x\",e,t,n);return Ln(a,r,s)}case\"ScatterNd\":{let s=I(\"indices\",e,t,n),r=I(\"values\",e,t,n),a=I(\"shape\",e,t,n);return[M3(s,r,a)]}case\"GatherNd\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[W3(s,r)]}case\"SparseToDense\":{let s=I(\"sparseIndices\",e,t,n),r=I(\"outputShape\",e,t,n),a=I(\"sparseValues\",e,t,n),i=I(\"defaultValue\",e,t,n);return[xI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},u4=(e,t,n)=>{switch(e.op){case\"SparseFillEmptyRows\":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Wc.sparseFillEmptyRows(I(\"indices\",e,t,n),I(\"values\",e,t,n),I(\"denseShape\",e,t,n),I(\"defaultValue\",e,t,n));return[s,r,a,i]}case\"SparseReshape\":{let{outputIndices:s,outputShape:r}=Wc.sparseReshape(I(\"inputIndices\",e,t,n),I(\"inputShape\",e,t,n),I(\"newShape\",e,t,n));return[s,r]}case\"SparseSegmentMean\":return[Wc.sparseSegmentMean(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];case\"SparseSegmentSum\":return[Wc.sparseSegmentSum(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Xx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Yx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>$n(d)[0]),c=[];s!=null&&(c=s.map(d=>$n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((cS(d)||g4(d)||b4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function p4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>$n(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var h4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],f4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],m4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function cS(e){return h4.indexOf(e.op)>=0}function g4(e){return f4.indexOf(e.op)>=0}function b4(e){return m4.indexOf(e.op)>=0}var Pm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Pm(e.functions[n],this)})}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){let t=Object.keys(e).map(n=>e[n].map(s=>s.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=>{let 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),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=Yx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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empty.\");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},mS=class extends fS{constructor(){super(mS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},gS=mS;gS.INITIAL_CAPACITY=32;function bS(e){return new D4(e)}function Xy(e){return new F4(e)}function E4(e,t){return new yS(e,t)}function R4(e,t=vS.FAIL){return new U4(e,t)}var Ut=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new V4(this,e)}filter(e){return new L4(this,e)}map(e){return new B4(this,e)}mapAsync(e){return new Qx(this,e)}serialMapAsync(e){return new Qx(this,e).serial()}flatmap(e){return new W4(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new M4(this,e,t)}columnMajorBatch(e,t=!0,n=pS){return this.rowMajorBatch(e,t).map(r=>N4(r,n))}concatenate(e,t){return new yS(bS([this,e]),t)}take(e){return e<0||e==null?this:new z4(this,e)}skip(e){return e<0||e==null?this:new P4(this,e)}prefetch(e){return new xS(this,e)}shuffle(e,t){return new G4(this,e,t)}serial(){return new O4(this)}},D4=class extends Ut{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:_4(e),done:!1}}},F4=class extends Ut{constructor(e){super();this.nextFn=e}summary(){return\"Function call\"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},O4=class extends Ut{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},P4=class extends Ut{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Re(e.value)}return this.upstream.next()}},z4=class extends Ut{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},M4=class extends Ut{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},L4=class extends Ut{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Re(e.value)}}},B4=class extends Ut{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},V4=class extends Ut{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Qx=class extends Ut{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Yy=class extends Ut{constructor(){super();this.outputQueue=new gS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},W4=class extends Yy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},yS=class extends Ut{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return\"TODO: fill in upstream of chained summaries -> Chained\"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},vS=(e=>(e[e.FAIL=0]=\"FAIL\",e[e.SHORTEST=1]=\"SHORTEST\",e[e.LONGEST=2]=\"LONGEST\",e))(vS||{}),U4=class extends Ut{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return\"{TODO: fill in upstream of zip summaries} -> Zip\"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await hS(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. 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q4(e){if(!eo(e))throw new Error(\"The argument to zip() must be an object or array.\");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Tn(async()=>{let n=await hS(e,s=>{if(s instanceof Jo)return{value:s.iterator(),recurse:!1};if(eo(s))return{value:null,recurse:!0};throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\")});return R4(n,1)},t)}function j4(e){if(e===null)return null;let t=e[0];return T4(t)?{value:K4(e),recurse:!1}:{value:null,recurse:!0}}function K4(e){if(e.length===0)throw new Error(\"Can't make a batch of zero elements.\");return e[0]instanceof et?Qn(e):hs(e)}var wS=class extends Jo{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`\n`).map(s=>(s.endsWith(\"\\r\")&&(s=s.slice(0,-1)),s))}},Kc='\"',Tu=Symbol(\"out\"),Zx=Symbol(\"field\"),Xc=Symbol(\"quote\"),Hf=Symbol(\"quoteafterquote\"),Jx=Symbol(\"quoteinquote\"),kS=class extends Jo{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=\",\",this.delimWhitespace=!1,this.base=new wS(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>\"Delimiter should not be provided when delimWhitespace is true.\"),this.delimWhitespace=!0,this.delimiter=\" \"):this.delimiter=t.delimiter?t.delimiter:\",\"}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error(\"Column names must be provided if there is no header line.\");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>\"The length of provided columnNames (\"+this.fullColumnNames.length.toString()+\") does not match the length of the header line read from file (\"+e.length.toString()+\").\"),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>\"Duplicate column names found: \"+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key \"'+s+'\" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+\").\")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error(\"No data was found for CSV parsing.\");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o===\"\")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype===\"bool\"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else 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Kc:a=Xc;break;default:}break;default:}if(a===Hf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},IS=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(X().get(\"IS_NODE\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new IS(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),hs(n,t)}},SS=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Qt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(X().get(\"IS_NODE\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new SS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=xk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=On(ce(e,\"float32\"),0),n;n=ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}},CS=class{},NS=class extends Ut{split(e){return new X4(this,e)}},X4=class extends NS{constructor(e,t){super();this.upstream=e,this.impl=new Y4(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y4=class extends Yy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Q4=class extends Ut{decodeUTF8(){return new Z4(this)}},Z4=class extends NS{constructor(e){super();this.upstream=e,this.impl=new J4(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},J4=class extends Yy{constructor(e){super();if(this.upstream=e,X().get(\"IS_BROWSER\"))this.decoder=new 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o=e.locToIndex(i);s.values[r]=e.values[o]}return s}var Ru=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function fC(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let o=s-n+1,u=t-n+1,l=Math.log(o),c=.5*Math.exp(2*l/3),p=.5*Math.sqrt(l*c*(o-c)/o)*Math.sign(u-o/2),d=Math.max(n,Math.floor(t-u*c/o+p)),h=Math.min(s,Math.floor(t+(o-u)*c/o+p));fC(e,t,d,h)}let r=e[t],a=n,i=s;for(w.swap(e,n,t),Ru(e[s],r)>0&&w.swap(e,n,s);a<i;){for(w.swap(e,a,i),a++,i--;Ru(e[a],r)<0;)a=a+1;for(;Ru(e[i],r)>0;)i=i-1}Ru(e[n],r)===0?w.swap(e,n,i):(i=i+1,w.swap(e,i,s)),i<=t&&(n=i+1),t<=i&&(s=i-1)}}function mC(e,t,n,s,r){let a=t[t.length-1],[i,o]=[e.length/a,a],u=w.getTypedArrayFromDType(n,i*s),l=w.getTypedArrayFromDType(\"int32\",i*s);for(let p=0;p<i;p++){let d=p*o,h=e.subarray(d,d+o),f=new Array(h.length);h.forEach((y,v)=>f[v]={value:y,index:v}),s<f.length&&(fC(f,s),f=f.slice(0,s)),r&&f.sort(Ru);let m=p*s,g=u.subarray(m,m+s),b=l.subarray(m,m+s);for(let y=0;y<s;y++)g[y]=f[y].value,b[y]=f[y].index}let c=t.slice();return c[c.length-1]=s,[De(c,n,u),De(c,\"int32\",l)]}function gC(e,t,n,s){let r=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let i={},o=new Int32Array(n[r]),u=new Vt(a,s,e),l=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let b=0;b<a[0];b++)for(let y=0;y<a[2];y++)g.push(u.get(b,f,y));m=g.join(\",\")}if(i[m]!==void 0)o[f]=i[m];else{let g=Object.keys(i).length;i[m]=g,o[f]=g,l.push(f)}}let p=a.slice();p[1]=Object.keys(i).length;let d=new Vt(p,s);l.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let b=0;b<a[2];b++)d.set(u.get(g,f,b),g,m,b)});let h=n.slice();return h[r]=p[1],{outputValues:d.values,outputShape:h,indices:o}}var vpe=\"0.0.0\";pp(\"cpu\",()=>new RS,1);var bC=st(Ra,e=>e>=0?e:Math.exp(e)-1),uG={kernelName:Ra,backendName:\"cpu\",kernelFunc:bC};function yC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;ve([r],\"leakyRelu\");let i=w.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,u=w.getTypedArrayFromDType(\"float32\",i);for(let l=0;l<o.length;l++)u[l]=o[l]<0?a*o[l]:o[l];return n.makeTensorInfo(r.shape,\"float32\",u)}var lG={kernelName:La,backendName:\"cpu\",kernelFunc:yC},cG=At((e,t)=>e<0?t*e:e);function vC(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;ve([s,r],\"prelu\");let a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,[o,u]=cG(s.shape,r.shape,a,i,\"float32\");return n.makeTensorInfo(u,\"float32\",o)}var dG={kernelName:Qa,backendName:\"cpu\",kernelFunc:vC},xC=st(Za,e=>Math.max(0,e)),pG={kernelName:Za,backendName:\"cpu\",kernelFunc:xC},wC=st(ei,e=>Math.min(Math.max(0,e),6)),hG={kernelName:ei,backendName:\"cpu\",kernelFunc:wC};function iv(e,t,n,s,r){if(n===\"linear\")return Os({inputs:{x:t},backend:e});if(n===\"relu\")return xC({inputs:{x:t},backend:e});if(n===\"elu\")return bC({inputs:{x:t},backend:e});if(n===\"relu6\")return wC({inputs:{x:t},backend:e});if(n===\"prelu\")return 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mG={kernelName:Ia,backendName:\"cpu\",kernelFunc:kC};function gG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=kC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=Kl({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=iv(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var bG={kernelName:na,backendName:\"cpu\",kernelFunc:gG},yG=st(sl,e=>Math.acos(e)),vG={kernelName:sl,backendName:\"cpu\",kernelFunc:yG},xG=st(rl,e=>Math.acosh(e)),wG={kernelName:rl,backendName:\"cpu\",kernelFunc:xG};function kG(e){let{inputs:t,backend:n}=e,s=t;ve(t,\"addN\");let r=s.map(o=>n.data.get(o.dataId).values),a=De(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var IG={kernelName:xa,backendName:\"cpu\",kernelFunc:kG};function 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o=w.parseAxisParam(a,r.shape),u=o,l=N.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=Vn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=N.getInnerMostAxes(u.length,r.shape.length)),N.assertAxesAreInnerMostDims(\"any\",u,c.shape.length);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(i){let b=N.expandShapeToKeepDim(p,o),y=mt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var TG={kernelName:il,backendName:\"cpu\",kernelFunc:NG};function $G(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;ve(r,\"argMax\");let 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EG={kernelName:ol,backendName:\"cpu\",kernelFunc:AG},RG=st(ul,e=>Math.asin(e)),DG={kernelName:ul,backendName:\"cpu\",kernelFunc:RG},FG=st(ll,e=>Math.asinh(e)),OG={kernelName:ll,backendName:\"cpu\",kernelFunc:FG},PG=st(cl,e=>Math.atan(e)),zG={kernelName:cl,backendName:\"cpu\",kernelFunc:PG},MG=At((e,t)=>Math.atan2(e,t)),LG=Gt(pl,MG),BG={kernelName:pl,backendName:\"cpu\",kernelFunc:LG},VG=st(dl,e=>Math.atanh(e)),WG={kernelName:dl,backendName:\"cpu\",kernelFunc:VG};function ov(e,t,n,s,r,a){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a===\"max\"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=De(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,C=x*s[0];for(let T=0;T<r.inChannels;++T)for(let E=0;E<r.outHeight;++E){let 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W=z-T,q=m.get(g,F,z,b);q>P&&(P=q,r?R=a?((g*s.inHeight+F)*s.inWidth+z)*s.inChannels+b:(F*s.inWidth+z)*s.inChannels+b:R=$*d+W)}}i.set(R,g,y,C,b)}}return i}function SC(e,t,n,s,r,a){let i=r.strideDepth,o=r.strideHeight,u=r.strideWidth,l=r.dilationDepth,c=r.dilationHeight,p=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,b=r.padInfo.left,y=a===\"max\"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=De(r.outShape,n),x=v.values,k=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let A=0;A<r.batchSize;++A){let P=A*k,R=A*s[0];for(let F=0;F<r.inChannels;++F)for(let $=0;$<r.outDepth;++$){let z=$*i-m,W=z;for(;W<0;)W+=l;let q=Math.min(r.inDepth,d+z),K=P+$*C;for(let Y=0;Y<r.outHeight;++Y){let Z=Y*o-g,te=Z;for(;te<0;)te+=c;let ee=Math.min(r.inHeight,h+Z),se=K+Y*T;for(let ne=0;ne<r.outWidth;++ne){let oe=ne*u-b,re=oe;for(;re<0;)re+=p;let le=Math.min(r.inWidth,f+oe),me=se+ne*E,we=y,Se=0,Ee=0;for(let Xe=W;Xe<q;Xe+=l){let Je=R+Xe*s[1];for(let Ye=te;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=re;Ce<le;Ce+=p){let ut=tt+Ce*s[3],rt=e[ut+F];if(a===\"max\"&&rt>we?we=rt:a===\"avg\"&&(Se+=rt,Ee++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Pe=me+F;x[Pe]=a===\"avg\"?Se/Ee:we}}}}return v}function UG(e,t){let n=De(t.outShape,\"int32\"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let C=k*r-h,T=C;for(;T<0;)T+=o;let E=Math.min(t.inHeight,c+C);for(let A=0;A<t.outWidth;++A){let P=A*a-f,R=P;for(;R<0;)R+=u;let 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c=N.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,C=c.effectiveFilterWidth,T=x-1-c.padInfo.front,E=C-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,\"float32\"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-T,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<C;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var XG={kernelName:ig,backendName:\"cpu\",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;ve([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,\"float32\"),C=1/(h*f),T=n.data.get(r.dataId).values,E=De(r.shape,\"float32\",T);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*C,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var QG={kernelName:ag,backendName:\"cpu\",kernelFunc:YG};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var JG={kernelName:Pa,backendName:\"cpu\",kernelFunc:ZG};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;ve([r],\"batchToSpaceND\");let o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Vn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=fa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var tH={kernelName:oo,backendName:\"cpu\",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=Jy(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var sH={kernelName:og,backendName:\"cpu\",kernelFunc:nH};function rH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var aH={kernelName:ug,backendName:\"cpu\",kernelFunc:rH},iH=st(Ir,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),oH={kernelName:Ir,backendName:\"cpu\",kernelFunc:iH},uH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let c=o[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,\"float32\")},lH={kernelName:Kd,backendName:\"cpu\",kernelFunc:uH};function 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l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let we=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let 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Vt(p.inShape,\"float32\"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,C]=l,T=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,we=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,($+Je)/oe);for(let Ce=0;Ce<Y;++Ce){let ut=Ce-we,rt=Math.max(0,Math.ceil(ut/re)),Zt=Math.min(se,(z+ut)/re);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Se,Et=Math.max(0,Math.ceil(In/le)),Jt=Math.min(ne,(W+In)/le),Sn=0;for(let Cn=Ye;Cn<tt;++Cn){let Xt=Cn*oe-Je;for(let Rn=rt;Rn<Zt;++Rn){let en=Rn*re-ut;for(let Ms=Et;Ms<Jt;++Ms){let Ls=Ms*le-In,xi=v*Ee+x*Cn+k*Rn+C*Ms,Js=E*($-1-Xt)+A*(z-1-en)+P*(W-1-Ls)+R*Pe;for(let Bs=0;Bs<te;++Bs){let 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$H={kernelName:co,backendName:\"cpu\",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;ve(r,\"cumsum\");let u=N.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=N.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=yn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=N.getUndoAxesPermutation(u),y=Vn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var AH={kernelName:lo,backendName:\"cpu\",kernelFunc:_H};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=Jy(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OS(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var RH={kernelName:pg,backendName:\"cpu\",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,C=new Vt(h.outShape,r.dtype),T=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,A=C.values;for(let P=0;P<h.batchSize;++P){let R=P*c[0],F=P*C.strides[0];for(let $=0;$<h.outHeight;++$){let z=F+$*C.strides[1],W=$*h.strideHeight-x;for(let q=0;q<f;++q){let K=W+q*g;if(K<0||K>=h.inHeight)continue;let Y=q*p[0],Z=R+K*c[1];for(let te=0;te<h.outWidth;++te){let ee=z+te*C.strides[2],se=te*h.strideWidth-v;for(let ne=0;ne<m;++ne){let oe=se+ne*b;if(oe<0||oe>=h.inWidth)continue;let re=Y+ne*p[1],le=Z+oe*h.inChannels,me=ee,we=re;for(let Se=0;Se<h.inChannels;++Se){let Ee=T[le+Se];for(let Pe=0;Pe<k;++Pe)A[me+Pe]+=Ee*E[we+Pe];me+=k,we+=k}}}}}}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var OH={kernelName:Aa,backendName:\"cpu\",kernelFunc:NC};function PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],\"depthwiseConv2dNativeBackpropFilter\");let p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,\"float32\"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),C=n.data.get(a.dataId).values,T=new Vt(a.shape,a.dtype,C);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*T.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var zH={kernelName:hg,backendName:\"cpu\",kernelFunc:PH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;ve([r,a],\"depthwiseConv2DNativeBackpropInput\");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,\"float32\"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,C]=p,T=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let we=me-se,Se=Math.max(0,Math.ceil(we/te)),Ee=Math.min(Y,(F+we)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-we;for(let rt=Je;rt<Ye;++rt){let Zt=rt*ee-Xe,Nt=x*re+k*Ce+C*rt,In=E*(F-1-ut)+A*($-1-Zt)+P*le;for(let Et=0;Et<oe;++Et){let Jt=le*oe+Et,Sn=v[Nt+Jt],Cn=T[In+Et];tt+=Sn*Cn}}}m[g*re+b*me+y*Pe+le]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var LH={kernelName:fg,backendName:\"cpu\",kernelFunc:MH};function BH(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=De([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var VH={kernelName:mg,backendName:\"cpu\",kernelFunc:BH},WH={kernelName:Yd,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:C,filterWidth:T,dilationHeight:E,dilationWidth:A,outShape:P}=N.computeDilation2DInfo(s.shape,r.shape,a,i,\"NHWC\",o),R=w.sizeFromShape(P),F=P.length,$=w.getArrayFromDType(s.dtype,R);for(let W=0;W<h;++W)for(let q=0;q<b;++q){let K=q*x-v.top;for(let Y=0;Y<y;++Y){let Z=Y*k-v.left;for(let te=0;te<g;++te){let ee=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<C;++ne){let oe=K+ne*E;if(oe>=0&&oe<f)for(let re=0;re<T;++re){let le=Z+re*A;if(le>=0&&le<m){let me=w.locToIndex([W,oe,le,te],c,w.computeStrides(s.shape)),we=w.locToIndex([ne,re,te],d,w.computeStrides(r.shape)),Se=l[me]+p[we];Se>ee&&(ee=Se)}}}let se=w.locToIndex([W,q,Y,te],F,w.computeStrides(P));$[se]=ee}}}return{dataId:u.write(w.toTypedArray($,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},UH={kernelName:Yf,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:C,dilationHeight:T,dilationWidth:E,outShape:A}=N.computeDilation2DInfo(s.shape,r.shape,i,o,\"NHWC\",u);w.assert(a.rank===A.length,()=>`Error in ${Yf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);let P=w.toNestedArray(A,l.data.get(a.dataId).values),R=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let $=0;$<d;++$)for(let z=0;z<g;++z){let W=z*v-y.top;for(let q=0;q<b;++q){let K=q*x-y.left;for(let Y=0;Y<m;++Y){let 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#define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `):(e=\"\",t=\"attribute\",n=\"varying\",s=\"varying\",r=\"texture2D\",a=\"gl_FragColor\",i=\"\",o=`\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `,u=`\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `,l=`\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function gi(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join(\"\")}function qp(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join(\"\")}function $5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function _5(e,t,n=\"index\"){let s=e.map((a,i)=>i),r=$5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join(\"\")}function dv(e){let t=w.computeStrides(e).map(n=>n.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n  }\n`}function pv(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var r1=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:a1}=N;function A5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=hv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push(\"uniform int outShape;\");break;case 2:s.push(\"uniform ivec2 outShape;\"),s.push(\"uniform int outShapeStrides;\");break;case 3:s.push(\"uniform ivec3 outShape;\"),s.push(\"uniform ivec2 outShapeStrides;\");break;case 4:s.push(\"uniform ivec4 outShape;\"),s.push(\"uniform ivec3 outShapeStrides;\");break;default:break}s.push(\"uniform ivec2 outTexShape;\")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:\"\"};`)});let r=s.join(`\n`),a=e.map(h=>E5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`\n`),i=t.texShape,o=hn(),u=F5(o),l,c,p=z5(o);return t.isPacked?(l=R5(t.logicalShape,i,n.enableShapeUniforms),c=P5(o)):(l=D5(t.logicalShape,i,n.enableShapeUniforms),c=O5(o)),n.packedInputs&&(p+=V5),[p,u,c,r,l,a,n.userCode].join(`\n`)}function su(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return J5(e,t);case 1:return tK(e,t);case 2:return sK(e,t);case 3:return aK(e,t);case 4:return oK(e,t);case 5:return uK(e);case 6:return lK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function i1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Z5(e);case 1:return eK(e,t);case 2:return nK(e,t);case 3:return rK(e,t);default:return iK(e,t)}}function E5(e,t,n=!1,s){let r=\"\";n?r+=i1(e,s):r+=su(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=cK(e,t):r+=dK(e,t)),r}function R5(e,t,n){switch(e.length){case 0:return o1();case 1:return W5(e,t,n);case 2:return Y5(e,t,n);case 3:return G5(e,t,n);default:return q5(e,t,n)}}function D5(e,t,n){switch(e.length){case 0:return o1();case 1:return U5(e,t,n);case 2:return Q5(e,t,n);case 3:return H5(e,t,n);case 4:return j5(e,t,n);case 5:return K5(e,t);case 6:return X5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function F5(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function O5(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function P5(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function z5(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${M5}\n    ${L5}\n    ${B5}\n  `}var M5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,L5=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,B5=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,V5=`\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;function o1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function W5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function U5(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function G5(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function H5(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${qp([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=gi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function q5(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function j5(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${qp([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=gi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function K5(e,t){let n=gi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function X5(e,t){let n=gi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function Y5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function Q5(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function bi(e){return`offset${e}`}function Z5(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=hn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function J5(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=bi(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function eK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=hn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function tK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${ru(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=bi(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function nK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=hn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function sK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=au(e,u),h=[\"row\",\"col\"];return`\n      ${su(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${iu(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${ru(e)}\n      }\n    `;let l=a[0],c=a[1],p=bi(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function rK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=au(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${i1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${iu(m,h)});\n        }\n      `}let o=hn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function aK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=au(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${su(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${iu(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=bi(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function iK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=hn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function oK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=au(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${su(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${iu(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=bi(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function uK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=au(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${su(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${iu(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${ru(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=bi(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function lK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=au(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${su(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${iu(b,a)});\n      }\n    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${ru(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=bi(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function ru(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function cK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=a1(e.shapeInfo.logicalShape,t.logicalShape),u=ot(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":o.indexOf(v)>-1&&(h=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${r}() {\n      ${u} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${s}(${d});\n      ${h}\n    }\n  `}function dK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`\n      float ${r}() {\n        return sampleTexture(${n}, resultUV);\n      }\n    `;let l=ot(u),c=a1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];o===0?d=\"\":u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`\n`);let f=\"\";return u<2&&o>0?f=\"coords\":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(\", \"),`\n    float ${r}() {\n      ${l} coords = getOutputCoords();\n      ${d}\n      return get${s}(${f});\n    }\n  `}function ot(e){if(e<=1)return\"int\";if(e===2)return\"ivec2\";if(e===3)return\"ivec3\";if(e===4)return\"ivec4\";if(e===5)return\"ivec5\";if(e===6)return\"ivec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function hv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function au(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function iu(e,t){return t.map(n=>e[n]).join(\", \")}function pK(e,t,n,s){let r=n.map((x,k)=>{let C={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(C.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:C}}),a=r.map(x=>x.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=A5(r,i,t),u=zC(e.gl,o),l=e.createProgram(u),c=null,p=e.getUniformLocation(l,\"NAN\",!1);X().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(l,\"INFINITY\",!1));let d=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,d),h[`offset${k}`]=e.getUniformLocation(l,`offset${k}`,d),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(l,`${k}Shape`,d),m[`${k}TexShape`]=e.getUniformLocation(l,`${k}TexShape`,d))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,\"outShape\",d),y=e.getUniformLocation(l,\"outShapeStrides\",d),b=e.getUniformLocation(l,\"outTexShape\",d));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(l,x.name,d)}),{program:t,fragmentShader:u,source:o,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function nw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function hK(e,t,n,s,r){t.program.enableShapeUniforms||(nw(t.inShapeInfos,n),nw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=hv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function fK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=hv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${X().getNumber(\"WEBGL_VERSION\")}`,a}function kn(e){return X().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var mK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):gi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},gK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?qp([\"r\",\"c\",\"d\"],e):gi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},bK=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=hn();this.outputShape=e,this.userCode=`\n      ${r1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},yK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=hn();this.outputShape=e,this.userCode=`\n      ${r1}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},vK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?pv():dv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},xK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${i};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${o}] = values[0];\n            } else if (offset == 1) {\n              result[${o}] = values[1];\n            } else if (offset == 2) {\n              result[${o}] = values[2];\n            } else {\n              result[${o}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?pv():dv(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    `}},wK={};Ae(wK,{bindVertexProgramAttributeStreams:()=>g1,createBufferFromOutputTexture:()=>v1,createFloat16MatrixTexture:()=>p1,createFloat16PackedMatrixTexture:()=>m1,createFloat32MatrixTexture:()=>d1,createIndexBuffer:()=>c1,createPackedMatrixTexture:()=>f1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>l1,createVertexShader:()=>u1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>w1,downloadFloat32MatrixFromBuffer:()=>x1,downloadMatrixFromPackedOutputTexture:()=>I1,downloadPackedMatrixFromBuffer:()=>k1,getInternalFormatForFloat16MatrixTexture:()=>mv,getInternalFormatForFloat16PackedMatrixTexture:()=>yv,getInternalFormatForFloat32MatrixTexture:()=>fv,getInternalFormatForPackedMatrixTexture:()=>bv,getInternalFormatForUnsignedBytesMatrixTexture:()=>gv,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>y1});function u1(e){let t=hn(),n=`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    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t=X().getNumber(\"WEBGL_VERSION\");e!=null?(this.gl=e,p5(t,e)):this.gl=vs(t);let n=\"WEBGL_color_buffer_float\",s=\"EXT_color_buffer_half_float\";if(X().getNumber(\"WEBGL_VERSION\")===1){let r=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Fu(this.gl,r),Mn(this.gl,a))this.textureHalfFloatExtension=Fu(this.gl,a);else if(X().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Mn(this.gl,s))this.colorBufferHalfFloatExtension=Fu(this.gl,s);else if(X().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(n=\"EXT_color_buffer_float\",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=l1(this.gl),this.indexBuffer=c1(this.gl),this.framebuffer=GC(this.gl),this.textureConfig=cv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;fe(e,()=>e.finish()),fe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),fe(e,()=>e.deleteFramebuffer(this.framebuffer)),fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),fe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),fe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),d1(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),p1(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),h1(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),y1(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),b1(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return 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fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),LC(t,n),this.debug&&rd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=g1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&rd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?qC(this.gl,e,t):jC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),KC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=tu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&rd(this.gl,this.program),Ou(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Fu(this.gl,X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=kK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),ad(this.gl,e,this.framebuffer),this.debug&&Ou(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(ad(this.gl,this.outputTexture,this.framebuffer),this.debug&&Ou(this.gl)):Bm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;ad(s,e,this.framebuffer),this.debug&&Ou(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function kK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:IK,bincountImpl:S1,bincountReduceImpl:SK,ceilImpl:CK,concatImpl:NK,equalImpl:TK,expImpl:$K,expm1Impl:_K,floorImpl:AK,gatherNdImpl:EK,gatherV2Impl:RK,greaterImpl:DK,greaterEqualImpl:FK,lessImpl:OK,lessEqualImpl:PK,linSpaceImpl:zK,logImpl:MK,maxImpl:LK,maximumImpl:BK,minimumImpl:VK,multiplyImpl:WK,negImpl:UK,notEqualImpl:GK,prodImpl:HK,rangeImpl:qK,rsqrtImpl:jK,sigmoidImpl:KK,simpleAbsImpl:C1,sliceImpl:XK,sparseFillEmptyRowsImpl:YK,sparseReshapeImpl:QK,sparseSegmentReductionImpl:N1,sqrtImpl:ZK,stridedSliceImpl:JK,stringNGramsImpl:eX,stringSplitImpl:tX,stringToHashBucketFastImpl:nX,subImpl:sX,tileImpl:rX,topKImpl:aX,transposeImpl:vv,uniqueImpl:iX}=Qy;function T1(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:T1(e,t)}function oX(e,t){if(e===1)return\"rc\";let n=\"\";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=\",\");return n}var uX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=kn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=un(\"rc\",this.rank),n=ot(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},$1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${lX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?pv():dv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function lX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?_5([\"r\",\"c\",\"d\"],\"inputShape\"):gi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var cX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=rw(t,n),r=aw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=sw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=rw(n,s),a=aw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=sw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function dX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function sw(e,t,n,s,r){let a=pX(t,s),i;if(r){let[u,l]=tu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=dX(n,a);return i*o}function pX(e,t){switch(e){case 3:return bv(t);case 4:return yv(t);case 1:return fv(t);case 0:return mv(t);case 2:return gv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function hX(e){return X().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function rw(e,t){if(e===1)return 3;if(e===0||e==null)return hX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function aw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},ts=\"if (isnan(x)) return x;\",fX=\"return x;\",iw=\"return abs(x);\",mX=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",gX=ts+`\n  return (x < 0.0) ? 0.0 : x;\n`,bX=ts+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Pi=\"return x;\",yX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",vX=\"return x;\",xX=`\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`,wX=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,kX=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,IX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Yr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},SX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let t=e.length,n=un(\"rc\",t),s=ot(t),r=oX(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},CX=xs.whereImpl,NX=1e-7,TX=1e-4,Zc={};function $X(e){return e in Zc||(Zc[e]={}),Zc[e]}var _X=X().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),AX=600;function EX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*AX/1024/1024}var _1=class extends nl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof jf)t=e;else{let n=vs(X().getNumber(\"WEBGL_VERSION\"),e);t=new jf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber(\"WEBGL_VERSION\"));t=new jf(n),this.binaryCache=$X(X().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new cX(this.gpgpu),this.numMBBeforeWarning=EX(),this.texData=new Ud(this,Ss())}nextDataId(){return _1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||X().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Yr(i,Pi):p=new Hs(i,Pi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=N.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Yr(s,Pi):h=new Hs(s,Pi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool(\"DEBUG\")&&!X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&X().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&X().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=N.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new Yr(r,Pi):d=new Hs(r,Pi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!FC(n))throw X().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool(\"WEBGL_PACK\")&&s===!0,i=a?id(t):t,o=a?new yK(i):new bK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=_X){return X().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return CX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Yr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=C1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,iw,e.dtype);let t=new Hs(e.shape,iw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new SX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new uX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ma(e.shape),...ga(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ma(t),...ga(t)],a=new $1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=id(r),o;s?o=new gK(i):o=new mK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!el(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=fK(e,l,c),d=this.getAndSaveBinary(p,()=>pK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),hK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=X().getBool(\"DEBUG\");X().set(\"DEBUG\",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(X().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?NX:TX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=QC(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=tu(c[0],c[1])),o?d=new xK(p,m):d=new vK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),C=this.texData.get(k.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=RX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},A1=_1;A1.nextDataId=0;function RX(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var xpe=\"0.0.0\";function DX(){X().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}dp.isBrowser()&&pp(\"webgl\",()=>new A1,2);var wpe={forceHalfFloat:DX},E1=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,so=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},jp=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,Zl=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=kn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${ot(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=un(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function En(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var FX={kernelName:Ma,backendName:\"webgl\",kernelFunc:En};function Er(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=En({inputs:{x:s},backend:n}),u=En({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var OX={kernelName:jd,backendName:\"webgl\",kernelFunc:Er},R1=\"return (a < 0.) ? b * a : a;\",D1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function PX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(D1,r.shape,i.shape):new so(R1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var zX={kernelName:La,backendName:\"webgl\",kernelFunc:PX},F1=\"return (a < 0.) ? b * a : a;\",O1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function MX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(O1,s.shape,r.shape):new so(F1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var LX={kernelName:Qa,backendName:\"webgl\",kernelFunc:MX},ou=\"if (isnan(x)) return x;\",BX=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,VX=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Yr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,C={dataId:x.dataId,dtype:x.dtype,shape:u.shape},T={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new so(e,u.shape,l.shape);return c.runWebGLProgram(E,[C,T],yn(x.dtype,k.dtype))}),y=Er({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||yn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?N.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?N.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new so(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e===\"linear\")return t?vX:fX;if(e===\"relu\")return t?wX:gX;if(e===\"elu\")return t?xX:mX;if(e===\"relu6\")return t?kX:bX;if(e===\"prelu\")return t?O1:F1;if(e===\"leakyrelu\")return t?D1:R1;if(e===\"sigmoid\")return t?IX:yX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=kn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},ow={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},uw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},lw=\"return a * b;\";function xv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new uw(ow.REAL,s.shape,r.shape),c=new uw(ow.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Er({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=WK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new Zl(lw,s.shape,r.shape):i=new so(lw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var WX={kernelName:Ka,backendName:\"webgl\",kernelFunc:xv};function UX(e,t,n){let s=[ma(e.shape),...ga(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ma(t),...ga(t)],i=new $1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!el(r.shape,u)&&!(c.texture!==null&&el(c.shape,u))?UX(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var GX={kernelName:Eo,backendName:\"webgl\",kernelFunc:he},cw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},HX=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function qX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yi(e,t,n,s){let r=qX(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new cw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new cw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new HX({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var jX=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=KX(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function KX(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var XX=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ot(this.rank),r=T1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function Xp(e,t,n){let s=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new XX(e.shape,t):new jX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function YX(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=N.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=N.getInnerMostAxes(o.length,a)),N.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=N.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=yi(b,y,\"sum\",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return YX(r,a,i,n)}var QX={kernelName:ai,backendName:\"webgl\",kernelFunc:Yp};function cn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=vv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var ZX={kernelName:ci,backendName:\"webgl\",kernelFunc:cn},z1=1e3;function zd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=n?T.shape[1]:T.shape[2],F=a!=null,$=i!=null,z=u===\"leakyrelu\",W=u!=null?Kp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>z1&&q===!1){let Z=T,te=E;n&&(Z=cn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=cn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=xv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=yn(e.dtype,t.dtype),te=new P1(k,C,[P,h,f],n,s,F,W,$,z),ee=[T,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function JX(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var e8={kernelName:na,backendName:\"webgl\",kernelFunc:JX},dw=\"return abs(x);\";function t8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),i=C1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new Yr(s.shape,dw):r=new Hs(s.shape,dw),n.runWebGLProgram(r,[s],s.dtype)}var n8={kernelName:io,backendName:\"webgl\",kernelFunc:t8},s8=ts+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,r8=Ke({opSnippet:s8}),a8={kernelName:sl,backendName:\"webgl\",kernelFunc:r8},i8=ts+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:rl,backendName:\"webgl\",kernelFunc:o8},pw=\"return a + b;\",l8=qt({opSnippet:pw,packedOpSnippet:pw,supportsComplex:!0,cpuKernelImpl:IK}),c8={kernelName:kr,backendName:\"webgl\",kernelFunc:l8},d8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(\" + \");this.userCode=`\n      void main() {\n        ${n.join(`\n        `)}\n\n        float result = ${s};\n        setOutput(result);\n      }\n    `}},p8=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(\" + \");this.userCode=`\n      void main() {\n        ${n.join(`\n        `)}\n\n        vec4 result = ${s};\n        setOutput(result);\n      }\n    `}};function ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return En({inputs:{x:s[0]},backend:n});if(s.length>X().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>yn(u,l)),a=s.map(u=>u.shape),o=X().getBool(\"WEBGL_PACK\")?new p8(s[0].shape,a):new d8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var h8={kernelName:xa,backendName:\"webgl\",kernelFunc:ld};function f8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims(\"all\",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,\"all\",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var m8={kernelName:al,backendName:\"webgl\",kernelFunc:f8};function g8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,\"any\",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var b8={kernelName:il,backendName:\"webgl\",kernelFunc:g8},y8=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},v8=class{constructor(e,t,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,o=i.length,u=ot(o),l=un(\"coords\",o),c,p;if(a===1){p=o+1;let T=ot(p);c=`\n        ${T} sourceLocR = ${T}(${l.join()}, 0);\n        ++${l[o-1]};\n        ${T} sourceLocG = ${T}(${l.join()}, 0);\n        ++${l[o-2]};\n        ${T} sourceLocA = ${T}(${l.join()}, 0);\n        --${l[o-1]};\n        ${T} sourceLocB = ${T}(${l.join()}, 0);\n        --${l[o-2]};`}else p=o,c=`\n        ${u} sourceLocR = coords;\n        ++${l[o-1]};\n        ${u} sourceLocG = coords;\n        ++${l[o-2]};\n        ${u} sourceLocA = coords;\n        --${l[o-1]};\n        ${u} sourceLocB = coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map(T=>\"int \"+T),m=un(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=un(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=un(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=un(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,C=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${C}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function M1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=N.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new y8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=M1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function L1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=N.computeOptimalWindowSize(a),o=new v8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=L1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function B1(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=N.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=M1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return L1(e,t,s)}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=B1(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var w8={kernelName:wa,backendName:\"webgl\",kernelFunc:x8};function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=B1(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var I8={kernelName:ol,backendName:\"webgl\",kernelFunc:k8},S8=ts+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,C8=Ke({opSnippet:S8}),N8={kernelName:ul,backendName:\"webgl\",kernelFunc:C8},T8=ts+\"return log(x + sqrt(x * x + 1.0));\",$8=Ke({opSnippet:T8}),_8={kernelName:ll,backendName:\"webgl\",kernelFunc:$8},A8=ts+`\n  return atan(x);\n`,E8=Ke({opSnippet:A8}),R8={kernelName:cl,backendName:\"webgl\",kernelFunc:E8},D8=BX+`\n  return atan(a, b);\n`,F8=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+VX+`\n  return result;\n`,O8=qt({opSnippet:D8,packedOpSnippet:F8}),P8={kernelName:pl,backendName:\"webgl\",kernelFunc:O8},z8=ts+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelName:dl,backendName:\"webgl\",kernelFunc:M8},tl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let T=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${T} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,C=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${C}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${C}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${C}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${C}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},wv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let A=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${A} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let C=Math.floor(a/4)*4,T=a%4,E=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${C}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${E}\n            }\n\n            int xC = xCCorner + ${C};\n            if (${T===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${T===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${T===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${E}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function B8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new tl(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var V8={kernelName:ka,backendName:\"webgl\",kernelFunc:B8};function W8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new wv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var U8={kernelName:qd,backendName:\"webgl\",kernelFunc:W8},G8=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},H8=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function q8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new H8(d);return n.runWebGLProgram(h,[r],i.dtype)}var j8={kernelName:ig,backendName:\"webgl\",kernelFunc:q8};function K8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;nu([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=new G8(c);return n.runWebGLProgram(p,[r],i.dtype)}var X8={kernelName:ag,backendName:\"webgl\",kernelFunc:K8};function Y8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return 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So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function fw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function NY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new CY(s.shape),i=[fw(s,r.complexTensorInfos.real),fw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var TY={kernelName:Kd,backendName:\"webgl\",kernelFunc:NY},$Y=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},_Y=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ot(s),a=un(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${Jc(i,u,m)}),\n            vec2(${Jc(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${Jc(i,u,h)}),\n          vec2(${Jc(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 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pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},DY=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},FY=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"},{name:\"pad\",type:\"ivec2\"},{name:\"stride\",type:\"ivec2\"},{name:\"dilation\",type:\"ivec2\"},{name:\"inChannels\",type:\"int\"},{name:\"itemsPerBlockRow\",type:\"int\"},{name:\"outWidth\",type:\"int\"}],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let{dataFormat:n}=t,s=hn(),r=n===\"channelsLast\",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?\"if(blockIndex < outShape[1] && pos < outShape[0]) {\":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.y + ${c};\n          pos = rc.x + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${s.output} = result;\n      }\n    `}};function G1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>z1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},C=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(el(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(T);let E=zd({a:k,b:T,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=C,A.shape=n.outShape,g=En({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),T=zd({a:k,b:C,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:T},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(C),b.push(T)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function H1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(C);let T=new FY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(T,[k],\"float32\",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o===\"leakyrelu\",z=o?Kp(o,!0):null,W=new P1(P.shape,C.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,C];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,\"float32\"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function OY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=G1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)h=H1({x:r,filter:a,convInfo:d,backend:n});else{let m=new U1(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var PY={kernelName:Na,backendName:\"webgl\",kernelFunc:OY},zY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},MY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},LY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},BY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new zY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var WY={kernelName:lg,backendName:\"webgl\",kernelFunc:VY};function UY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new MY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var GY={kernelName:Ta,backendName:\"webgl\",kernelFunc:UY};function HY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new DY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var qY={kernelName:Xd,backendName:\"webgl\",kernelFunc:HY};function jY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=N.computeConv3DInfo(r.shape,u,i,1,o),c=new LY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var KY={kernelName:cg,backendName:\"webgl\",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=N.computeConv3DInfo(u,a.shape,o,1,i),c=new BY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var YY={kernelName:dg,backendName:\"webgl\",kernelFunc:XY},QY=ou+`\n  return cos(x);\n`,ZY=Ke({opSnippet:QY}),JY={kernelName:$a,backendName:\"webgl\",kernelFunc:ZY},e7=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,t7=Ke({opSnippet:e7}),n7={kernelName:_a,backendName:\"webgl\",kernelFunc:t7},s7=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},r7=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new s7(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},a7={kernelName:co,backendName:\"webgl\",kernelFunc:r7},mw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"0.0\":`getX(${gw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${ot(s)} coords = getOutputCoords();\n        int end = ${bw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${bw(s,\"coords\")} = idx;\n          val += getX(${gw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function gw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function bw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function i7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=N.getAxesPermutation([a],u),c=r;l!=null&&(c=cn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=En({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new mw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new mw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=cn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var o7={kernelName:lo,backendName:\"webgl\",kernelFunc:i7};function u7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=S1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=SK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var l7={kernelName:pg,backendName:\"webgl\",kernelFunc:u7},c7=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function d7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new c7(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var p7={kernelName:po,backendName:\"webgl\",kernelFunc:d7},q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},j1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function h7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new j1(p):d=new q1(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var f7={kernelName:Aa,backendName:\"webgl\",kernelFunc:h7},m7=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},g7=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function b7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new m7(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var y7={kernelName:hg,backendName:\"webgl\",kernelFunc:b7};function v7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new g7(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var x7={kernelName:fg,backendName:\"webgl\",kernelFunc:v7},w7=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function k7(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new w7(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var I7={kernelName:mg,backendName:\"webgl\",kernelFunc:k7},S7=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function C7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new S7(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var N7={kernelName:Yd,backendName:\"webgl\",kernelFunc:C7};function T7(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=cn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=xv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Yp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var $7={kernelName:Qd,backendName:\"webgl\",kernelFunc:T7},_7=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",A7=`\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`,E7=Ke({opSnippet:_7,packedOpSnippet:A7}),R7={kernelName:Ra,backendName:\"webgl\",kernelFunc:E7},D7=\"return (b >= 1.0) ? a : a * (b + 1.0);\",F7=`\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,O7=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Zl(F7,s.shape,r.shape):new so(D7,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},P7={kernelName:gg,backendName:\"webgl\",kernelFunc:O7},z7=`\n  return vec4(equal(a, b));\n`,M7=\"return float(a == b);\",L7=qt({opSnippet:M7,packedOpSnippet:z7,dtype:\"bool\",cpuKernelImpl:TK}),B7={kernelName:ho,backendName:\"webgl\",kernelFunc:L7},V7=`\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${N.ERF_P};\n  float a1 = ${N.ERF_A1};\n  float a2 = ${N.ERF_A2};\n  float a3 = ${N.ERF_A3};\n  float a4 = ${N.ERF_A4};\n  float a5 = ${N.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,W7=Ke({opSnippet:V7}),U7={kernelName:hl,backendName:\"webgl\",kernelFunc:W7},G7=ou+`\n  return exp(x);\n`,H7=`\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,K1=Ke({opSnippet:G7,packedOpSnippet:H7,cpuKernelImpl:$K,dtype:\"float32\"}),q7={kernelName:Da,backendName:\"webgl\",kernelFunc:K1};function Hm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var j7={kernelName:fo,backendName:\"webgl\",kernelFunc:Hm},yw=\"return exp(x) - 1.0;\",K7=Ke({opSnippet:yw,packedOpSnippet:yw,cpuKernelImpl:_K}),X7={kernelName:mo,backendName:\"webgl\",kernelFunc:K7},vw=class{constructor(e,t,n){this.variableNames=[\"real\",\"imag\"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n      const float exponentMultiplier = ${r};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${i}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${s});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${s}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${a};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}};function X1(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new vw(\"real\",u,t),c=new vw(\"imag\",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Er({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Y7(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!1,n)}var 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k=vv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let C=n.texData.get(h.dataId);C.values=k}else h=Xp(r,c,n);l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims(\"max\",l,o);let[f,m]=N.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=N.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=LK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=kQ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var IQ={kernelName:Va,backendName:\"webgl\",kernelFunc:Q1},SQ=E1+`\n  return max(a, b);\n`,CQ=`\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+jp+`\n  return result;\n`,NQ=qt({opSnippet:SQ,packedOpSnippet:CQ,cpuKernelImpl:BK}),TQ={kernelName:Wa,backendName:\"webgl\",kernelFunc:NQ};function 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Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new tl(c,\"max\",!1);return n.runWebGLProgram(p,[r],r.dtype)}var _Q={kernelName:Ua,backendName:\"webgl\",kernelFunc:$Q};function AQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new wv(p,\"max\",!1);return n.runWebGLProgram(d,[r],r.dtype)}var EQ={kernelName:tp,backendName:\"webgl\",kernelFunc:AQ},RQ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${r};\n          wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${a}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${a} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},DQ=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${p}, ${d});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? 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1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function FQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new wv(d,\"max\",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new DQ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var OQ={kernelName:kg,backendName:\"webgl\",kernelFunc:FQ};function PQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;nu([a,i],\"maxPoolGrad\");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=N.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new tl(d,\"max\",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new RQ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var zQ={kernelName:wg,backendName:\"webgl\",kernelFunc:PQ};function MQ(e,t,n,s){let r=new tl(n,\"max\",!1),a=s.runWebGLProgram(r,[e],\"float32\");r=new tl(n,\"max\",!0,!0,t);let i=s.runWebGLProgram(r,[e],\"float32\");return[a,i]}var LQ={kernelName:Ig,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. 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i < ${s}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${u};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n          }\n        }\n        ${r} coords = outC - start;\n        setOutput(getX(${o}));\n      }\n    `}},XQ=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ot(s),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,f)=>h[0]+e[f]).join(\",\"),o=un(\"rc\",s),u=un(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=n===\"reflect\"?0:1,d=\"\";if(s===1){let h=`\n        ${r} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${p};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${p};\n        }\n        source -= start;\n      `;d=`\n        ${r} rc = outputLoc;\n        ${h}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${o[s-1]} += 1;\n        if(${l}) {\n          ${h}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n      `}else{let h=`\n        ${r} source = rc;\n        ${r} lt = ${r}(lessThan(source, start));\n        ${r} gte = ${r}(greaterThanEqual(source, end));\n        ${r} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${p}) +\n                gte * ((end - 1) * 2 - source + ${p});\n        source -= start;\n      `;d=`\n        ${r} rc = outputLoc;\n        ${h}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${o[s-1]} += 1;\n        if(${l}) {\n          ${h}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n        rc = outputLoc;\n        ${o[s-2]} += 1;\n        if(${o[s-2]} < ${this.outputShape[s-2]}) {\n          ${h}\n          result[2] = getChannel(getX(${u.join()}), ${c});\n          ${o[s-1]} += 1;\n          if(${l}) {\n            ${h}\n            result[3] = getChannel(getX(${u.join()}), ${c});\n          }\n        }\n      `}this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${d}\n        setOutput(result);\n      }\n    `}},YQ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new XQ(s.shape,r,a):new KQ(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},QQ={kernelName:ja,backendName:\"webgl\",kernelFunc:YQ},ZQ=`if (b == 0.0) return NAN;\n  return mod(a, b);`,JQ=`\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  `+jp+`\n  return result;\n`,eZ=qt({opSnippet:ZQ,packedOpSnippet:JQ}),tZ={kernelName:xl,backendName:\"webgl\",kernelFunc:eZ},nZ=class{constructor(e,t,n){this.variableNames=[\"probs\"],this.customUniforms=[{name:\"seed\",type:\"float\"}],this.outputShape=[e,n],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; 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t.makeTensorInfo([o.length],i,o)},LZ={kernelName:kl,backendName:\"webgl\",kernelFunc:s2},BZ=\"return 1.0 / x;\",VZ=Ke({opSnippet:BZ}),WZ={kernelName:Il,backendName:\"webgl\",kernelFunc:VZ},UZ=ts+`\n  return (x < 0.0) ? 0.0 : x;\n`,GZ=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,HZ=Ke({opSnippet:UZ,packedOpSnippet:GZ}),qZ={kernelName:Za,backendName:\"webgl\",kernelFunc:HZ},jZ=ts+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,KZ=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,XZ=Ke({opSnippet:jZ,packedOpSnippet:KZ}),YZ={kernelName:ei,backendName:\"webgl\",kernelFunc:XZ},QZ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n       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sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function JZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new ZZ(r.shape,u,l,a,i):new QZ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var eJ={kernelName:Ja,backendName:\"webgl\",kernelFunc:JZ},tJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function nJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new tJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var sJ={kernelName:Ng,backendName:\"webgl\",kernelFunc:nJ},rJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},aJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new aJ(r.shape,u,l,a,i):new rJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var oJ={kernelName:Sl,backendName:\"webgl\",kernelFunc:iJ},uJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function lJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new uJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var cJ={kernelName:Cg,backendName:\"webgl\",kernelFunc:lJ},dJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `;return}let s=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>s(o)).join(\",\"),a=ot(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}},pJ=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=un(\"rc\",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${r}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:this.userCode=`\n        void main() {\n          ${i} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${o(s.slice())};\n          if(${r}){\n            result.g = ${u(s.slice())};\n          }\n          if(${a}) {\n            result.b = ${l(s.slice())};\n            if(${r}) {\n              result.a = ${c(s.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;function o(h){return p(h)}function u(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",p(h)}function l(h){return h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function c(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(\",\"),g=f.slice(-2).join(\",\");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function hJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return En({inputs:{x:r},backend:n});let u=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new pJ(r.shape,o):new dJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var fJ={kernelName:Ro,backendName:\"webgl\",kernelFunc:hJ},mJ=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=e[1],s=e[2];this.outputShape=e;let r=\"\";typeof t==\"number\"?r=`float outputValue = ${t.toFixed(2)};`:r=`\n        vec3 fill = vec3(${t.join(\",\")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${r}\n          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},gJ={kernelName:qo,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new mJ(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},bJ=`\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`,yJ=Ke({opSnippet:bJ}),vJ={kernelName:Do,backendName:\"webgl\",kernelFunc:yJ},xJ=\"return inversesqrt(x);\",wJ=Ke({opSnippet:xJ,cpuKernelImpl:jK}),kJ={kernelName:ti,backendName:\"webgl\",kernelFunc:wJ},r2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\");let c=`getIndices(${l})`,p=\"\";s===1?p=\"i\":s===2&&(p=\"i, coords[1]\");let d=`getUpdates(${p})`,h=t>1?\"strides[j]\":\"strides\";this.userCode=`\n        ${o} strides = ${o}(${r});\n\n        void main() {\n          ${u} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${c});\n              flattenedIndex += index * ${h};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${d};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}};function IJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new r2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var SJ={kernelName:Fo,backendName:\"webgl\",kernelFunc:IJ},CJ=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r=\"resRC\",s=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${r}));\n        } else {\n          setOutput(getB(${r}));\n        }\n      }\n    `}};function NJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new CJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var TJ={kernelName:Oo,backendName:\"webgl\",kernelFunc:NJ},$J=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${N.SELU_SCALEALPHA};\n  float scale = ${N.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,_J=Ke({opSnippet:$J}),AJ={kernelName:Cl,backendName:\"webgl\",kernelFunc:_J},EJ=ou+`\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`,RJ=`\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,DJ=Ke({opSnippet:EJ,packedOpSnippet:RJ,cpuKernelImpl:KK}),FJ={kernelName:si,backendName:\"webgl\",kernelFunc:DJ},OJ=`\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`,PJ=Ke({opSnippet:OJ}),zJ={kernelName:Nl,backendName:\"webgl\",kernelFunc:PJ},MJ=ou+`\n  return sin(x);\n`,LJ=Ke({opSnippet:MJ}),BJ={kernelName:ni,backendName:\"webgl\",kernelFunc:LJ},VJ=`\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:zo,backendName:\"webgl\",kernelFunc:WJ},GJ=`\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`,HJ=Ke({opSnippet:GJ}),qJ={kernelName:Tl,backendName:\"webgl\",kernelFunc:HJ},jJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=n2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},KJ={kernelName:Mo,backendName:\"webgl\",kernelFunc:jJ};function XJ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n         ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n         ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:\n         ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=YK(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var YJ={kernelName:sp,backendName:\"webgl\",kernelFunc:XJ};function QJ(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=QK(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var ZJ={kernelName:$l,backendName:\"webgl\",kernelFunc:QJ};function JJ(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var eee={kernelName:rp,backendName:\"webgl\",kernelFunc:JJ};function tee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n             ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var nee={kernelName:ap,backendName:\"webgl\",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=new r2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var ree={kernelName:ip,backendName:\"webgl\",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=uu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var iee={kernelName:Lo,backendName:\"webgl\",kernelFunc:aee},kw=\"return sqrt(x);\",oee=Ke({opSnippet:kw,packedOpSnippet:kw,cpuKernelImpl:ZK}),uee={kernelName:ri,backendName:\"webgl\",kernelFunc:oee},lee=\"return x * x;\",cee=Ke({opSnippet:lee}),dee={kernelName:_l,backendName:\"webgl\",kernelFunc:cee},Iw=\"return (a - b) * (a - b);\",pee=qt({opSnippet:Iw,packedOpSnippet:Iw}),hee={kernelName:oi,backendName:\"webgl\",kernelFunc:pee};function fee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ts+`\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var mee={kernelName:di,backendName:\"webgl\",kernelFunc:fee},gee=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=n;let s=n.length,r=ot(n.length),a=ot(n.length),i=\"\";if(s===1)i=\"coords * strides + begin\";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(\",\")}this.userCode=`\n      ${r} begin = ${r}(${e});\n      ${r} strides = ${r}(${t});\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}};function bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),E=uu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=JK(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new gee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let C=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),C}var yee={kernelName:Bo,backendName:\"webgl\",kernelFunc:bee};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=eX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var xee={kernelName:op,backendName:\"webgl\",kernelFunc:vee};function wee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=tX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],\"int32\",l),n.makeTensorInfo([d],\"string\",c),n.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var kee={kernelName:Tg,backendName:\"webgl\",kernelFunc:wee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(r<=0)throw new Error(\"Number of buckets must be at least 1\");let i=n.readSync(a.dataId),o=nX(i,r);return n.makeTensorInfo(a.shape,\"int32\",o)}var See={kernelName:$g,backendName:\"webgl\",kernelFunc:Iee},Cee=\"return 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u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Eee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var Dee={kernelName:Sr,backendName:\"webgl\",kernelFunc:a2},Fee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `}},Oee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `}};function Wr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Sw(e){let t=1;for(;t<e;)t*=2;return t}function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=X().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=aX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Wr(n,h);let b=Sw(a),y=Sw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new Fee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,\"int32\",q),Wr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Oee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,\"int32\",z),Wr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let C=v;v=uu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Wr(n,C);let T=Y1({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Wr(n,g);let E=l.slice(0,-1);E.push(a),C=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Wr(n,C);let A=T;return T=he({inputs:{x:T},attrs:{shape:E},backend:n}),Wr(n,A),[T,v]}var zee={kernelName:Wo,backendName:\"webgl\",kernelFunc:Pee},Mee=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n       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1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Lee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Mee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Bee={kernelName:Uo,backendName:\"webgl\",kernelFunc:Lee};function Vee(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nu(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=iX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var Wee={kernelName:_g,backendName:\"webgl\",kernelFunc:Vee};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=uu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Gee={kernelName:Go,backendName:\"webgl\",kernelFunc:Uee},Hee=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=N.getAxesPermutation([l],o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=N.getInnerMostAxes(1,o)[0]);let d=N.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,C,T,E)=>{let A=x.shape[0],P=x.shape[1],R=N.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new Hee(F,k),z=n.compileAndRun($,[x,C],T);if(u.push(z),z.shape[1]===E)return z;let W=s2({backend:n,attrs:{start:0,stop:E,step:1,dtype:\"float32\"}}),q=a2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,T,E)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=N.getUndoAxesPermutation(c);v=cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jee={kernelName:up,backendName:\"webgl\",kernelFunc:qee},Kee=[e8,n8,a8,u8,c8,h8,m8,b8,w8,I8,N8,_8,R8,P8,L8,V8,U8,j8,X8,Q8,tY,uY,cY,pY,yY,xY,SY,OX,TY,RY,PY,WY,GY,qY,KY,YY,JY,n7,a7,o7,l7,p7,f7,y7,x7,I7,N7,$7,R7,P7,B7,U7,q7,j7,X7,Q7,J7,t9,s9,o9,c9,h9,m9,y9,w9,C9,_9,FX,E9,AY,F9,z9,B9,zX,G9,K9,Y9,eQ,sQ,oQ,cQ,fQ,yQ,wQ,IQ,TQ,_Q,EQ,OQ,zQ,LQ,VQ,UQ,jQ,QQ,tZ,lZ,WX,hZ,gZ,vZ,kZ,fY,CZ,TZ,_Z,RZ,PZ,LX,MZ,LZ,mY,aZ,WZ,qZ,YZ,GX,eJ,sJ,oJ,cJ,fJ,gJ,vJ,kJ,SJ,TJ,AJ,FJ,zJ,BJ,UJ,iY,oZ,qJ,KJ,YJ,ZJ,eee,nee,ree,iee,uee,dee,hee,mee,yee,xee,kee,See,iZ,QX,Tee,Aee,Dee,zee,Bee,ZX,Wee,Gee,jee,NZ];for(let e of Kee)Al(e);var zs=X();zs.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);zs.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);zs.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D\",()=>!1);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);zs.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);zs.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);zs.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);zs.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);zs.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);function Xee(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function kv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Rr(){return`\n  ${kv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${Rr()}\n      let index = getGlobalIndex();\n`}function Yee(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `),s===!0)return r.push(`\n      struct Matrix0 {\n        numbers: array<${cd(t.dtype,n.isVec4)}>;\n      };\n      struct Uniform {\n        size            : i32;\n        numChannels     : i32;\n        outShapeStrides : vec2<i32>;\n        dispatchSize    : vec3<u32>;\n      };\n\n      @group(0) @binding(0) var<storage, write> result : Matrix0;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Cw,r.join(`\n`),Nw(t.shape),n.getUserCode()].join(`\n`);let a=\"struct Uniforms { NAN : f32; \";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}; `}),a+=`outShape : ${Wt(t.shape.length)} ; `;let i=t.shape.length-1;a+=`\n       outShapeStrides: ${Wt(i)}; `,n.size&&(a+=\"size : i32; \"),n.uniforms&&(a+=n.uniforms),a+=\"};\",r.push(a),n.atomic?r.push(`\n    struct Matrix0 {\n        numbers: array<atomic<i32>>;\n    };\n\n    @group(0) @binding(0) var<storage, read_write> result : Matrix0;\n  `):r.push(`\n    struct Matrix0 {\n        numbers: array<${cd(t.dtype,n.isVec4)}>;\n    };\n\n    @group(0) @binding(0) var<storage, write> result : Matrix0;\n  `),n.variableNames.forEach((p,d)=>{r.push(`\n    struct Matrix${1+d} {\n      numbers: array<${cd(e[d].dtype,n.isVec4)}>;\n    };\n    @group(0) @binding(${1+d}) var<storage, read> ${p} : Matrix${1+d};\n    `)}),a!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms : Uniforms;\n    `);let[o,u]=nte(t.shape,n.dispatchLayout),l=[Cw,r.join(`\n`),Nw(t.shape),o,Qee(t.shape.length)];if(n.atomic||l.push(Zee(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Jee(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);l.push(p)}return l.push(n.getUserCode()),l.join(`\n`)}var Cw=`\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  fn isNanCustom(val : f32) -> bool {\n    if (val > 0.0) {\n      return false;\n    }\n    if (val < 0.0) {\n      return false;\n    }\n    if (val == 0.0) {\n      return false;\n    }\n    return true;\n  }\n  fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));\n  }\n`;function Qee(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Zee(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result.numbers[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result.numbers[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result.numbers[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result.numbers[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function Jee(e,t,n,s){let r=ete(e,n);return e.shape.length<=t.length&&(r+=tte(e,t,n,s)),r}function ete(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}.numbers[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}.numbers[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function tte(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}.numbers[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}.numbers[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}.numbers[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}.numbers[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=N.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords[${g+p}] = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function nte(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Xee(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Nw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join(\"\");return`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var i2={};Ae(i2,{ArrayBufferToTypedArray:()=>u2,GPUBytesPerElement:()=>qm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Iv,computeWorkGroupSizeForMatMul:()=>o2,computeWorkPerThreadForConv2d:()=>Sv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Cv,tilesFitEvenlyIntoShape:()=>Ks});var Mi=65535,ea=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ea(e.x.map(u=>t[u]))/(n[0]*s[0])),e.y?Math.ceil(ea(e.y.map(u=>t[u]))/(n[1]*s[1])):1,e.z?Math.ceil(ea(e.z.map(u=>t[u]))/(n[2]*s[2])):1];if(r<=Mi&&a<=Mi&&i<=Mi)return[r,a,i];w.assert(r>Mi&&e.y===void 0&&e.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let o=Math.ceil(Math.sqrt(r));return o>Mi?(o=Math.ceil(Math.cbrt(r)),w.assert(o<=Mi,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[o,o,o]):[o,o,1]}function Iv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function o2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Sv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function qm(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function u2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Cv(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}var ste=\"return a + b;\",rte=\"return areal * breal - aimag * bimag;\",ate=\"return areal * bimag + aimag * breal;\",ite=\"return a / b;\",ote=\"return a * b;\",ute=\"return (a - b) * (a - b);\",lte=\"return a - b;\",cte=\"return f32(a == b);\",dte=\"return vec4<f32>(a == b);\",pte=\"return f32(a > b);\",hte=\"return vec4<f32>(a > b);\",fte=\"return f32(a >= b);\",mte=\"return vec4<f32>(a >= b);\",gte=\"return f32(a < b);\",bte=\"return vec4<f32>(a < b);\",yte=\"return f32(a <= b);\",vte=\"return vec4<f32>(a <= b);\",xte=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",wte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,kte=`\n  if (isNanCustom(a)) { return a; }\n  if (isNanCustom(b)) { return b; }\n  `,l2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,Ite=`\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `,Ste=`\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `,Cte=\"return f32(a != b);\",Nte=\"return vec4<f32>(a != b);\",Tte=`\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `,$te=`\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${l2}\n  return resultTemp;\n  `,_te=\"if (a < 0.0) { return b * a; }  return a;\",Ate=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Tw(e,t){let n=t?l2:kte;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function tc(e,t){switch(e){case 0:return ote;case 1:return ste;case 2:return lte;case 3:return ite;case 4:return t?dte:cte;case 5:return t?hte:pte;case 6:return t?mte:fte;case 7:return t?bte:gte;case 8:return t?vte:yte;case 9:return t?wte:xte;case 10:return t?Nte:Cte;case 11:return ute;case 12:return t?Ste:Ite;case 14:return t?Ate:_te;case 15:return Tw(\"max\",t);case 16:return Tw(\"min\",t);case 13:return t?$te:Tte;case 17:return rte;case 18:return ate;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Ete=\"return abs(a);\",Rte=\"return ceil(a);\",Dte=\"return cos(a);\",Fte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Ote=\"return exp(a) - 1.0;\",Pte=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",zte=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Mte=\"return exp(a);\",Lte=\"return floor(a);\",Bte=\"return a;\",Vte=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,Wte=\"return f32(!(a >= 1.0));\",Ute=\"return -a;\",Gte=\"return (a < 0.0) ? b * a : a;\",Hte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",qte=\"if(a < 0.0) { return 0.0; } return a;\",jte=\"return clamp(a, 0.0, 6.0);\",Kte=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",Xte=`\n  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));\n  let isNaN = isNanCustomVec4(a);\n\n  if (isNaN.r) {\n    resFloat.r = a.r;\n  }\n  if (isNaN.g) {\n    resFloat.g = a.g;\n  }\n  if (isNaN.b) {\n    resFloat.b = a.b;\n  }\n  if (isNaN.a) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Yte=\"return 1.0/sqrt(a);\",Qte=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Zte=\"return sin(a);\",Jte=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,ene=\"return sqrt(a);\",tne=\"return a * a;\",nne=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,sne=\"return f32(i32((a)));\";function Gi(e,t){switch(e){case 0:return Ete;case 2:return Dte;case 3:return Fte;case 1:return Rte;case 4:return t?zte:Pte;case 5:return Mte;case 6:return Ote;case 7:return Lte;case 8:return Bte;case 9:return Vte;case 10:return Wte;case 11:return Ute;case 12:return Gte;case 15:return Hte;case 13:return t?Xte:qte;case 14:return t?Kte:jte;case 16:return Yte;case 19:return Qte;case 17:return Zte;case 18:return Jte;case 20:return ene;case 21:return tne;case 22:return nne;case 23:return sne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e===\"linear\")return Gi(8);if(e===\"relu\")return Gi(13,t);if(e===\"elu\")return Gi(4,t);if(e===\"relu6\")return Gi(14,t);if(e===\"prelu\")return tc(14,t);if(e===\"sigmoid\")return Gi(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function c2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>\"tileInner must be divisible by 4. And ColPerThread must be 4\"),`\n  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let TileInner = ${s};\n\n  ${Rr()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var ACached : vec4<f32>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / ColPerThread;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {\n            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];\n            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];\n            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];\n            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                acc[i] = BCached[3] * ACached.w + acc[i];\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var rne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}};function Nv(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`\n    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;\n    ${Rr()}\n      let tileRow = i32(localId.y) * ${e[1]};\n      let tileCol = i32(localId.x) * ${e[0]};\n\n      let globalRow = i32(globalId.y) * ${e[1]};\n      let globalCol = i32(globalId.x) * ${e[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;\n\n      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${e[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${r} / ${t[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${r} / ${t[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${r} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${r} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${r}; k = k + 1) {\n          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}function ane(e){return`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${Rr()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var d2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=o2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?\"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`:e=this.fitA?\"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?\"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`:t=this.fitB?\"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?Nv([this.workPerThread,this.workPerThread,1],this.workGroupSize):ane(this.workGroupSize)}\n    `}};function ine(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${Rr()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var one=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e=\"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\":e=\"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];\";let t;this.transposeB===!1?t=\"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        ${e}\n      }\n\n      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${ine()}\n    `}};function une(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${Rr()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var lne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${une(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var cne={kernelName:Eo,backendName:\"webgpu\",kernelFunc:Me};function Tv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new one([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new lne(k,C,[P,h,f],a,u,i):R?F=new rne(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),a,u,i):F=new d2(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),n,s,a,u,i);let $=[T,E];a&&$.push(a),i&&$.push(i);let z=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}],W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function dne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Tv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var pne={kernelName:na,backendName:\"webgpu\",kernelFunc:dne},$w=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},hne=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${tc(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}.numbers[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},fne=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${tc(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},p2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${tc(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function _w(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new fne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new hne(e,t,n,a):new p2(e,t,n)}function Zn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var mne={kernelName:Ma,backendName:\"webgpu\",kernelFunc:Zn};function lu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=Zn({inputs:{x:s},backend:n}),u=Zn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var gne={kernelName:jd,backendName:\"webgpu\",kernelFunc:lu},nc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${Gi(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function fn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=_w(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],yn(b.dtype,y.dtype))});else{let g=new $w(17,i.shape,o.shape),b=new $w(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=lu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||yn(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?N.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?N.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=_w(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:bne,ceilImpl:yne,concatImpl:vne,equalImpl:xne,expImpl:wne,expm1Impl:kne,floorImpl:Ine,gatherNdImpl:Sne,gatherV2Impl:Cne,greaterEqualImpl:Nne,greaterImpl:Tne,lessEqualImpl:$ne,lessImpl:_ne,logImpl:Ane,maxImpl:Ene,maximumImpl:Rne,minimumImpl:Dne,multiplyImpl:Fne,negImpl:One,notEqualImpl:Pne,prodImpl:zne,rangeImpl:Mne,rsqrtImpl:Lne,simpleAbsImpl:Bne,sliceImpl:Vne,stridedSliceImpl:Wne,stringNGramsImpl:Une,subImpl:Gne,tileImpl:Hne,topKImpl:qne,transposeImpl:jne,uniqueImpl:kpe}=Qy,Kne=jt({opType:0,cpuKernelImpl:Bne}),Xne={kernelName:io,backendName:\"webgpu\",kernelFunc:Kne},Yne=fn({opSnippet:1,cpuKernelImpl:bne,supportsComplex:!0}),Qne={kernelName:kr,backendName:\"webgpu\",kernelFunc:Yne},Zne=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function Jne(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Zn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>yn(o,u)),a=s.map(o=>o.shape),i=new Zne(a);return n.runWebGPUProgram(i,s,r)}var ese={kernelName:xa,backendName:\"webgpu\",kernelFunc:Jne},h2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"axis : i32; infinityValue : f32;\",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape[${r}]`;return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      // In order to get a flattened index into the input tensor, we need to\n      // add back the index along the reduced dimension to |outputCoords|.\n      // This function outputs the offset to the first value along\n      // |axis| and the stride to get the next value of the input along |axis|.\n      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        var i = ${this.outputShape.length-1};\n\n        var stride = 1;\n        var inputStride = 1;\n        var offset = 0;\n\n        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {\n          let length = ${n(`${this.inputShape.length} - r`)};\n          if (${this.inputShape.length} - r == uniforms.axis) {\n            inputStride = stride;\n          } else {\n            offset = offset + ${t(\"outputCoords\",\"i\")} * stride;\n            i = i - 1;\n          }\n          stride = stride * length;\n        }\n\n        return vec2<i32>(offset, inputStride);\n      }\n\n      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{\n        return coordInfo[0] + coordInfo[1] * index;\n      }\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let coordInfo = getInputCoordInfo(outputIndex);\n        let Length = ${n(\"uniforms.axis\")};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n\n        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);\n          if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(Length), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},tse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${kv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] =\n              A.numbers[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},nse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=sse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function sse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=jne(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new tse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new nse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var rse={kernelName:ci,backendName:\"webgpu\",kernelFunc:vi};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],\"max\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var ise={kernelName:wa,backendName:\"webgpu\",kernelFunc:ase};function ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],\"min\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var use={kernelName:ol,backendName:\"webgpu\",kernelFunc:ose},f2=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;\",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},m2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>;\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new m2(c):(p=new f2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var cse={kernelName:ka,backendName:\"webgpu\",kernelFunc:lse};function dse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Tv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var pse={kernelName:Ia,backendName:\"webgpu\",kernelFunc:dse},hse=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}; `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=fse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${jm[a]} = uniforms.start[${a}] + coords.${jm[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},jm=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function fse(e){if(e===1)return\"sourceLoc\";if(e<=6)return jm.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=Vne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new hse(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var mse={kernelName:Po,backendName:\"webgpu\",kernelFunc:cu},gse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},bse={kernelName:oo,backendName:\"webgpu\",kernelFunc:gse},g2=fn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Pne}),yse={kernelName:So,backendName:\"webgpu\",kernelFunc:g2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.real},backend:n})}var vse={kernelName:np,backendName:\"webgpu\",kernelFunc:sc};function xse(e,t){let n=new nc(e.shape,23),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Km(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Zn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Km({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=lu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=sc({inputs:{input:r},backend:n}),o=Km({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Zn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return xse(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=g2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var wse={kernelName:Sa,backendName:\"webgpu\",kernelFunc:Km},kse=jt({opType:1,cpuKernelImpl:yne}),Ise={kernelName:Ca,backendName:\"webgpu\",kernelFunc:kse},Sse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32; maxVal : f32;\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isNanCustom(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},Cse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32; maxVal : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isNanCustom(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Nse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new Sse(r.shape):o=new Cse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Tse={kernelName:Ir,backendName:\"webgpu\",kernelFunc:Nse},$se=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var _se={kernelName:Zd,backendName:\"webgpu\",kernelFunc:Zp};function Xm(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Xm(h,t,n),g=Xm(f,t,n),b=lu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=vne(f,m,s,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Ase(e,t,n),o=a.map(h=>h.shape),u=new $se(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Ase(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function b2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Zn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return N.assertParamsConsistent(u,a),Xm(o,a,n)}var Ese={kernelName:uo,backendName:\"webgpu\",kernelFunc:b2},Rse=class{constructor(e,t){this.variableNames=[\"A\"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;\n      inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`\n    ${Ue()}\n\n      for(var i = 0; i<${this.workPerThread}; i = i + 1) {\n        let flatIndex = index * ${this.workPerThread} + i;\n\n        let rc = getCoordsFromIndex(flatIndex);\n\n        if(flatIndex < uniforms.size) {\n          let blockIndex = rc[0];\n          let pos = rc[1];\n\n          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];\n          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;\n          var value = 0.0;\n          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {\n            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -\n              uniforms.pad[0];\n            let d1 = offsetX + uniforms.dilation[0] * ((pos %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n            let ch = pos % uniforms.inChannels;\n            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {\n              value = getA(d0, d1, ch);\n            }\n          }\n          setOutputAtIndex(flatIndex, value);\n        }\n      }\n    }\n  `}},Dse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;\n      dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.hasLeakyreluAlpha&&this.variableNames.push(\"leakyreluAlpha\"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);\n    let divBy4Remainder${e} = flatIndex${e} % 4;\n    let divBy4Index${e} = flatIndex${e} / 4;\n    let curData${e} = x.numbers[divBy4Index${e}];\n    if (divBy4Remainder${e} == 0) {\n      temp = curData${e};\n    } else {\n      // TODO: This could end up being a redundant load with another one in\n      // the same shader invocation. Perhaps there's an opportunity for\n      // optimization\n      let nextData${e} = x.numbers[divBy4Index${e} + 1];\n      if (divBy4Remainder${e} == 1) {\n        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);\n      } else if (divBy4Remainder${e} == 2) {\n        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);\n      } else if (divBy4Remainder${e} == 3) {\n        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);\n      }\n    }\n    `}getUserCode(){let e=c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        var coord = vec4<i32>(\n            batch,\n            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n            inChCoord);\n        var resData = vec4<f32>(0.0);\n        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for\n          // the 'same' padding type.\n          if (coordsInBounds4D(coord, uniforms.xShape)) {\n            resData = x.numbers[getIndexFromCoords4D(coord, uniforms.xShape) / 4];\n          } else {\n            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);\n          ${this.getSampleAWithRemainder(1)}\n          resData = temp;\n          if (WCol == (uniforms.filterDims[1] - 1)) {\n            coord = vec4<i32>(\n              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);\n              ${this.getSampleAWithRemainder(2)}\n            if (inChCoord == 0) {\n              resData = vec4<f32>(resData.xyz, temp.x);\n            } else if (inChCoord == 1) {\n              resData = vec4<f32>(resData.xy, temp.xy);\n            } else {\n              resData = vec4<f32>(resData.x, temp.xyz);\n            }\n          }\n          `}\n        return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${s}\n         }\n         return vec4<f32>(0.0);\n        `,a=this.fitB?\"return W.numbers[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W.numbers[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,i=\"\",o=\"\";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${c}\n        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {\n          let b = getLeakyreluAlphaByOutputCoords(outCoord);\n          ${c}\n        }`,new Error(\"Leakyrelu is not supported.\");i=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${c}\n        }`}o=\"value = activation(value, outCoord);\"}let u=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${i}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          let r = row;\n          let c = col * 4;\n          var batch = i32(globalId.z);\n          ${r}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${a}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${u}\n            ${o}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},Fse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Iv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Sv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Nv(this.elementsPerThread,this.workGroupSize),t=`\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);\n    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];\n    let coord = vec4<i32>(\n        batch,\n        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n        col % uniforms.xShape[3]);\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${t}\n    }\n    return 0.0;\n    `,s=this.fitB?\"return W.numbers[row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W.numbers[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `,r=\"\",a=\"\";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${u}\n                }`:r=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${u}\n                  }\n                `,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${r}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${n}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${s}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      ${i}\n      ${a}\n      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${e}\n  `}},Ose=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;\",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${r}\n             }`:e=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n                    ${r}\n                  }\n                `,t=\"value = activation(value, outCoord);\"}let n=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${e}\n      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if(coordsInBounds4D(coord, uniforms.xShape)) {\n          return getX(batch, row, col, chan);\n        }\n        return 0.0;\n      }\n\n      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n        let coord = vec4<i32>(row, col, xChannel, outChannel);\n        if(coordsInBounds4D(coord, uniforms.wShape)) {\n          return getW(row, col, xChannel, outChannel);\n        }\n        return 0.0;\n      }\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          ${n}\n          ${t}\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Rr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let outChannel = coords[3];\n\n        var acc = 0.0;\n\n        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {\n              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n              let v = readInp(batch, coordRow, coordCol, xChannel);\n              let f = readFilt(row, col, xChannel, outChannel);\n              acc = acc + v * f;\n            }\n          }\n        }\n\n        writeResult(batch, coords[1], coords[2], outChannel, acc);\n      }\n    `}};function Pse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat===\"channelsLast\",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Tv({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function zse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y===\"channelsLast\",x=u*l*c,k=m*f,C=[k,x],T=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Rse(C,v),$=[{type:\"int32\",data:[h.left,h.top]},{type:\"int32\",data:[p,d]},{type:\"int32\",data:[g,b]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*u]},{type:\"int32\",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,C[0],C[1]]}});A.push(z),A.push(W);let q=[1,C[0],C[1]],K=new d2(q,[1,k,n.outChannels],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),T,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:\"int32\",data:[Y]},{type:\"int32\",data:[te]},{type:\"int32\",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a);let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function y2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Pse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")&&e.shape[0]===1)return zse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool(\"WEBGPU_USE_NAIVE_CONV2D\"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type===\"VALID\")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...f]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new Ose(n,u,o,l);else{h?c=new Dse(n,u,o,l):c=new Fse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:\"int32\",data:[b]},{type:\"int32\",data:[y]},{type:\"int32\",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),s.runWebGPUProgram(c,g,e.dtype,m)}function Mse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return y2({x:r,filter:a,convInfo:d,backend:s})}var Lse={kernelName:Na,backendName:\"webgpu\",kernelFunc:Mse},Bse=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Iv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Sv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W.numbers[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${Nv(this.elementsPerThread,this.workGroupSize)}\n  `}},Vse=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function Wse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new Vse(d);else{f=new Bse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var Use={kernelName:Ta,backendName:\"webgpu\",kernelFunc:Wse},Gse=jt({opType:2}),Hse={kernelName:$a,backendName:\"webgpu\",kernelFunc:Gse},qse=jt({opType:3}),jse={kernelName:_a,backendName:\"webgpu\",kernelFunc:qse},Kse=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32;\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},Xse=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Kse(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},Yse={kernelName:co,backendName:\"webgpu\",kernelFunc:Xse},Qse=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32;\",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new Qse(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Jse={kernelName:po,backendName:\"webgpu\",kernelFunc:Zse},v2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${kv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},x2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;\n      inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;\n      channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Rr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function ere(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.filterHeight===3&&p.inChannels%4===0?h=new v2(p):(h=new x2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var tre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:ere},w2=fn({opSnippet:0,cpuKernelImpl:Fne,supportsComplex:!0}),nre={kernelName:Ka,backendName:\"webgpu\",kernelFunc:w2},sre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32;\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isNanCustom(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isNanCustom(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x.numbers[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x.numbers[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=vi({inputs:{x:e},attrs:{perm:l},backend:r}),u=N.getInnerMostAxes(u.length,a),i.push(c)),N.assertAxesAreInnerMostDims(s,u,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=N.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Ene(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=zne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":cp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new sre(y,s),C=r.runWebGPUProgram(k,[c],v,x);i.push(C),f=Me({inputs:{x:C},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function $v(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"sum\",n)}var rre={kernelName:ai,backendName:\"webgpu\",kernelFunc:$v};function are(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=vi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=w2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=$v({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var ire={kernelName:Qd,backendName:\"webgpu\",kernelFunc:are},ore=jt({opType:4}),ure={kernelName:Ra,backendName:\"webgpu\",kernelFunc:ore},lre=fn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:xne}),cre={kernelName:ho,backendName:\"webgpu\",kernelFunc:lre},k2=jt({opType:5,cpuKernelImpl:wne,dtype:\"float32\"}),dre={kernelName:Da,backendName:\"webgpu\",kernelFunc:k2};function Ym(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var pre={kernelName:fo,backendName:\"webgpu\",kernelFunc:Ym},hre=jt({opType:6,cpuKernelImpl:kne}),fre={kernelName:mo,backendName:\"webgpu\",kernelFunc:hre},mre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function du(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new mre(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var gre={kernelName:fl,backendName:\"webgpu\",kernelFunc:du},bre=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},yre={kernelName:go,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bre(n.shape);return 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this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Tre={kernelName:Pa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Nre(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function 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Ere={kernelName:ra,backendName:\"webgpu\",kernelFunc:Are},Rre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${Wt(e)};`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function Dre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Sne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Rre(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Fre={kernelName:yo,backendName:\"webgpu\",kernelFunc:Dre},Ore=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Pre(this.aShape,\"i32\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Pre(e,t=\"int\"){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),C=n.tensorMap.get(d.dataId).values,T=De(d.shape,d.dtype,C),E=Cne(T,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Ore(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var zre={kernelName:bo,backendName:\"webgpu\",kernelFunc:C2},Mre=fn({opSnippet:5,cpuKernelImpl:Tne,dtype:\"bool\"}),Lre={kernelName:vo,backendName:\"webgpu\",kernelFunc:Mre},Bre=fn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:Nne}),Vre={kernelName:za,backendName:\"webgpu\",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new nc(r.shape,15);return o.uniforms=\"alpha : f32;\",n.runWebGPUProgram(o,[r],\"float32\",i)}var Ure={kernelName:La,backendName:\"webgpu\",kernelFunc:Wre},Gre=fn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:_ne}),Hre={kernelName:xo,backendName:\"webgpu\",kernelFunc:Gre},qre=fn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:$ne}),jre={kernelName:wo,backendName:\"webgpu\",kernelFunc:qre},Kre=jt({opType:9,cpuKernelImpl:Ane}),Xre={kernelName:Ba,backendName:\"webgpu\",kernelFunc:Kre},Yre=fn({opSnippet:9,dtype:\"bool\"}),Qre={kernelName:ko,backendName:\"webgpu\",kernelFunc:Yre},Zre=jt({opType:10}),Jre={kernelName:vl,backendName:\"webgpu\",kernelFunc:Zre};function N2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,\"max\",n)}var eae={kernelName:Va,backendName:\"webgpu\",kernelFunc:N2},tae=fn({opSnippet:15,cpuKernelImpl:Rne}),nae={kernelName:Wa,backendName:\"webgpu\",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});p=new m2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new f2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var rae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:sae};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,\"mean\",n)}var iae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:aae};function oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"min\",n)}var uae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:oae},lae=fn({opSnippet:16,cpuKernelImpl:Dne}),cae={kernelName:qa,backendName:\"webgpu\",kernelFunc:lae},dae=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + 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i.runWebGPUProgram(u,[s],s.dtype,o)}};function hae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=One(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var fae={kernelName:Io,backendName:\"webgpu\",kernelFunc:hae};function mae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var gae={kernelName:Co,backendName:\"webgpu\",kernelFunc:mae};function bae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var yae={kernelName:No,backendName:\"webgpu\",kernelFunc:bae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var vae={kernelName:Ho,backendName:\"webgpu\",kernelFunc:Ld};function T2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=sc({inputs:{input:s},backend:n}),a=T2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xae={kernelName:To,backendName:\"webgpu\",kernelFunc:T2};function wae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Ym({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=Ym({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=b2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var kae={kernelName:_o,backendName:\"webgpu\",kernelFunc:wae},Iae=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${o}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},$2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Zn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return du({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:\"float32\",data:[i]}];a.map(l=>o.push({type:\"int32\",data:[l[0],l[1]]}));let u=new Iae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Sae={kernelName:Xa,backendName:\"webgpu\",kernelFunc:$2},Cae=fn({opSnippet:13}),Nae={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new p2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var $ae={kernelName:Qa,backendName:\"webgpu\",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,\"prod\",n)}var Aae={kernelName:Ao,backendName:\"webgpu\",kernelFunc:_ae},Eae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Mne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Rae={kernelName:kl,backendName:\"webgpu\",kernelFunc:Eae},_2=fn({opSnippet:3}),Dae={kernelName:Ea,backendName:\"webgpu\",kernelFunc:_2},Fae=jt({opType:13}),Oae={kernelName:Za,backendName:\"webgpu\",kernelFunc:Fae},Pae=jt({opType:14}),zae={kernelName:ei,backendName:\"webgpu\",kernelFunc:Pae},Mae=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Lae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new Mae(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var Bae={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Lae},Vae=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>; roundBase : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new Vae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Uae={kernelName:Sl,backendName:\"webgpu\",kernelFunc:Wae},Gae=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;\n          cosRadians : f32;`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32;\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>;\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},Hae={kernelName:qo,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Gae(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},qae=jt({opType:16,cpuKernelImpl:Lne}),jae={kernelName:ti,backendName:\"webgpu\",kernelFunc:qae},Kae=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result.numbers[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=du({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new Kae(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Yae={kernelName:Fo,backendName:\"webgpu\",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function Zae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Qae(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var Jae={kernelName:Oo,backendName:\"webgpu\",kernelFunc:Zae},eie=jt({opType:19}),tie={kernelName:si,backendName:\"webgpu\",kernelFunc:eie},nie=jt({opType:17}),sie={kernelName:ni,backendName:\"webgpu\",kernelFunc:nie},rie=jt({opType:18}),aie={kernelName:zo,backendName:\"webgpu\",kernelFunc:rie},A2=fn({opSnippet:2,cpuKernelImpl:Gne,supportsComplex:!0}),iie={kernelName:ui,backendName:\"webgpu\",kernelFunc:A2};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=N2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=A2({inputs:{a:r,b:l},backend:n}),p=k2({inputs:{x:c},backend:n}),d=$v({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=_2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var uie={kernelName:ii,backendName:\"webgpu\",kernelFunc:oie},lie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=$2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},cie={kernelName:Mo,backendName:\"webgpu\",kernelFunc:lie},die=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:c}],f=new die(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var hie={kernelName:ip,backendName:\"webgpu\",kernelFunc:pie};function fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var mie={kernelName:Lo,backendName:\"webgpu\",kernelFunc:fie},gie=jt({opType:20}),bie={kernelName:ri,backendName:\"webgpu\",kernelFunc:gie},yie={kernelName:_l,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,21);return s.runWebGPUProgram(r,[n],n.dtype)}},vie=fn({opSnippet:11}),xie={kernelName:oi,backendName:\"webgpu\",kernelFunc:vie},wie=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=wt.computeOutShape(y,v,x),T=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});k=Me({inputs:{x:T},backend:n,attrs:{shape:f}}),n.disposeData(T.dataId)}else if(n.shouldExecuteOnCPU([r])){let T=n.readSync(r.dataId),E=De(r.shape,r.dtype,T),A=Wne(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let T=new wie(h),E=[{type:\"int32\",data:y},{type:\"int32\",data:x}],A=n.runWebGPUProgram(T,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Iie={kernelName:Bo,backendName:\"webgpu\",kernelFunc:kie};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Une(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var Cie={kernelName:op,backendName:\"webgpu\",kernelFunc:Sie},Nie=jt({opType:22}),Tie={kernelName:li,backendName:\"webgpu\",kernelFunc:Nie},$ie=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=_ie(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function _ie(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Aie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=Hne(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new $ie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Eie={kernelName:Sr,backendName:\"webgpu\",kernelFunc:Aie},Rie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;\n        dir : i32; inc : i32;`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},Die=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32; firstPass : i32; k : i32;\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Bi(e,t){t!==null&&e.disposeData(t.dataId)}function Ew(e){let t=1;for(;t<e;)t*=2;return t}function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[C,T]=qne(k,o,r.dtype,a,i);return[n.makeTensorInfo(C.shape,C.dtype,C.values),n.makeTensorInfo(T.shape,T.dtype,T.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,du({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Ew(a),h=Ew(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,C,T)=>{let E=m(),A=new Rie(T),R=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[C]}],F=f;f=n.runWebGPUProgram(A,E,\"int32\",R),Bi(n,F)};for(let k=1;k<d;k*=2){let C=k*2;for(let T=k;T>=1;T/=2)g(C,T,[c,h])}for(let k=h;k>d;k/=2){let C=m(),T=new Die([c,k/2]),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram(T,C,\"int32\",A),Bi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=cu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Bi(n,b);let y=C2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Bi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Bi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Bi(n,x),[y,f]}var Oie={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Fie},Pie=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32; fillModeId : i32; fillValue : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Pie(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Mie={kernelName:Uo,backendName:\"webgpu\",kernelFunc:zie};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Bie={kernelName:Go,backendName:\"webgpu\",kernelFunc:Lie},Vie=[pne,Xne,Qne,ese,ise,use,cse,pse,bse,wse,Ise,Tse,gne,Ese,Lse,Use,Hse,jse,Yse,Jse,tre,ire,ure,cre,dre,pre,fre,gre,yre,Sre,xre,kre,Tre,_re,Ere,Fre,zre,Lre,Vre,mne,_se,Ure,Hre,jre,Xre,Qre,Jre,eae,nae,rae,iae,uae,cae,pae,nre,fae,gae,yae,yse,xae,kae,Sae,Nae,$ae,Aae,Rae,vse,Dae,Oae,zae,cne,Bae,Uae,Hae,jae,Yae,Jae,tie,sie,aie,mse,Iie,Cie,uie,cie,hie,mie,bie,yie,xie,iie,rre,Tie,Eie,Oie,Mie,rse,Bie,vae];for(let e of Vie)Al(e);var Wie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Rw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Rw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Rw(e,t){return`${e}_${t}`}var E2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey=\"fromPixels\",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result.numbers[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:\"rgba8unorm\",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Uie=class extends E2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gie=X().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),R2=class extends nl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Cv())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Wie(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return R2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*qm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*qm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case\"copyExternal\":return this.fromPixelProgram||(this.fromPixelProgram=new E2),this.fromPixelProgram;case\"import\":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Uie),this.fromPixelImportProgram;default:w.assert(!1,()=>\"Unsupported fromPixels shape\");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=N.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=u2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type===\"int32\"?new Int32Array(s,o,a.data.length).set(a.data):a.type===\"uint32\"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let T=this.tensorMap.get(r.dataId);return T.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(T=>T.shape),o=\"int32\";i.map(T=>{a.push({type:o,data:T})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let T=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?T/4:T]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((T,E)=>{if(T.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),p=c.map(T=>T.dtype).concat(r.dtype),d=c.map(T=>N.getBroadcastDims(T.shape,r.shape)),h=c.map(T=>w.arraysEqual(T.shape,r.shape)).join(\"_\"),f=d.map(T=>T.join(\"_\")).join(\";\"),m=S2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>I2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ire(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let C={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(C),X().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Gie){return X().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},_v=R2;_v.nextDataId=0;var Hie={};Ae(Hie,{WebGPUBackend:()=>_v,webgpu_util:()=>i2});Cv()&&pp(\"webgpu\",async()=>{X().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:X().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has(\"timestamp-query\");s?n={requiredFeatures:[\"timestamp-query\"]}:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let r=await t.requestDevice(n);return new _v(r,s)},3);var Ct=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(Ct||{}),Jp=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(Jp||{}),D2;function qie(e){D2=e.wasm.cwrap(na,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,C=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(a.shape).buffer);return D2(d,C,r.shape.length,h,T,a.shape.length,u,l,g,f,m,p||0,k),x}var Kie={kernelName:na,backendName:\"wasm\",setupFunc:qie,kernelFunc:jie};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,Ct[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var Xie=Kt(io);function mn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=N.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,Ct[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var Yie=!0,Qie=mn(kr,Yie),F2;function Zie(e){F2=e.wasm.cwrap(xa,null,[\"array\",\"number\",\"number\",\"number\"])}function Jie(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return F2(a,r.length,Ct[s.dtype],i),s}var eoe={kernelName:xa,backendName:\"wasm\",setupFunc:Zie,kernelFunc:Jie};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var toe={kernelName:Ma,backendName:\"wasm\",kernelFunc:eh},O2;function noe(e){O2=e.wasm.cwrap(ci,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function ro(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=roe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=soe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return O2(c,h,u.shape.length,Ct[u.dtype],p,d,a.length),l}function soe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function roe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var aoe={kernelName:ci,backendName:\"wasm\",kernelFunc:ro,setupFunc:noe};function Dr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=N.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=N.getInnerMostAxes(i.length,r),u=ro({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var P2;function ioe(e){P2=e.wasm.cwrap(al,null,[\"number, number, number\"])}function ooe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;P2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var uoe={kernelName:al,backendName:\"wasm\",setupFunc:ioe,kernelFunc:ooe},z2;function loe(e){z2=e.wasm.cwrap(il,null,[\"number, number, number\"])}function coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;z2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var doe={kernelName:il,backendName:\"wasm\",setupFunc:loe,kernelFunc:coe},M2;function poe(e){M2=e.wasm.cwrap(wa,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function hoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Dr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return M2(o,Ct[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var foe={kernelName:wa,backendName:\"wasm\",kernelFunc:hoe,setupFunc:poe},L2;function moe(e){L2=e.wasm.cwrap(ka,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function goe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=N.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return L2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var boe={kernelName:ka,backendName:\"wasm\",setupFunc:moe,kernelFunc:goe};function bn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var yoe={kernelName:Eo,backendName:\"wasm\",kernelFunc:bn},B2;function voe(e){B2=e.wasm.cwrap(Ia,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function xoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],C=bn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=bn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(C.dataId).id,A=n.dataIdMap.get(T.dataId).id,P=i?C.shape[2]:C.shape[1],R=o?T.shape[1]:T.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],C.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(C.shape).buffer),q=new Uint8Array(new Int32Array(T.shape).buffer);return B2(E,W,C.shape.length,A,q,T.shape.length,i,o,z),n.disposeData(C.dataId),n.disposeData(T.dataId),$.shape=v,$}var woe={kernelName:Ia,backendName:\"wasm\",setupFunc:voe,kernelFunc:xoe};function ba(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)koe(u,c[0],d,a,i);else if(h===3)Ioe(u,c[0],c[1],d,a,i);else if(h===4)Soe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function koe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ioe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Soe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Coe={kernelName:Po,backendName:\"wasm\",kernelFunc:ba};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=bn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=ro({inputs:{x:h},backend:n,attrs:{perm:l}}),m=bn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Toe={kernelName:oo,backendName:\"wasm\",kernelFunc:Noe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var $oe={kernelName:Sa,backendName:\"wasm\",kernelFunc:ac},_oe=Kt(Ca),V2;function Aoe(e){V2=e.wasm.cwrap(Ir,null,[\"number\",\"number\",\"number\",\"number\"])}function Eoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return V2(o,a,i,l),u}var Roe={kernelName:Ir,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Eoe};function W2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return eh({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(N.assertParamsConsistent(o,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return bn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=N.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=ev(f,r,t[0].dtype,m),b=N.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Doe={kernelName:uo,backendName:\"wasm\",kernelFunc:W2},U2;function Foe(e){U2=e.wasm.cwrap(Na,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ooe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,C=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let F=s.makeOutput(f.outShape,\"float32\"),$=s.dataIdMap.get(F.dataId).id;return U2(i,r.shape[0],r.shape[1],r.shape[2],o,m,g,b,y,v,x,R,k,C,T,E,A,P,$),F}var Poe={kernelName:Na,backendName:\"wasm\",setupFunc:Foe,kernelFunc:Ooe},G2;function zoe(e){G2=e.wasm.cwrap(Ta,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Moe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=N.convertConv2DDataFormat(u),h=N.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:C,strideHeight:T,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat===\"channelsLast\",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,\"float32\"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return G2(me,we,f,m,g,y,v,b,k,C,x,T,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Loe={kernelName:Ta,backendName:\"wasm\",setupFunc:zoe,kernelFunc:Moe},Boe=Kt($a),Voe=Kt(_a),H2=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(H2||{}),q2;function Woe(e){q2=e.wasm.cwrap(co,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Uoe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return q2(g,b,y,c,k,p,d,H2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Goe={kernelName:co,backendName:\"wasm\",setupFunc:Woe,kernelFunc:Uoe},j2;function Hoe(e){j2=e.wasm.cwrap(lo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function qoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=N.getAxesPermutation([a],u),c=r;l!==null&&(c=ro({inputs:{x:r},attrs:{perm:l},backend:n}));let p=N.getInnerMostAxes(1,u)[0];N.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;j2(f,i?1:0,o?1:0,h,m,Ct[r.dtype]);let g=d;if(l!==null){let b=N.getUndoAxesPermutation(l);g=ro({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var joe={kernelName:lo,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:qoe},K2;function Koe(e){K2=e.wasm.cwrap(po,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Xoe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return K2(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var Yoe={kernelName:po,backendName:\"wasm\",setupFunc:Koe,kernelFunc:Xoe},X2;function Qoe(e){X2=e.wasm.cwrap(Aa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Zoe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=N.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,C=h.strideHeight,T=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,\"float32\"),F=s.dataIdMap.get(R.dataId).id;return X2(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,C,T,E,A,F),R}var Joe={kernelName:Aa,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe},eue=Kt(Ra),tue=!1,nue=mn(ho,tue,\"bool\"),sue=Kt(Da,\"float32\");function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),bn({inputs:{x:r},backend:s,attrs:{shape:o}})}var rue={kernelName:fo,backendName:\"wasm\",kernelFunc:Qm};function Y2(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var aue={kernelName:fl,backendName:\"wasm\",kernelFunc:Y2},Q2;function iue(e){Q2=e.wasm.cwrap(go,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function oue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return Q2(a,o,u,l,c,i),r}var uue={kernelName:go,backendName:\"wasm\",kernelFunc:oue,setupFunc:iue},lue=Kt(Fa),cue=!1,due=mn(Oa,cue),Z2;function pue(e){Z2=e.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function hue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return Z2(c,p,d,h,f,r,g),m}var fue={kernelName:Pa,backendName:\"wasm\",setupFunc:pue,kernelFunc:hue},J2;function mue(e){J2=e.wasm.cwrap(sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return J2(b,K,Y,Z,y,k,C,x,T,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var bue={kernelName:sa,backendName:\"wasm\",setupFunc:mue,kernelFunc:gue},eN;function yue(e){eN=e.wasm.cwrap(ra,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function vue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return eN(b,K,Y,Z,y,k,C,x,T,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var xue={kernelName:ra,backendName:\"wasm\",setupFunc:yue,kernelFunc:vue},tN;function wue(e){tN=e.wasm.cwrap(yo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function kue(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,i,o,u]=kk.prepareAndValidate(s,r),l=t.makeOutput(a,s.dtype);if(i===0)return l;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(u).buffer),b=t.dataIdMap.get(l.dataId).id;return tN(h,Ct[s.dtype],m,i,p,o,g,b),l}var Iue={kernelName:yo,backendName:\"wasm\",setupFunc:wue,kernelFunc:kue},nN;function Sue(e){nN=e.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Cue(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=t.readSync(a.dataId),c=r.shape[u];for(let A=0;A<l.length;++A){let P=l[A];w.assert(P<=c-1&&P>=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let p=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),d=bn({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=w.sizeFromShape(a.shape),f=bn({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(w.sizeFromShape(r.shape)===0)return g;let b=d.shape.length-1,v=t.dataIdMap.get(d.dataId).id,k=t.dataIdMap.get(f.dataId).id,C=t.dataIdMap.get(g.dataId).id,T=new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);return nN(v,Ct[r.dtype],T,b,k,p.batchSize,E,C),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var Nue={kernelName:bo,backendName:\"wasm\",setupFunc:Sue,kernelFunc:Cue},Tue=!1,$ue=mn(vo,Tue,\"bool\"),_ue=!1,Aue=mn(za,_ue,\"bool\"),sN;function Eue(e){sN=e.wasm.cwrap(La,null,[\"number\",\"number\",\"number\",\"number\"])}function Rue(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,\"float32\");if(w.sizeFromShape(t.shape)!==0){let i=s.dataIdMap.get(a.dataId).id;sN(r,Ct[t.dtype],n,i)}return a}var Due={kernelName:La,backendName:\"wasm\",setupFunc:Eue,kernelFunc:Rue},Fue=!1,Oue=mn(xo,Fue,\"bool\"),Pue=!1,zue=mn(wo,Pue,\"bool\"),Mue=Kt(Ba),Lue=!1,Bue=mn(ko,Lue,\"bool\"),rN;function Vue(e){rN=e.wasm.cwrap(Va,null,[\"number\",\"number\",\"number\",\"number\"])}function Wue(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims(\"max\",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;rN(u,Ct[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Uue={kernelName:Va,backendName:\"wasm\",setupFunc:Vue,kernelFunc:Wue},Gue=!1,Hue=mn(Wa,Gue),aN;function que(e){aN=e.wasm.cwrap(Ua,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function jue(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;w.assert(r.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. 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ele={kernelName:Ha,backendName:\"wasm\",setupFunc:Zue,kernelFunc:Jue},tle=!1,nle=mn(qa,tle),uN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(uN||{}),lN;function sle(e){lN=e.wasm.cwrap(ja,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function rle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return lN(i,l,t.shape.length,Ct[t.dtype],d,h,uN[r],u),o}var ale={kernelName:ja,backendName:\"wasm\",kernelFunc:rle,setupFunc:sle},ile=!0,ole=mn(Ka,ile),ule=Kt(Io);function Av(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return 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hle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=dN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Av(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var fle={kernelName:wl,backendName:\"wasm\",setupFunc:ple,kernelFunc:hle},pN;function mle(e){pN=e.wasm.cwrap(No,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=pN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Av(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var ble={kernelName:No,backendName:\"wasm\",setupFunc:mle,kernelFunc:gle},yle=!1,vle=mn(So,yle,\"bool\"),hN;function xle(e){hN=e.wasm.cwrap($o,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function wle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return hN(p,a,i,o,l),u}var kle={kernelName:$o,backendName:\"wasm\",setupFunc:xle,kernelFunc:wle};function Ile(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Sle={kernelName:To,backendName:\"wasm\",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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mN={kernelName:Xa,backendName:\"wasm\",kernelFunc:$le,setupFunc:Tle},_le=!1,Ale=mn(Ya,_le),gN;function Ele(e){gN=e.wasm.cwrap(Qa,null,[\"number\",\"number\",\"number\"])}function Rle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return gN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var Dle={kernelName:Qa,backendName:\"wasm\",setupFunc:Ele,kernelFunc:Rle},bN;function Fle(e){bN=e.wasm.cwrap(Ao,null,[\"number\",\"number\",\"number\",\"number\"])}function Ole(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=N.getInnerMostAxes(f.length,l.shape.length))}N.assertAxesAreInnerMostDims(\"prod\",f,l.shape.length);let[m,g]=N.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;bN(u,b,Ct[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ple={kernelName:Ao,backendName:\"wasm\",setupFunc:Fle,kernelFunc:Ole},zle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},Mle={kernelName:kl,backendName:\"wasm\",kernelFunc:zle},Lle=!0,Ble=mn(Ea,Lle),Vle=Kt(Za),Wle=Kt(ei),yN;function Ule(e){yN=e.wasm.cwrap(Ja,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Gle(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!==\"float32\"&&(g=ac({backend:t,inputs:{x:r},attrs:{dtype:\"float32\"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,\"float32\");if(w.sizeFromShape(r.shape)===0)return y;let v=t.dataIdMap.get(y.dataId).id;return yN(b,c,p,d,h,u,l,a?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),y}var Hle={kernelName:Ja,backendName:\"wasm\",setupFunc:Ule,kernelFunc:Gle},vN;function qle(e){vN=e.wasm.cwrap(Ro,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function jle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return eh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new 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ece(e){wN=e.wasm.cwrap(Fo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function tce(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:i}=s,o=t.makeOutput(i,a.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=Sk.calculateShapes(a,r,i),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=new Uint8Array(new Int32Array(p).buffer),y=t.dataIdMap.get(o.dataId).id;return wN(f,g,Ct[a.dtype],u,l,c,b,d,y),o}var nce={kernelName:Fo,backendName:\"wasm\",setupFunc:ece,kernelFunc:tce},kN;function sce(e){kN=e.wasm.cwrap(\"SelectV2\",null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function 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i=w.sizeFromShape(n),o=i*w.bytesPerElement(s),u=this.wasm._malloc(o);this.dataIdMap.set(e,{id:a,memoryOffset:u,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,i,u),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,o),u)}async read(e){return this.readSync(e)}readSync(e,t,n){let{memoryOffset:s,dtype:r,shape:a,stringBytes:i}=this.dataIdMap.get(e);if(r===\"string\")return(t==null||t===0)&&(n==null||n>=i.length)?i:i.slice(t,n);t=t||0,n=n||w.sizeFromShape(a);let o=w.bytesPerElement(r),u=this.wasm.HEAPU8.slice(s+t*o,s+n*o);return lde(u.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),\"PThread\"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=w.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=w.sizeFromShape(e);switch(t){case\"float32\":return new Float32Array(s,r,a);case\"int32\":return new Int32Array(s,r,a);case\"bool\":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function ode(e){return(t,n)=>(w.fetch(e,{credentials:\"same-origin\"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function Fw(e,t,n){if(Bd!=null)return Bd;let s=\"tfjs-backend-wasm.wasm\";return e&&t?s=\"tfjs-backend-wasm-threaded-simd.wasm\":e&&(s=\"tfjs-backend-wasm-simd.wasm\"),Lu!=null&&Lu[s]!=null?Lu[s]:n+s}async function ude(){let[e,t]=await Promise.all([X().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),X().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((n,s)=>{let r={};r.locateFile=(o,u)=>{if(o.endsWith(\".worker.js\")){let l=rde,c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return o.endsWith(\".wasm\")?Fw(e,t,Pu!=null?Pu:u):u+o},Ev&&(r.instantiateWasm=ode(Fw(e,t,Pu!=null?Pu:\"\")));let a=!1;r.onAbort=()=>{if(a||Bu)return;Bu=!0,s({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers\"})};let i;t&&e&&Bd==null?(r.mainScriptUrlOrBlob=new Blob([\"var WasmBackendModuleThreadedSimd = \"+Dw.default.toString()],{type:\"text/javascript\"}),i=(0,Dw.default)(r)):i=(0,ade.default)(r),i.then(o=>{a=!0,Bu=!1;let u=null;o.tfjs={init:o.cwrap(\"init\",null,[]),initWithThreadsCount:o.cwrap(\"init_with_threads_count\",null,[\"number\"]),getThreadsCount:o.cwrap(\"get_threads_count\",\"number\",[]),registerTensor:o.cwrap(\"register_tensor\",null,[\"number\",\"number\",\"number\"]),disposeData:o.cwrap(\"dispose_data\",u,[\"number\"]),dispose:o.cwrap(\"dispose\",u,[])},n({wasm:o})})})}function lde(e,t){switch(t){case\"float32\":return new Float32Array(e);case\"int32\":return new Int32Array(e);case\"bool\":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var cde=[\"tfjs-backend-wasm.wasm\",\"tfjs-backend-wasm-simd.wasm\",\"tfjs-backend-wasm-threaded-simd.wasm\"],Bd=null,Pu=null,Lu={},Bu=!1,Ev=!1;function Ipe(e,t=!1){if(Mk(\"setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.\"),Bu)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Bd=e,Ev=t}function Spe(e,t=!1){if(Bu)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");if(typeof e==\"string\")Pu=e;else{Lu=e;let n=cde.filter(s=>Lu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Ev=t}var PN=-1,Jm=-1;function Cpe(e){PN=e}function Npe(){if(Jm===-1)throw new Error(\"WASM backend not initialized.\");return Jm}var Tpe=\"0.0.0\",dde=2;pp(\"wasm\",async()=>{let{wasm:e}=await ude();return new ide(e)},dde);var sr=\"3.13.0-20220217\",$pe={tfjs:sr,\"tfjs-core\":sr,\"tfjs-data\":sr,\"tfjs-layers\":sr,\"tfjs-converter\":sr,\"tfjs-backend-cpu\":sr,\"tfjs-backend-webgl\":sr,\"tfjs-backend-wasm\":sr};export{io as Abs,sl as Acos,rl as Acosh,hb as AdadeltaOptimizer,fb as AdagradOptimizer,mb as AdamOptimizer,gb as AdamaxOptimizer,kr as Add,xa as AddN,al as All,il as Any,wa as ArgMax,ol as ArgMin,ul as Asin,ll as Asinh,cl as Atan,pl as Atan2,dl as Atanh,ka as AvgPool,qd as AvgPool3D,ig as AvgPool3DGrad,ag as AvgPoolGrad,ide as BackendWasm,Ia as BatchMatMul,oo as BatchToSpaceND,og as Bincount,ug as BroadcastArgs,w$ as BroadcastTo,mW as Callback,TL as CallbackList,Sa as Cast,Ca as Ceil,Ir as ClipByValue,jd as Complex,Kd as ComplexAbs,uo as Concat,Na as Conv2D,lg as Conv2DBackpropFilter,Ta as Conv2DBackpropInput,Xd as Conv3D,cg as Conv3DBackpropFilterV2,dg as Conv3DBackpropInputV2,$a as Cos,_a as Cosh,co as CropAndResize,lo as Cumsum,AL as CustomCallback,Ud as DataStorage,pg as DenseBincount,po as DepthToSpace,Aa as DepthwiseConv2dNative,hg as DepthwiseConv2dNativeBackpropFilter,fg as DepthwiseConv2dNativeBackpropInput,mg as Diag,Yd as Dilation2D,Yf as Dilation2DBackpropFilter,Xf as Dilation2DBackpropInput,jw as ENV,gW as EarlyStopping,Qd as Einsum,Ra as Elu,gg as EluGrad,m$ as Environment,ho as Equal,hl as Erf,Da as Exp,fo as ExpandDims,mo as Expm1,bg as FFT,fl as Fill,go as FlipLeftRight,Fa as Floor,Oa as FloorDiv,fd as FromPixels,Pa as FusedBatchNorm,sa as FusedConv2D,ra as FusedDepthwiseConv2D,jf as GPGPUContext,yo as GatherNd,bo as GatherV2,w4 as GraphModel,vo as Greater,za as GreaterEqual,_L as History,yg as IFFT,Ma as Identity,Zd as Imag,Dt as InputSpec,ml as IsFinite,gl as IsInf,bl as IsNan,nl as KernelBackend,ep as LRN,xg as LRNGrad,xL as LayerVariable,pr as LayersModel,La as LeakyRelu,xo as Less,wo as LessEqual,vg as LinSpace,Ba as Log,yl as Log1p,k$ as LogSoftmax,ko as LogicalAnd,vl as LogicalNot,Jd as LogicalOr,RS as MathBackendCPU,A1 as MathBackendWebGL,Va as Max,Ua as MaxPool,tp as MaxPool3D,kg as MaxPool3DGrad,wg as MaxPoolGrad,Ig as MaxPoolWithArgmax,Wa as Maximum,Ga as Mean,Ha as Min,qa as Minimum,ja as MirrorPad,xl as Mod,bb as MomentumOptimizer,Sg as Multinomial,Ka as Multiply,Io as Neg,Co as NonMaxSuppressionV3,wl as NonMaxSuppressionV4,No as NonMaxSuppressionV5,So as NotEqual,e_ as OP_SCOPE_SUFFIX,$o as OneHot,To as OnesLike,$r as Optimizer,Vr as OptimizerConstructors,_o as Pack,Xa as PadV2,pde as Pool,Ya as Pow,Qa as Prelu,Ao as Prod,yb as RMSPropOptimizer,_r as RNN,kl as Range,H$ as Rank,np as Real,Ea as RealDiv,Il as Reciprocal,YF as Reduction,Za as Relu,ei as Relu6,Eo as Reshape,Ja as ResizeBilinear,Ng as ResizeBilinearGrad,Sl as ResizeNearestNeighbor,Cg as ResizeNearestNeighborGrad,Ro as Reverse,qo as RotateWithOffset,Do as Round,ti as Rsqrt,Ip as SGDOptimizer,Fo as ScatterNd,Oo as Select,Cl as Selu,Wb as Sequential,si as Sigmoid,Nl as Sign,ni as Sin,zo as Sinh,Po as Slice,ii as Softmax,Tl as Softplus,Mo as SpaceToBatchND,sp as SparseFillEmptyRows,$l as SparseReshape,rp as SparseSegmentMean,ap as SparseSegmentSum,ip as SparseToDense,Lo as SplitV,ri as Sqrt,_l as Square,oi as SquaredDifference,di as Step,Bo as StridedSlice,op as StringNGrams,Tg as StringSplit,$g as StringToHashBucketFast,ui as Sub,ai as Sum,$s as SymbolicTensor,Vo as Tan,li as Tanh,et as Tensor,Vt as TensorBuffer,Sr as Tile,Wo as TopK,Uo as Transform,ci as Transpose,_g as Unique,Go as Unpack,up as UnsortedSegmentSum,gd as Variable,Ho as ZerosLike,na as _FusedMatMul,Mt as abs,OA as acos,zA as acosh,ie as add,LA as addN,Bk as all,dm as any,Hu as argMax,GA as argMin,qA as asin,KA as asinh,YA as atan,ZA as atan2,eE as atanh,Ug as avgPool,Gk as avgPool3d,$A as backend,N as backend_util,Ade as basicLSTMCell,ju as batchNorm,vE as batchNorm2d,wE as batchNorm3d,IE as batchNorm4d,Gg as batchToSpaceND,Hk as bincount,rpe as booleanMaskAsync,NE as broadcastArgs,nd as broadcastTo,jo as broadcast_util,xk as browser,De as buffer,fpe as callbacks,ce as cast,_E as ceil,Bn as clipByValue,lr as clone,aa as complex,Ft as concat,RE as concat1d,FE as concat2d,PE as concat3d,ME as concat4d,$M as constraints,qk as conv1d,ua as conv2d,jk as conv2dTranspose,Kk as conv3d,qE as conv3dTranspose,mde as copyRegisteredKernels,qg as cos,Yk as cosh,wI as cosineWindow,Qk as cumsum,js as customGrad,k4 as data,QE as denseBincount,Mk as deprecationWarn,JE as depthToSpace,hp as depthwiseConv2d,gpe as deregisterOp,dp as device_util,Ede as diag,sR as dilation2d,vde as disableDeprecationWarnings,Re as dispose,xde as disposeVariables,xe as div,uR as divNoNan,Rde as dot,H3 as dropout,dR as einsum,fp as elu,yde as enableDebugMode,bde as enableProdMode,q3 as enclosingPowerOfTwo,Ss as engine,X as env,qn as equal,fR as erf,jn as exp,On as expandDims,yR as expm1,Zk as eye,ub as fft,Ol as fill,Tde as findBackend,$de as findBackendFactory,mp as floor,Lk as floorDiv,DX as forceHalfFloat,da as fused,Ku as gather,W3 as gatherND,kk as gather_util,Cde as getBackend,ex as getGradient,Qf as getKernel,Zf as getKernelsForBackend,Npe as getThreadsCount,wK as gpgpu_util,Ode as grad,Pde as grads,Wn as greater,Ko as greaterEqual,Id as ifft,jg as imag,ds as image,ipe as inTopKAsync,PM as initializers,CB as input,_n as io,fI as irfft,Dde as isFinite,Fde as isInf,_R as isNaN,Ht as keep,xs as kernel_impls,yL as layers,Kg as leakyRelu,Jk as less,Xo as lessEqual,AO as linalg,DR as linspace,bpe as loadGraphModel,ppe as loadLayersModel,OR as localResponseNormalization,Kn as log,Xg as log1p,Lde as logSigmoid,eI as logSoftmax,XR as logSumExp,Ds as logicalAnd,Zg as logicalNot,rI as logicalOr,Bde as logicalXor,lpe as losses,We as matMul,Q_ as math,As as max,Jg as maxPool,aI as maxPool3d,sD as maxPoolWithArgmax,Tr as maximum,It as mean,cm as memory,Vde as meshgrid,KV as metrics,hm as min,bp as minimum,lD as mirrorPad,dD as mod,cpe as model,cW as models,eb as moments,ape as movingAverage,V as mul,Wde as multiRNNCell,gD as multinomial,kt as neg,RO as nextFrame,vI as norm,Xu as notEqual,vd as oneHot,zn as ones,Xn as onesLike,L as op,Ude as outerProduct,pi as pad,Gde as pad1d,Hde as pad2d,qde as pad3d,jde as pad4d,Kde as pool,ca as pow,nb as prelu,F_ as print,iI as prod,wde as profile,Xde as rand,Yde as randomGamma,zD as randomNormal,zl as randomUniform,Yu as range,Sde as ready,wd as real,VD as reciprocal,pp as registerBackend,hpe as registerCallbackConstructor,S$ as registerGradient,Al as registerKernel,mpe as registerOp,dW as regularizers,Xs as relu,oI as relu6,Nde as removeBackend,G as reshape,Yn as reverse,Qde as reverse1d,Zde as reverse2d,Jde as reverse3d,epe as reverse4d,lb as rfft,uI as round,lI as rsqrt,Ie as scalar,M3 as scatterND,Sk as scatter_util,cI as selu,JD as separableConv2d,dpe as sequential,ae as serialization,Ide as setBackend,_de as setPlatform,Cpe as setThreadsCount,Ipe as setWasmPath,Spe as setWasmPaths,p5 as setWebGLContext,t3 as setdiff1dAsync,Qy as shared,qs as sigmoid,s3 as sign,upe as signal,dI as sin,pI as sinh,He as slice,ab as slice1d,hI as slice2d,ib as slice3d,kd as slice4d,wt as slice_util,ob as softmax,Pl as softplus,tb as spaceToBatchND,Wc as sparse,xI as sparseToDense,ope as spectral,Ln as split,ln as sqrt,ct as square,mI as squaredDifference,mr as squeeze,Qn as stack,yp as step,k3 as stridedSlice,zf as string,ge as sub,ye as sum,cp as sumOutType,S3 as tan,qu as tanh,hs as tensor,Qt as tensor1d,Ki as tensor2d,sA as tensor3d,tpe as tensor4d,npe as tensor5d,spe as tensor6d,_s as tensor_util,xA as test_util,j as tidy,cs as tile,kde as time,N3 as topk,Fi as train,qe as transpose,cb as truncatedNormal,dx as unique,fde as unregisterGradient,hde as unregisterKernel,A3 as unsortedSegmentSum,Fs as unstack,yn as upcastType,w as util,zde as valueAndGrad,Mde as valueAndGrads,R3 as variable,MR as variableGrads,$pe as version,ype as version_converter,gde as version_core,vpe as version_cpu,t0 as version_layers,Tpe as version_wasm,xpe as version_webgl,wpe as webgl,d5 as webgl_util,Hie as webgpu,vn as where,bI as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        return { tensor: null, canvas: inCanvas };\n      }\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  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0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.2;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const [contours, confidence, contourCoords] = model.execute(face.tensor as Tensor) as Array<Tensor>; // first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      tf.dispose([contourCoords, coordsReshaped, confidence, contours]);\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.iris?.enabled) rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // augment results with iris\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.mesh?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 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x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep, now, log } from './util';\nimport { env } from './env';\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Point } from '../result';\nimport type { AnyCanvas } from '../exports';\n\n/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n\nlet drawTime = 0;\n\nconst getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nconst rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * (pt[2] || 0))}, ${127.5 - (2 * (pt[2] || 0))}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (i * z)}, ${127.5 - (i * z)}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see {@link similarity}\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - {@link distance} calculated `distance` of given descriptor to the best match\n * - {@link similarity} calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human } from './human';\nimport type { Tensor } from './tfjs/types';\nimport { env } from './util/env';\n\nasync function warmupBitmap(instance: Human) {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  let res;\n  return new Promise(async (resolve) => {\n    if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n    else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n    else res = await warmupNode(instance);\n    const t1 = now();\n    if (instance.config.debug) log('Warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './util/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default };\n"],
-  "mappings": 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+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  // eslint-disable-next-line no-console\n  if (msg) console.log(ts, 'Human:', ...msg);\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: Array<{ reason: string, where: string, expected?: string }> = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: Array<number>) => data.reduce((acc: Array<number>, val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable indent */\n/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be 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background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, 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for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: true,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n 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The gradient of input '${u}' has shape '${l.shape}', which does not match the shape of the input '${c.shape}'`);if(e[c.id]==null)e[c.id]=l;else{let p=e[c.id];e[c.id]=s(p,l),p.dispose()}}}}var nx=20,Iu=3,Rf=7;function M$(e,t,n,s){let r=ro(t),a=L$(e,t,n,r),i=t.length,o=Jc(e,t,n,r,a),u=[\"Tensor\"];return s&&(u.push(`  dtype: ${n}`),u.push(`  rank: ${i}`),u.push(`  shape: [${t}]`),u.push(\"  values:\")),u.push(o.map(l=>\"    \"+l).join(`\n`)),u.join(`\n`)}function L$(e,t,n,s){let r=pt(t),a=s[s.length-1],i=new Array(a).fill(0),o=t.length,u=n===\"complex64\"?$u(e):e;if(o>1)for(let l=0;l<r/a;l++){let c=l*a;for(let p=0;p<a;p++)i[p]=Math.max(i[p],Tu(u[c+p],0,n).length)}return i}function Tu(e,t,n){let s;return Array.isArray(e)?s=`${parseFloat(e[0].toFixed(Rf))} + ${parseFloat(e[1].toFixed(Rf))}j`:ir(e)?s=`'${e}'`:n===\"bool\"?s=Qw(e):s=parseFloat(e.toFixed(Rf)).toString(),Pu(s,t)}function Qw(e){return e===0?\"false\":\"true\"}function Jc(e,t,n,s,r,a=!0){let i=n===\"complex64\"?2:1,o=t[0],u=t.length;if(u===0){if(n===\"complex64\"){let m=$u(e);return[Tu(m[0],0,n)]}return n===\"bool\"?[Qw(e[0])]:[e[0].toString()]}if(u===1){if(o>nx){let g=Iu*i,b=Array.from(e.slice(0,g)),y=Array.from(e.slice((o-Iu)*i,o*i));return n===\"complex64\"&&(b=$u(b),y=$u(y)),[\"[\"+b.map((v,x)=>Tu(v,r[x],n)).join(\", \")+\", ..., \"+y.map((v,x)=>Tu(v,r[o-Iu+x],n)).join(\", \")+\"]\"]}let m=n===\"complex64\"?$u(e):Array.from(e);return[\"[\"+m.map((g,b)=>Tu(g,r[b],n)).join(\", \")+\"]\"]}let l=t.slice(1),c=s.slice(1),p=s[0]*i,d=[];if(o>nx){for(let m=0;m<Iu;m++){let g=m*p,b=g+p;d.push(...Jc(e.slice(g,b),l,n,c,r,!1))}d.push(\"...\");for(let m=o-Iu;m<o;m++){let g=m*p,b=g+p;d.push(...Jc(e.slice(g,b),l,n,c,r,m===o-1))}}else for(let m=0;m<o;m++){let g=m*p,b=g+p;d.push(...Jc(e.slice(g,b),l,n,c,r,m===o-1))}let h=u===2?\",\":\"\";d[0]=\"[\"+d[0]+h;for(let m=1;m<d.length-1;m++)d[m]=\" \"+d[m]+h;let f=`,\n`;for(let m=2;m<u;m++)f+=`\n`;return d[d.length-1]=\" \"+d[d.length-1]+\"]\"+(a?\"\":f),d}function $u(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var Vt=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=pt(e),n!=null){let s=n.length;O(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t===\"complex64\")throw new Error(\"complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=n||Bw(t,this.size),this.strides=ro(e)}set(e,...t){t.length===0&&(t=[0]),O(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return us().makeTensor(this.values,this.shape,this.dtype)}},us=null,Bi=null,B$=null;function V$(e){us=e}function W$(e){Bi=e}function U$(e){B$=e}var et=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=pt(e),this.strides=ro(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():\"higher\"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Bi.buffer(this.shape,this.dtype,e)}bufferSync(){return Bi.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Gi(this.shape,e,this.dtype===\"complex64\")}arraySync(){return Gi(this.shape,this.dataSync(),this.dtype===\"complex64\")}async data(){this.throwIfDisposed();let e=us().read(this.dataId);if(this.dtype===\"string\"){let t=await e;try{return t.map(n=>fd(n))}catch(n){throw new Error(\"Failed to decode the string bytes into utf-8. 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Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ar(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new D$(this.backendInstance),!0}setupRegisteredKernels(){Qf(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Qf(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof tl)&&typeof n.then==\"function\"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error(\"No backend found in registry.\");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error(\"Could not initialize any backends, all backend initializations failed.\")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!=\"function\")throw new Error(\"Please provide a function to tidy()\");t=e}else{if(typeof e!=\"string\"&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(typeof t!=\"function\")throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Jf.nextTensorId++}nextVariableId(){return Jf.nextVariableId++}clone(e){let t=M.runKernel(Ma,{x:e}),n={x:e},s=a=>({x:()=>{let i=\"float32\",o={x:a},u={dtype:i};return M.runKernel(Sa,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Yf(e,this.backendName)!=null))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){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Df(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Df(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Yf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:C,dtype:T}=x;return this.makeTensorFromDataId(k,C,T)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Df(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Jv(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");n=n||\"float32\",s=s||this.backend;let r=e;n===\"string\"&&ir(e[0])&&(r=e.map(o=>El(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n===\"string\"){let o=this.state.tensorInfo.get(a),u=Uw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||\"float32\";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new md(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let n=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(n=e.size*jf(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 md||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;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let n=e.size*jf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let 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,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let 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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of 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4 or rank 1 but got rank ${o.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),qu(i,o,u,c,l,a)}var IE=L({batchNorm4d_:kE});function SE(e,t,n){let s=_(e,\"x\",\"bincount\"),r=_(t,\"weights\",\"bincount\");O(s.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},i={size:n};return M.runKernel(ig,a,i)}var Hk=L({bincount_:SE});function CE(e,t){let n=_(e,\"s0\",\"broadcastArgs\",\"int32\"),s=_(t,\"s1\",\"broadcastArgs\",\"int32\");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). 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i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(ln(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(ca(this.beta1,this.iterations_+1)),this.accBeta2.assign(ca(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};fb.className=\"Adam\";Nr(fb);var mb=class extends $r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=Tr(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(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)}};mb.className=\"Adamax\";Nr(mb);var kp=class extends $r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};kp.className=\"SGD\";Nr(kp);var gb=class extends kp{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ie(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(!1))});let a=this.accumulations[s].variable,i=Array.isArray(e)?e[s].tensor:e[n];i!=null&&j(()=>{let o,u=ie(V(this.m,a),i);this.useNesterov?o=ie(V(this.c,ie(i,V(u,this.m))),r):o=ie(V(this.c,u),r),a.assign(u),r.assign(o)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(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);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};gb.className=\"Momentum\";Nr(gb);var bb=class extends $r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),ln(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),ln(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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adagrad(e,t=.1){return new hb(e,t)}},Fi={sgd:Vr.sgd,momentum:Vr.momentum,adadelta:Vr.adadelta,adagrad:Vr.adagrad,rmsprop:Vr.rmsprop,adamax:Vr.adamax,adam:Vr.adam},EO=(()=>typeof requestAnimationFrame!=\"undefined\"?requestAnimationFrame:typeof setImmediate!=\"undefined\"?setImmediate:e=>e())();function RO(){return new Promise(e=>EO(()=>e()))}var 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DO(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)O(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function FO(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var yb=30;function OO(e){return e<=yb?e:pd(e,Math.floor(Math.sqrt(e)))}function PO(e,t,n){let s=n*(typeof e==\"number\"?e:e[0]),r=t*(typeof e==\"number\"?e:e[1]);return[s,r]}function zO(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function MO(e,t,n=!0){let s=[];if(n){s.push(t);for(let r=t+1;r<e;++r)r<=2*t?(s.push(r),s.push(r-(t+1))):s.push(r)}else{let r=[],a=[];for(let i=1;i<e;++i)i>=t*2+1||i%2===1?a.push(i):r.push(i);s.push(...r),s.push(0),s.push(...a)}return s}function LO(e,t,n,s=!0){let r=[];s?r.push(e[0]/n):r.push(e[0]*n);for(let a=1;a<e.length;++a)a<=t.length?s?r.push(t[a-1]*e[a]):r.push(e[a]/t[a-1]):r.push(e[a]);return r}function BO(e,t){let n=[0];for(let s=0;s<t;++s)n.push(e[s][0]);return n}function VO(e,t,n){let s=e.slice(0,1);for(let r=0;r<n;++r)s.push(e[r+1]-t[r][0]-t[r][1]);return s}var _I=1.7580993408473768,AI=1.0507009873554805,WO=.3275911,UO=.254829592,GO=-.284496736,HO=1.421413741,qO=-1.453152027,jO=1.061405429;function KO(e,t){if(e.length!==t.length)throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);let n=new Float32Array(e.length*2);for(let s=0;s<n.length;s+=2)n[s]=e[s/2],n[s+1]=t[s/2];return n}function XO(e){let t=new Float32Array(e.length/2),n=new Float32Array(e.length/2);for(let 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},IL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=IL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+\"_\"+Rp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function aB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Rp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,\"input layer has >1 nodes\"),Cs(x===0,\"input layer has >1 tensors\"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Yo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,C)=>{(x==null||k==null||C==null)&&(x=b.sourceLayer,k=b.nodeIndex,C=b.tensorIndex);let T=x.inboundNodes[k];if(v.indexOf(T)!==-1)throw new ps(`The tensor ${b.name} at layer \"${x.name}\" is part of a cycle.`);if(y.indexOf(T)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(T)===-1&&v.push(T);let E=T.inboundLayers.length;for(let A=0;A<E;A++){let P=T.inputTensors[A],R=T.inboundLayers[A],F=T.nodeIndices[A],$=T.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(T);v.indexOf(T)>=0;)v.splice(v.indexOf(T),1);i.push(T)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],C=b.nodeIndices[x],T=k.inboundNodes[C],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(y+1,E),n[T.id]=T}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Wc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],C=a[x.id];return k<C?-1:k>C?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Wc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new ps(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${v.name}\". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new ps(`The name \"${b}\" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split(\"/\");i=o.slice(0,-2).concat([o[o.length-1]]).join(\"/\")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Fb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${t0}`,t.backend=\"TensorFlow.js\",t}toJSON(e,t=!0){let n=vm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Xr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return _u(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=pa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Id(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+\"_0_0\";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Wc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(gn(c)),d=Id(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return gn(r)}runInternalGraph(e,t){t==null&&(t=pa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Wc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],C=y[v];n[x.id]=[k,C]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U(\"Provide either a layer name or layer index\");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],C=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let T=r[x];if(T.inboundNodes.length<=k){i(m,g);return}let E=T.inboundNodes[k];b.push(E.outputTensors[C])}b.length>0&&m.apply(gn(b),y)}function u(m){let g=m.name,b=fs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!AM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function iB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e==\"object\"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e==\"object\"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]==\"object\"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function n0(e,t){return iB(e,t,\"classWeight\")}async function s0(e,t,n,s){if(t!=null||s!=null)throw new Error(\"Support sampleWeight is not implemented yet\");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Gu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,\"float32\")}else return null}function oB(e,t){return V(e,t)}var uB=32;function r0(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Dx(\"input\",e.inputNames,n),i=Dx(\"output\",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Dx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function lB(e){if(e.length===3)throw new Fe(\"Validation with sample weights is not implemented yet.\");return{xs:e[0],ys:e[1]}}async function cB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>\"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).\");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Nd?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Px(e,this.feedInputNames,this.feedInputShapes,!1,\"input\"),t=Px(t,this.feedOutputNames,r,!1,\"target\"),bB(e,t,null),yB(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,n,s,r=!0,a){let[i,o]=this.standardizeUserDataXY(e,t,r,a);if(n!=null)throw new Error(\"sample weight is not supported yet.\");let u=null;if(s!=null){let l=n0(s,this.outputNames);u=[];for(let c=0;c<l.length;++c)u.push(await s0(o[c],null,l[c]))}return[i,o,u]}testLoop(e,t,n,s=0,r){return j(()=>{let a=this.checkNumSamples(t,n,r,\"steps\"),i=[];if(s>0)throw new Fe(\"Verbose mode is not implemented yet.\");if(r!=null)throw new Fe(\"steps mode in testLoop() is not implemented yet\");{let o=wm(a,n),u=Qt(ys(0,a));for(let l=0;l<o.length;++l){let c=o[l][0],p=o[l][1],d=Jr(u,c,p-c),h=Bb(t,d),f=e(h);if(l===0)for(let m=0;m<f.length;++m)i.push(Ie(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=ie(i[m],V(p-c,g))}}for(let l=0;l<i.length;++l)i[l]=xe(i[l],a)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let n=0;n<e.length;++n){let s=e[n],r=s;wx(e,s)>1&&(r+=`_${wx(e.slice(0,n),s)}`),t.push(r)}return 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t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let u=0;u<this.inputs.length;++u)a.push({key:this.inputs[u],value:s[u]});let i=new Xr(a),o=_u(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],c=It(l(r[u],o[u]));u===0?n=c:n=ie(n,c),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],c=this.metricsTensors[u][1],p=It(l(r[c],o[c]));t.push(p)}return t})}async fit(e,t,n={}){return mB(this,e,t,n)}async fitDataset(e,t){return cB(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],i=this.makeTrainFunction()(s.concat(r)),o=[];for(let u of i){let l=await u.data();o.push(l[0])}return Re(i),ls(n[0],e),ls(n[1],t),gn(o)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=lm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-lm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss==\"string\")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!=\"string\")throw new Error(\"Serialization of non-string loss is not supported.\");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]==\"string\")e[s]=Ws(n[s]);else throw new Error(\"Serialization of non-string loss is not supported.\")}return e}getMetricIdentifiers(){if(typeof this.metrics==\"string\"||typeof this.metrics==\"function\")return[Ws(Hc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(Hc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(Hc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error(\"Loading weight_metrics is not supported yet.\");if(e.loss_weights!=null)throw new Error(\"Loading loss_weights is not supported yet.\");if(e.sample_weight_mode!=null)throw new Error(\"Loading sample_weight_mode is not supported yet.\");let t=Qu(e.optimizer_config),n=fs(t),s;if(typeof e.loss==\"string\")s=qr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>qr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=qr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>qr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=qr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e==\"string\"){let u=_n.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");let n=await _n.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:xB,generatedBy:`TensorFlow.js tfjs-layers v${t0}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u=\"optimizer\",{data:l,specs:c}=await _n.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=_n.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Ax(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Ax(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className=\"Model\";ae.registerClass(pr);var i0=class extends pr{};i0.className=\"Functional\";ae.registerClass(i0);async function wB(e,t){\"modelTopology\"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Qu(n),r=fs(s,t);if(e.weightsManifest!=null){let a=await _n.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function kB(e,t){if(t==null&&(t={}),typeof e==\"string\"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return IB(e,void 0,t)}async function IB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=fs(Qu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U(\"LayersModel artifacts contains weight data, but not weight specs. 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For multi-output layers, use the functional API.\");if(n.inputs.length!==1)throw new U(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");let s=HI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+\"_input\"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U(\"All layers in a Sequential model should have a single output tensor. 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Add some layers first.\");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+\"_model\"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ps(\"The model needs to be compiled before being used.\");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className===\"Merge\")throw new U(\"Legacy serialization format not supported yet.\");r=t}else w.assert(t.layers!=null,()=>\"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.\"),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Im))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=fs(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Vb=Im;Vb.className=\"Sequential\";ae.registerClass(Vb);function cpe(e){return new pr(e)}function dpe(e){return new Vb(e)}function ppe(e,t){return t==null&&(t={}),kB(e,t)}function CB(e){return HI(e)}function hpe(e,t){Ob.registerCallbackConstructor(e,t)}var wn=class extends ae.Serializable{getConfig(){return{}}},o0=class extends wn{apply(e,t=1){return XM(e,t)}};o0.className=\"elu\";ae.registerClass(o0);var u0=class extends wn{apply(e){return cI(e)}};u0.className=\"selu\";ae.registerClass(u0);var l0=class extends wn{apply(e){return Xs(e)}};l0.className=\"relu\";ae.registerClass(l0);var c0=class extends wn{apply(e){return j(()=>gp(6,Xs(e)))}};c0.className=\"relu6\";ae.registerClass(c0);var d0=class extends wn{apply(e){return e}};d0.className=\"linear\";ae.registerClass(d0);var p0=class extends wn{apply(e){return qs(e)}};p0.className=\"sigmoid\";ae.registerClass(p0);var h0=class extends wn{apply(e){return QM(e)}};h0.className=\"hardSigmoid\";ae.registerClass(h0);var f0=class extends wn{apply(e){return Ol(e)}};f0.className=\"softplus\";ae.registerClass(f0);var m0=class extends wn{apply(e){return YM(e)}};m0.className=\"softsign\";ae.registerClass(m0);var g0=class extends wn{apply(e){return Hu(e)}};g0.className=\"tanh\";ae.registerClass(g0);var Wb=class extends wn{apply(e,t=-1){return ib(e,t)}};Wb.className=\"softmax\";ae.registerClass(Wb);var b0=class extends wn{apply(e,t=-1){return eI(e,t)}};b0.className=\"logSoftmax\";ae.registerClass(b0);var y0=class extends wn{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};y0.className=\"swish\";ae.registerClass(y0);var v0=class extends wn{apply(e){return j(()=>V(e,Hu(Ol(e))))}};v0.className=\"mish\";ae.registerClass(v0);function br(e){return e.getClassName()}function Wf(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,\"activation\")}function yr(e){if(e==null){let t={};return t.className=\"linear\",t.config={},Wf(t)}if(typeof e==\"string\"){let t={};return t.className=e,t.config={},Wf(t)}else return e instanceof wn?e:Wf(e)}function Ub(e){if(e!=null&&typeof e!=\"object\")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var x0=class extends ae.Serializable{},Wl=class extends x0{constructor(e){super();Ub(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ye(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ye(V(this.l2,Bl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wl.className=\"L1L2\";ae.registerClass(Wl);function NB(e){return Ub(e),new Wl({l1:e!=null?e.l1:null,l2:0})}function TB(e){return Ub(e),new Wl({l2:e!=null?e.l2:null,l1:0})}var Mx={l1l2:\"L1L2\"};function at(e){return vb(e)}function Lx(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,\"regularizer\")}function ft(e){if(e==null)return null;if(typeof e==\"string\"){let n={className:e in Mx?Mx[e]:e,config:{}};return Lx(n)}else return e instanceof x0?e:Lx(e)}var Gb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Bn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Gb.className=\"ReLU\";ae.registerClass(Gb);var Hb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return jg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Hb.className=\"LeakyReLU\";ae.registerClass(Hb);var qb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER=\"zeros\",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes==\"number\")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight(\"alpha\",t,\"float32\",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),tb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:at(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};qb.className=\"PReLU\";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return hp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className=\"ELU\";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),\"float32\"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Kb.className=\"ThresholdedReLU\";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xb.className=\"Softmax\";ae.registerClass(Xb);function Ki(e,t,n){if(typeof e==\"number\")return pa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!HM(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function ms(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n===\"same\"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s===\"valid\")e=e*t+gr([n-t,0]);else if(s===\"same\")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Yb(e,t){return j(()=>(St(t),t===\"channelsFirst\"?qe(e,[0,2,3,1]):e))}function w0(e,t){return j(()=>(St(t),t===\"channelsFirst\"?qe(e,[0,2,3,4,1]):e))}function $B(e,t,n,s=1,r=\"valid\",a,i=1){return j(()=>{if(a==null&&(a=bs()),St(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a===\"channelsFirst\"&&(e=qe(e,[0,2,1])),r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");let o=qk(e,t,s,r===\"same\"?\"same\":\"valid\",\"NWC\",i);return n!=null&&(o=ws(o,n)),o})}function Bx(e,t,n,s=[1,1],r=\"valid\",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Yb(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");return u=da.conv2d({x:u,filter:t,strides:s,pad:r===\"same\"?\"same\":\"valid\",dilations:i,dataFormat:\"NHWC\",bias:n,activation:o}),a===\"channelsFirst\"&&(u=qe(u,[0,3,1,2])),u})}function _B(e,t,n,s=[1,1,1],r=\"valid\",a,i){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=w0(e,a);if(r===\"causal\")throw new Fe(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");return o=Kk(o,t,s,r===\"same\"?\"same\":\"valid\",\"NDHWC\",i),n!=null&&(o=ws(o,n)),a===\"channelsFirst\"&&(o=qe(o,[0,4,1,2,3])),o})}var Qb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",Qb.verifyArgs(t),this.rank=e,Bt(this.rank,\"rank\"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ki(t.kernelSize,e,\"kernelSize\"),this.strides=Ki(t.strides==null?1:t.strides,e,\"strides\"),this.padding=t.padding==null?\"valid\":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?\"channelsLast\":t.dataFormat,St(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Ki(t.dilationRate==null?1:t.dilationRate,e,\"dilationRate\"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate==\"number\")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs(\"kernelSize\"in e,\"required key 'kernelSize' not in config\"),typeof e.kernelSize!=\"number\"&&!xb(e.kernelSize,\"number\",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ul=class extends Qb{constructor(e,t){super(e,t);this.kernel=null,Ul.verifyArgs(t),this.filters=t.filters,Bt(this.filters,\"filters\"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight(\"kernel\",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=PI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Bx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$B(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Bx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_B(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe(\"convolutions greater than 3D are not implemented yet.\");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat===\"channelsLast\"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=ms(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate==\"number\"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat===\"channelsLast\"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:at(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!(\"filters\"in e)||typeof e.filters!=\"number\"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},k0=class extends Ul{constructor(e){super(2,e);k0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!xb(e.kernelSize,\"number\",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Pp=k0;Pp.className=\"Conv2D\";ae.registerClass(Pp);var I0=class extends Ul{constructor(e){super(3,e);I0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},zp=I0;zp.className=\"Conv3D\";ae.registerClass(zp);var Zb=class extends Pp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U(\"Input should have rank 4; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat===\"channelsFirst\"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,1]));let g=jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Zb.className=\"Conv2DTranspose\";ae.registerClass(Zb);var Jb=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!==\"same\"&&this.padding!==\"valid\")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U(\"Input should have rank 5; Received input shape: \"+JSON.stringify(e));let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null)throw new U(\"The channel dimension of the inputs should be defined. Found `None`.\");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight(\"kernel\",s,\"float32\",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat===\"channelsFirst\"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!==\"channelsLast\"&&(n=qe(n,[0,2,3,4,1]));let k=qE(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!==\"channelsLast\"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat===\"channelsFirst\"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jb.className=\"Conv3DTranspose\";ae.registerClass(Jb);var S0=class extends Ul{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER=\"glorotUniform\",this.DEFAULT_POINTWISE_INITIALIZER=\"glorotUniform\",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");if(t.padding!=null&&t.padding!==\"same\"&&t.padding!==\"valid\")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,\"float32\",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight(\"pointwise_kernel\",r,\"float32\",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.filters],\"float32\",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe(\"1D separable convolution is not implemented yet.\");return this.rank===2&&(this.dataFormat===\"channelsFirst\"&&(e=qe(e,[0,2,3,1])),n=JD(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,\"NHWC\")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat===\"channelsFirst\"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.pointwiseRegularizer=at(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};S0.className=\"SeparableConv\";var ey=class extends S0{constructor(e){super(2,e)}};ey.className=\"SeparableConv2D\";ae.registerClass(ey);var C0=class extends Ul{constructor(e){super(1,e);C0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!=\"number\"&&!xb(e.kernelSize,\"number\",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ty=C0;ty.className=\"Conv1D\";ae.registerClass(ty);var ny=class extends Ge{constructor(e){super(e);typeof e.cropping==\"number\"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]==\"number\"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?\"channelsLast\":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat===\"channelsFirst\"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat===\"channelsLast\"){let n=Uc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ny.className=\"Cropping2D\";ae.registerClass(ny);var sy=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?\"nearest\":e.interpolation,WM(this.interpolation)}computeOutputShape(e){if(this.dataFormat===\"channelsFirst\"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat===\"channelsFirst\"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation===\"nearest\"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation===\"nearest\"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sy.className=\"UpSampling2D\";ae.registerClass(sy);function AB(e,t,n=[1,1],s=\"valid\",r,a){return j(()=>{r==null&&(r=bs()),St(r);let i=Yb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=pp(i,t,n,s===\"same\"?\"same\":\"valid\",\"NHWC\",a),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}var ry=class extends Qb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat===\"channelsFirst\"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight(\"depthwise_kernel\",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=AB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=ms(t,this.kernelSize[0],this.padding,this.strides[0]),a=ms(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat===\"channelsFirst\"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};ry.className=\"DepthwiseConv2D\";ae.registerClass(ry);function N0(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U(\"When inputs is an array, neither initialState or constants should be provided\");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function T0(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");i&&console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\"),r!=null&&(r=ce(ce(r,\"bool\"),\"float32\"),r.rank===u-1&&(r=On(r,-1)),r=qe(r,l)),s&&(t=Yn(t,0),r!=null&&(r=Yn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],C=ge(Xn(k),k),T=ie(V(v[0],k),V(d[0],C)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,C)));return{output:T,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Qn(c,1)),[p,g,d]})}var $0=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U(\"cell property is missing for the constructor of RNN.\");if(Array.isArray(e.cell)?t=new Bp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){gm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe(\"Constants support is not implemented in RNN yet.\");gm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape[0];if(n==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");let i={training:s},u=T0((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ye(t,[1,2]),t=Ll(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?fm(t,[1,n]):t):this.cell.stateSize>1?[fm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$0.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=fs(s,n);return new e(Object.assign(t,{cell:r}))}},_r=$0;_r.className=\"RNN\";ae.registerClass(_r);var Gl=class extends Ge{},Mp=class extends Gl{constructor(e){super(e);this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight(\"kernel\",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Mp.className=\"SimpleRNNCell\";ae.registerClass(Mp);var ay=class extends _r{constructor(e){e.cell=new Mp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};ay.className=\"SimpleRNN\";ae.registerClass(ay);var Lp=class extends Gl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.resetAfter)throw new U(\"GRUCell does not support reset_after parameter set to true.\");this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight(\"bias\",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Ln(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Ln(l,3,l.rank-1),[b,y]=Ln(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Lp.className=\"GRUCell\";ae.registerClass(Lp);var iy=class extends _r{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};iy.className=\"GRU\";ae.registerClass(iy);var Hl=class extends Gl{constructor(e){super(e);this.DEFAULT_ACTIVATION=\"tanh\",this.DEFAULT_RECURRENT_ACTIVATION=\"hardSigmoid\",this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_RECURRENT_INITIALIZER=\"orthogonal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight(\"kernel\",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight(\"recurrent_kernel\",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends es{apply(i,o){let u=r.apply([a]),l=new Cp().apply([a]),c=r.apply([a*2]);return Cx(Cx(u,l),c)}},t.className=\"CustomInit\",t)}else s=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Ln(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Hl.className=\"LSTMCell\";ae.registerClass(Hl);var oy=class extends _r{constructor(e){e.implementation===0&&console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\"),e.cell=new Hl(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};oy.className=\"LSTM\";ae.registerClass(oy);var Bp=class extends Gl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){gm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Zr(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(fs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return bm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Fb(t)}};Bp.className=\"StackedRNNCells\";ae.registerClass(Bp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):WI(t(),n),o=()=>Vl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _0=class extends _r{constructor(e){if(e.unroll)throw new Fe(\"Unrolling is not possible with convolutional RNNs.\");if(Array.isArray(e.cell))throw new Fe(\"It is not possible at the moment to stack convolutional cells.\");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U(\"ConvRNN2D cell does not support constants\");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t===\"channelsFirst\",u=e[o?3:2],l=e[o?4:3],c=ms(u,s[0],r,a[0],i[0]),p=ms(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};_0.className=\"ConvRNN2D\";var Vp=class extends Hl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,\"filters\"),this.kernelSize=Ki(n,2,\"kernelSize\"),this.kernelSize.forEach(o=>Bt(o,\"kernelSize\")),this.strides=Ki(s||1,2,\"strides\"),this.strides.forEach(o=>Bt(o,\"strides\")),this.padding=r||\"valid\",Un(this.padding),this.dataFormat=a||\"channelsLast\",St(this.dataFormat),this.dilationRate=Ki(i||1,2,\"dilationRate\"),this.dilationRate.forEach(o=>Bt(o,\"dilationRate\"))}build(e){var t;e=nt(e);let n=this.dataFormat===\"channelsFirst\"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight(\"kernel\",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight(\"recurrent_kernel\",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends es{apply(c,p){let d=u.apply([l]),h=zn([l]),f=u.apply([l*2]);return Nb([d,h,f])}},t.className=\"CustomInit\",t)}else o=this.biasInitializer;this.bias=this.addWeight(\"bias\",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,C]=Ln(this.kernel.read(),i,y),[T,E,A,P]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,T,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,C,P,this.padding);let[R,F,$,z]=Ln(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ua(e,t,this.strides,s||\"valid\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ua(e,t,1,\"same\",this.dataFormat===\"channelsFirst\"?\"NCHW\":\"NHWC\")}};Vp.className=\"ConvLSTM2DCell\";ae.registerClass(Vp);var uy=class extends _0{constructor(e){let t=new Vp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};uy.className=\"ConvLSTM2D\";ae.registerClass(uy);var Wp=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Vl(()=>WI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Wp.className=\"Dropout\";ae.registerClass(Wp);var ly=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};ly.className=\"SpatialDropout1D\";ae.registerClass(ly);var cy=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER=\"glorotNormal\",this.DEFAULT_BIAS_INITIALIZER=\"zeros\",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,\"units\"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight(\"kernel\",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight(\"bias\",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=PI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};cy.className=\"Dense\";ae.registerClass(cy);var dy=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to \"Flatten\" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat===\"channelsFirst\"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return KM(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};dy.className=\"Flatten\";ae.registerClass(dy);var py=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};py.className=\"Activation\";ae.registerClass(py);var hy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),qM(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};hy.className=\"RepeatVector\";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n=\"Total size of new array must be unchanged.\",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U(\"Can only specifiy one unknown dimension.\");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};fy.className=\"Reshape\";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error(\"Invalid permutation `dims`: \"+JSON.stringify(e.dims)+\" `dims` must contain consecutive integers starting from 1.\");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};my.className=\"Permute\";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return cm(Ku(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=cm(Ku(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};gy.className=\"Masking\";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER=\"randomUniform\",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,\"inputDim\"),this.outputDim=e.outputDim,Bt(this.outputDim,\"outputDim\"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight(\"embeddings\",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Ku(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!==\"int32\"&&(n=Ip(n,\"int32\"));let s=VI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:at(this.embeddingsRegularizer),activityRegularizer:at(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};by.className=\"Embedding\";ae.registerClass(by);var mi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U(\"Operands could not be broadcast together with shapes \"+JSON.stringify(e)+\" \"+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Ll(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an Array\");if(!Array.isArray(e))throw new U(\"`inputs` should be an Array\");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:On(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},yy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};yy.className=\"Add\";ae.registerClass(yy);var vy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};vy.className=\"Multiply\";ae.registerClass(vy);var xy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};xy.className=\"Average\";ae.registerClass(xy);var wy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Tr(t,e[n]);return t})}};wy.className=\"Maximum\";ae.registerClass(wy);var ky=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=gp(t,e[n]);return t})}};ky.className=\"Minimum\";ae.registerClass(ky);var Iy=class extends mi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \"+JSON.stringify(e))}mergeFunction(e){return j(()=>Nb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U(\"A `Concatenate` layer should be called on a list of inputs.\");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U(\"`mask` should be an array for Concatenate\");if(!Array.isArray(e))throw new U(\"`inputs` should be an array for Concatenate\");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Xn(e[a]),\"bool\")):t[a].rank<e[a].rank?s.push(On(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return Bk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Iy.className=\"Concatenate\";ae.registerClass(Iy);function Su(e,t){for(;e<0;)e+=t;return e}function EB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe(\"batchDot is not implemented for tensors of 4D or higher rank yet\");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n==\"number\"&&(n=[n,n]),e.dtype===\"complex64\"||t.dtype===\"complex64\")throw new Fe(\"batchDot is not implemented for complex64-type Tensors yet.\");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ye(V(e,t),a[0]):o=ye(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=On(o,1)),o})}var Sy=class extends mi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Su(r,e[a].shape.length)):s=[Su(this.axes,t.shape.length),Su(this.axes,n.shape.length)],this.normalize&&(t=Cd(t,s[0]),n=Cd(n,s[1])),EB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Su(this.axes,e.length),Su(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>\"A `Dot` layer should be called on a list of exactly 2 inputs.\");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe(\"Dot layer does not support tensors of 4D or higher rank yet.\");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Sy.className=\"Dot\";ae.registerClass(Sy);var Cy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Vl(()=>ie(Sp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Cy.className=\"GaussianNoise\";ae.registerClass(Cy);var Ny=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Vl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Sp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ny.className=\"GaussianDropout\";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Vl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=jo(Pl(n),this.rate);u=Ip(u,\"float32\");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};Ty.className=\"AlphaDropout\";ae.registerClass(Ty);function Zu(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=vE(e,t,n,s,r,a);else if(e.rank===3)i=wE(e,t,n,s,r,a);else if(e.rank===4)i=IE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function RB(e,t,n,s,r=.001){return j(()=>{let a=Jg(e,s),i=a.mean,o=a.variance;return[Zu(e,i,o,n,t,r),i,o]})}function DB(e,t,n,s,r=.001){return j(()=>{let a=Jg(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Zu(e,l,c,d,p,r),i,o]})}function FB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?RB(e,t,n,s,r):DB(e,t,n,s,r)}var $y=class extends Ge{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.movingMeanInitializer=ht(e.movingMeanInitializer||\"zeros\"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||\"ones\"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight(\"gamma\",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight(\"beta\",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight(\"moving_mean\",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight(\"moving_variance\",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=pa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Zu(s,b,y,v,x,this.epsilon)}else return Zu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=FB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),C=V(ge(k,y),x);b.write(ge(k,C))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$y.className=\"BatchNormalization\";ae.registerClass($y);var _y=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis==\"number\"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||\"zeros\"),this.gammaInitializer=ht(e.gammaInitializer||\"ones\"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis==\"number\"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight(\"gamma\",n,\"float32\",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight(\"beta\",n,\"float32\",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=Jg(n,this.axis,!0),u=pa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=cs(i,d),o=cs(o,d),c=cs(c,h),p=cs(p,h),Zu(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};_y.className=\"LayerNormalization\";ae.registerClass(_y);function OB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");if(n==null&&(n=bs()),n!==\"channelsLast\"&&n!==\"channelsFirst\")throw new U(`Unknown data format: ${n}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat===\"channelsFirst\"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>OB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ay.className=\"ZeroPadding2D\";ae.registerClass(Ay);function Up(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=Yb(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=Zg(e,t,n,o):i=Wg(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,3,1,2])),i})}function A0(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s=\"valid\"),r==null&&(r=bs()),a==null&&(a=\"max\"),e=w0(e,r);let i,o=s===\"same\"?\"same\":\"valid\";return a===\"max\"?i=aI(e,t,n,o):i=Gk(e,t,n,o),r===\"channelsFirst\"&&(i=qe(i,[0,4,1,2,3])),i})}var E0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize==\"number\")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]==\"number\")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,\"poolSize\"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides==\"number\")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]==\"number\")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=ms(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Ll(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,\"channelsLast\");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ey=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,\"max\")}};Ey.className=\"MaxPooling1D\";ae.registerClass(Ey);var Ry=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,\"avg\")}};Ry.className=\"AveragePooling1D\";ae.registerClass(Ry);var R0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,\"max\")}};Dy.className=\"MaxPooling2D\";ae.registerClass(Dy);var Fy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,\"avg\")}};Fy.className=\"AveragePooling2D\";ae.registerClass(Fy);var D0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,\"poolSize\"),Bt(this.strides,\"strides\"),this.padding=e.padding==null?\"valid\":e.padding,this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat===\"channelsFirst\"?e[2]:e[1],n=this.dataFormat===\"channelsFirst\"?e[3]:e[2],s=this.dataFormat===\"channelsFirst\"?e[4]:e[3];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),s=ms(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat===\"channelsFirst\"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,\"max\")}};Oy.className=\"MaxPooling3D\";ae.registerClass(Oy);var Py=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,\"avg\")}};Py.className=\"AveragePooling3D\";ae.registerClass(Py);var F0=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},zy=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return It(n,1)})}};zy.className=\"GlobalAveragePooling1D\";ae.registerClass(zy);var My=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};My.className=\"GlobalMaxPooling1D\";ae.registerClass(My);var O0=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?\"channelsLast\":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat===\"channelsLast\"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ly=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat===\"channelsLast\"?It(n,[1,2]):It(n,[2,3])})}};Ly.className=\"GlobalAveragePooling2D\";ae.registerClass(Ly);var By=class extends 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KW=async(e,t,n)=>{switch(e.op){case\"If\":case\"StatelessIf\":{let s=I(\"thenBranch\",e,t,n),r=I(\"elseBranch\",e,t,n),a=I(\"cond\",e,t,n),i=I(\"args\",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case\"While\":case\"StatelessWhile\":{let s=I(\"body\",e,t,n),r=I(\"cond\",e,t,n),a=I(\"args\",e,t,n),i=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),o=a.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let l=a;for(;u[0];){let c=l;l=await n.functionMap[s].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await 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s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case\"TensorArrayReadV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"index\",e,t,n);return[n.getTensorArray(s.id).read(r)]}case\"TensorArrayGatherV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"dtype\",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case\"TensorArrayScatterV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case\"TensorArrayConcatV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id),a=I(\"dtype\",e,t,n);return[r.concat(a)]}case\"TensorArraySplitV3\":{let s=I(\"tensorArrayId\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"lengths\",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case\"TensorArraySizeV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return[Ie(r.size(),\"int32\")]}case\"TensorArrayCloseV3\":{let s=I(\"tensorArrayId\",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case\"TensorListSetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"tensor\",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case\"TensorListGetItem\":{let s=I(\"tensorListId\",e,t,n),r=I(\"index\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case\"TensorListScatterV2\":case\"TensorListScatter\":{let s=I(\"indices\",e,t,n),r=I(\"tensor\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"numElements\",e,t,n),o=qW(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListReserve\":case\"EmptyTensorList\":{let s=I(\"elementShape\",e,t,n),r=I(\"elementDType\",e,t,n),a;e.op===\"TensorListReserve\"?a=\"numElements\":a=\"maxNumElements\";let i=I(a,e,t,n),o=HW(s,r,i);return n.addTensorList(o),[o.idTensor]}case\"TensorListGather\":{let s=I(\"tensorListId\",e,t,n),r=I(\"indices\",e,t,n),a=I(\"elementShape\",e,t,n),i=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case\"TensorListStack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=I(\"numElements\",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case\"TensorListFromTensor\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n),i=GW(s,r,a);return n.addTensorList(i),[i.idTensor]}case\"TensorListConcat\":{let s=I(\"tensorListId\",e,t,n),r=n.getTensorList(s.id),a=I(\"dtype\",e,t,n),i=I(\"elementShape\",e,t,n);return[r.concat(a,i)]}case\"TensorListPushBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"tensor\",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case\"TensorListPopBack\":{let s=I(\"tensorListId\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"elementDType\",e,t,n);return[n.getTensorList(s.id).popBack(r,a)]}case\"TensorListSplit\":{let s=I(\"tensor\",e,t,n),r=I(\"elementShape\",e,t,n),a=I(\"lengths\",e,t,n),i=jW(s,a,r);return n.addTensorList(i),[i.idTensor]}default:throw TypeError(`Node type ${e.op} is not implemented`)}};function qx(e,t,n){let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=!a,o=r===\"prelu\",u=s===\"fusedbatchnorm\",l=I(\"numArgs\",e,t,n);if(a){if(o&&l!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!o&&a&&l!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(u)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",e,t,n),p=nd(e,t,n),d=I(\"dataFormat\",e,t,n).toUpperCase(),h=I(\"dilations\",e,t,n),[f,m]=I(\"args\",e,t,n);i&&(m=f,f=void 0);let g=I(\"leakyreluAlpha\",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var XW=(e,t,n)=>{switch(e.op){case\"Conv1D\":{let s=I(\"stride\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilation\",e,t,n);return[qk(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,r,a,i)]}case\"Conv2D\":{let s=I(\"strides\",e,t,n),r=nd(e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[ua(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=qx(e,t,n);return[da.conv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"FusedDepthwiseConv2dNative\":{let{stride:s,pad:r,dataFormat:a,dilations:i,biasArg:o,preluArg:u,activationFunc:l,leakyreluAlpha:c}=qx(e,t,n);return[da.depthwiseConv2d({x:I(\"x\",e,t,n),filter:I(\"filter\",e,t,n),strides:[s[1],s[2]],pad:r,dataFormat:a,dilations:[i[1],i[2]],bias:o,activation:l,preluActivationWeights:u,leakyreluAlpha:c})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let s=I(\"outputShape\",e,t,n),r=I(\"strides\",e,t,n),a=nd(e,t,n);return[jk(I(\"x\",e,t,n),I(\"filter\",e,t,n),s,[r[1],r[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let s=I(\"strides\",e,t,n),r=nd(e,t,n),a=I(\"dilations\",e,t,n),i=I(\"dataFormat\",e,t,n).toUpperCase();return[pp(I(\"input\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2]],r,i,[a[1],a[2]])]}case\"Conv3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dataFormat\",e,t,n).toUpperCase(),i=I(\"dilations\",e,t,n);return[Kk(I(\"x\",e,t,n),I(\"filter\",e,t,n),[s[1],s[2],s[3]],r,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Wg(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPool\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Zg(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r)]}case\"MaxPoolWithArgmax\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n),i=I(\"includeBatchInIndex\",e,t,n),{result:o,indexes:u}=sD(I(\"x\",e,t,n),[a[1],a[2]],[s[1],s[2]],r,i);return[o,u]}case\"AvgPool3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[Gk(I(\"x\",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case\"MaxPool3D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"kernelSize\",e,t,n);return[aI(I(\"x\",e,t,n),[a[1],a[2],a[3]],[s[1],s[2],s[3]],r)]}case\"Dilation2D\":{let s=I(\"strides\",e,t,n),r=I(\"pad\",e,t,n),a=I(\"dilations\",e,t,n),i=s[1],o=s[2],u=a[1],l=a[2];return[sR(I(\"x\",e,t,n),I(\"filter\",e,t,n),[i,o],r,[u,l],\"NHWC\")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},YW=(e,t,n)=>{switch(e.op){case\"Fill\":{let s=I(\"shape\",e,t,n),r=I(\"dtype\",e,t,n),a=I(\"value\",e,t,n);return[Fl(s,a,r)]}case\"LinSpace\":{let s=I(\"start\",e,t,n),r=I(\"stop\",e,t,n),a=I(\"num\",e,t,n);return[DR(s,r,a)]}case\"Multinomial\":{let s=I(\"logits\",e,t,n),r=I(\"numSamples\",e,t,n),a=I(\"seed\",e,t,n);return[gD(s,r,a)]}case\"OneHot\":{let s=I(\"indices\",e,t,n),r=I(\"depth\",e,t,n),a=I(\"onValue\",e,t,n),i=I(\"offValue\",e,t,n);return[yd(s,r,a,i)]}case\"Ones\":return[zn(I(\"shape\",e,t,n),I(\"dtype\",e,t,n))];case\"OnesLike\":return[Xn(I(\"x\",e,t,n))];case\"RandomUniform\":return[Pl(I(\"shape\",e,t,n),I(\"minval\",e,t,n),I(\"maxval\",e,t,n),I(\"dtype\",e,t,n))];case\"Range\":{let s=I(\"start\",e,t,n),r=I(\"stop\",e,t,n),a=I(\"step\",e,t,n);return[Xu(s,r,a,I(\"dtype\",e,t,n))]}case\"TruncatedNormal\":{let s=I(\"shape\",e,t,n),r=I(\"mean\",e,t,n),a=I(\"stdDev\",e,t,n),i=I(\"seed\",e,t,n);return[lb(s,r,a,I(\"dtype\",e,t,n),i)]}case\"Zeros\":return[$t(I(\"shape\",e,t,n),I(\"dtype\",e,t,n))];case\"ZerosLike\":return[je(I(\"x\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function Uf(e,t,n){let s=I(\"boxes\",e,t,n),r=I(\"scores\",e,t,n),a=I(\"maxOutputSize\",e,t,n),i=I(\"iouThreshold\",e,t,n),o=I(\"scoreThreshold\",e,t,n),u=I(\"softNmsSigma\",e,t,n);return{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}}var QW=async(e,t,n)=>{switch(e.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o,softNmsSigma:u}=Uf(e,t,n),l=await ds.nonMaxSuppressionWithScoreAsync(s,r,a,i,o,u);return[l.selectedIndices,l.selectedScores]}case\"NonMaxSuppressionV4\":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o}=Uf(e,t,n),u=I(\"padToMaxOutputSize\",e,t,n),l=await ds.nonMaxSuppressionPaddedAsync(s,r,a,i,o,u);return[l.selectedIndices,l.validOutputs]}case\"NonMaxSuppressionV3\":case\"NonMaxSuppressionV2\":{let{boxes:s,scores:r,maxOutputSize:a,iouThreshold:i,scoreThreshold:o}=Uf(e,t,n);return[await ds.nonMaxSuppressionAsync(s,r,a,i,o)]}case\"Where\":{let s=ce(I(\"condition\",e,t,n),\"bool\"),r=[await bI(s)];return s.dispose(),r}case\"ListDiff\":return t3(I(\"x\",e,t,n),I(\"y\",e,t,n));default:throw TypeError(`Node type ${e.op} is not implemented`)}},ZW=(e,t,n)=>{switch(e.op){case\"TopKV2\":{let s=I(\"x\",e,t,n),r=I(\"k\",e,t,n),a=I(\"sorted\",e,t,n),i=N3(s,r,a);return[i.values,i.indices]}case\"Unique\":{let s=I(\"x\",e,t,n),r=cx(s);return[r.values,r.indices]}case\"UniqueV2\":{let s=I(\"x\",e,t,n),r=I(\"axis\",e,t,n),a=cx(s,r);return[a.values,a.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},JW=(e,t,n)=>{switch(e.op){case\"Const\":return t[e.name];case\"PlaceholderWithDefault\":let s=I(\"default\",e,t,n);return[on(e.name,t,n)||s];case\"Placeholder\":return[on(e.name,t,n)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let l=I(\"x\",e,t,n);return[Us(l)]}case\"IdentityN\":return I(\"x\",e,t,n).map(l=>Us(l));case\"Snapshot\":let r=I(\"x\",e,t,n);return[Us(r)];case\"Shape\":return[Qt(I(\"x\",e,t,n).shape,\"int32\")];case\"ShapeN\":return I(\"x\",e,t,n).map(l=>Qt(l.shape));case\"Size\":return[Ie(I(\"x\",e,t,n).size,\"int32\")];case\"Rank\":return[Ie(I(\"x\",e,t,n).rank,\"int32\")];case\"NoOp\":return[Ie(1)];case\"Print\":let a=I(\"x\",e,t,n),i=I(\"data\",e,t,n),o=I(\"message\",e,t,n),u=I(\"summarize\",e,t,n);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(o);for(let l=0;l<i.length;l++)console.log(Array.prototype.slice.call(i[l].dataSync()).slice(0,u));return[a];default:throw TypeError(`Node type ${e.op} is not implemented`)}},e4=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Ie(0),this.tensorMap=new Map,Ht(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ie(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return this.tensorMap.forEach(s=>s.dispose()),this.tensorMap.clear(),j(()=>{let s=Fs(t),r=n.length,a=s.length;w.assert(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let i=0;i<r;i++){let o=n[i],u=s[i];Ht(u),this.tensorMap.set(o,u)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let n=await e.data();return j(()=>{let s=[];for(let r=0;r<n.length;r++){let a=n[r],i=this.findWithDefault(a,t);s.push(i)}return Qn(s)})}findWithDefault(e,t){let n=this.tensorMap.get(e);return n!=null?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},t4=async(e,t,n,s)=>{switch(e.op){case\"HashTable\":case\"HashTableV2\":{let r=I(\"keyDType\",e,t,n),a=I(\"valueDType\",e,t,n),i=new e4(r,a);return s.addHashTable(e.name,i),[i.handle]}case\"LookupTableImport\":case\"LookupTableImportV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"values\",e,t,n);return[await s.getHashTableById(r.id).import(a,i)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let r=I(\"tableHandle\",e,t,n,s),a=I(\"keys\",e,t,n),i=I(\"defaultValue\",e,t,n);return[await s.getHashTableById(r.id).find(a,i)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let r=I(\"tableHandle\",e,t,n,s);return[s.getHashTableById(r.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},n4=(e,t,n)=>{switch(e.op){case\"ResizeBilinear\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[ds.resizeBilinear(s,[r[0],r[1]],a,i)]}case\"ResizeNearestNeighbor\":{let s=I(\"images\",e,t,n),r=I(\"size\",e,t,n),a=I(\"alignCorners\",e,t,n),i=I(\"halfPixelCenters\",e,t,n);return[ds.resizeNearestNeighbor(s,[r[0],r[1]],a,i)]}case\"CropAndResize\":{let s=I(\"image\",e,t,n),r=I(\"boxes\",e,t,n),a=I(\"boxInd\",e,t,n),i=I(\"cropSize\",e,t,n),o=I(\"method\",e,t,n),u=I(\"extrapolationValue\",e,t,n);return[ds.cropAndResize(s,r,a,i,o,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},s4=(e,t,n)=>{switch(e.op){case\"Equal\":return[qn(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"NotEqual\":return[Ku(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Greater\":return[Wn(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"GreaterEqual\":return[jo(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Less\":return[Jk(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LessEqual\":return[Ko(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LogicalAnd\":return[Ds(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"LogicalNot\":return[Qg(I(\"a\",e,t,n))];case\"LogicalOr\":return[rI(I(\"a\",e,t,n),I(\"b\",e,t,n))];case\"Select\":case\"SelectV2\":return[vn(I(\"condition\",e,t,n),I(\"a\",e,t,n),I(\"b\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},r4=(e,t,n)=>{switch(e.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[We(I(\"a\",e,t,n),I(\"b\",e,t,n),I(\"transposeA\",e,t,n),I(\"transposeB\",e,t,n))];case\"Einsum\":return[dR(I(\"equation\",e,t,n),...I(\"tensors\",e,t,n))];case\"Transpose\":return[qe(I(\"x\",e,t,n),I(\"perm\",e,t,n))];case\"_FusedMatMul\":let[s,r]=I(\"fusedOps\",e,t,n),a=s===\"biasadd\",i=r===\"prelu\",o=I(\"numArgs\",e,t,n),u=I(\"leakyreluAlpha\",e,t,n);if(a){if(i&&o!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&o!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[l,c]=I(\"args\",e,t,n);return[da.matMul({a:I(\"a\",e,t,n),b:I(\"b\",e,t,n),transposeA:I(\"transposeA\",e,t,n),transposeB:I(\"transposeB\",e,t,n),bias:l,activation:r,preluActivationWeights:c,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},a4=(e,t,n)=>{switch(e.op){case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[qu(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"FusedBatchNormV3\":return[qu(I(\"x\",e,t,n),I(\"mean\",e,t,n),I(\"variance\",e,t,n),I(\"offset\",e,t,n),I(\"scale\",e,t,n),I(\"epsilon\",e,t,n))];case\"LRN\":return[OR(I(\"x\",e,t,n),I(\"radius\",e,t,n),I(\"bias\",e,t,n),I(\"alpha\",e,t,n),I(\"beta\",e,t,n))];case\"Softmax\":return[ib(I(\"x\",e,t,n))];case\"LogSoftmax\":return[eI(I(\"x\",e,t,n))];case\"SparseToDense\":return[xI(I(\"sparseIndices\",e,t,n),I(\"outputShape\",e,t,n),I(\"sparseValues\",e,t,n),I(\"defaultValue\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},i4=(e,t,n)=>{switch(e.op){case\"Max\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[As(I(\"x\",e,t,n),i,o)]}case\"Mean\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[It(I(\"x\",e,t,n),i,o)]}case\"Min\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[pm(I(\"x\",e,t,n),i,o)]}case\"Sum\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[ye(I(\"x\",e,t,n),i,o)]}case\"All\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[Bk(I(\"x\",e,t,n),i,o)]}case\"Any\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[cm(I(\"x\",e,t,n),i,o)]}case\"ArgMax\":{let i=I(\"axis\",e,t,n);return[Gu(I(\"x\",e,t,n),i)]}case\"ArgMin\":{let i=I(\"axis\",e,t,n);return[GA(I(\"x\",e,t,n),i)]}case\"Prod\":{let i=I(\"axis\",e,t,n),o=I(\"keepDims\",e,t,n);return[iI(I(\"x\",e,t,n),i,o)]}case\"Cumsum\":{let i=I(\"axis\",e,t,n),o=I(\"exclusive\",e,t,n),u=I(\"reverse\",e,t,n);return[Qk(I(\"x\",e,t,n),i,o,u)]}case\"Bincount\":let s=I(\"x\",e,t,n),r=I(\"weights\",e,t,n),a=I(\"size\",e,t,n);return[Hk(s,r,a)];case\"DenseBincount\":{let i=I(\"x\",e,t,n),o=I(\"weights\",e,t,n),u=I(\"size\",e,t,n),l=I(\"binaryOutput\",e,t,n);return[QE(i,o,u,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},o4=(e,t,n)=>{switch(e.op){case\"ConcatV2\":case\"Concat\":{let s=I(\"n\",e,t,n),r=I(\"axis\",e,t,n),a=I(\"tensors\",e,t,n);return a=a.slice(0,s),[Ft(a,r)]}case\"Gather\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[ju(s,ce(r,\"int32\"),0)]}case\"GatherV2\":{let s=I(\"axis\",e,t,n),r=I(\"batchDims\",e,t,n),a=I(\"x\",e,t,n),i=I(\"indices\",e,t,n);return[ju(a,ce(i,\"int32\"),s,r)]}case\"Reverse\":{let s=I(\"dims\",e,t,n),r=[];for(let i=0;i<s.length;i++)s[i]&&r.push(i);let a=I(\"x\",e,t,n);return[Yn(a,r)]}case\"ReverseV2\":{let s=I(\"axis\",e,t,n),r=I(\"x\",e,t,n);return[Yn(r,s)]}case\"Slice\":{let s=I(\"begin\",e,t,n),r=I(\"size\",e,t,n);return[He(I(\"x\",e,t,n),s,r)]}case\"StridedSlice\":{let s=I(\"begin\",e,t,n),r=I(\"end\",e,t,n),a=I(\"strides\",e,t,n),i=I(\"beginMask\",e,t,n),o=I(\"endMask\",e,t,n),u=I(\"ellipsisMask\",e,t,n),l=I(\"newAxisMask\",e,t,n),c=I(\"shrinkAxisMask\",e,t,n),p=I(\"x\",e,t,n);return[k3(p,s,r,a,i,o,u,l,c)]}case\"Pack\":return j(()=>{let s=I(\"axis\",e,t,n),r=I(\"tensors\",e,t,n),a=r[0].shape,i=mr(r[0]).shape,o=r.map(u=>{let l=w.arraysEqual(u.shape,a);if(!l&&!w.arraysEqual(mr(u).shape,i))throw new Error(\"the input tensors shape does not match\");return l?u:G(u,a)});return[Qn(o,s)]});case\"Unpack\":{let s=I(\"axis\",e,t,n),r=I(\"tensor\",e,t,n);return Fs(r,s)}case\"Tile\":{let s=I(\"reps\",e,t,n);return[cs(I(\"x\",e,t,n),s)]}case\"Split\":case\"SplitV\":{let s=I(\"axis\",e,t,n),r=I(\"numOrSizeSplits\",e,t,n),a=I(\"x\",e,t,n);return Ln(a,r,s)}case\"ScatterNd\":{let s=I(\"indices\",e,t,n),r=I(\"values\",e,t,n),a=I(\"shape\",e,t,n);return[M3(s,r,a)]}case\"GatherNd\":{let s=I(\"x\",e,t,n),r=I(\"indices\",e,t,n);return[W3(s,r)]}case\"SparseToDense\":{let s=I(\"sparseIndices\",e,t,n),r=I(\"outputShape\",e,t,n),a=I(\"sparseValues\",e,t,n),i=I(\"defaultValue\",e,t,n);return[xI(s,a,r,a.dtype===i.dtype?i:ce(i,a.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},u4=(e,t,n)=>{switch(e.op){case\"SparseFillEmptyRows\":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Vc.sparseFillEmptyRows(I(\"indices\",e,t,n),I(\"values\",e,t,n),I(\"denseShape\",e,t,n),I(\"defaultValue\",e,t,n));return[s,r,a,i]}case\"SparseReshape\":{let{outputIndices:s,outputShape:r}=Vc.sparseReshape(I(\"inputIndices\",e,t,n),I(\"inputShape\",e,t,n),I(\"newShape\",e,t,n));return[s,r]}case\"SparseSegmentMean\":return[Vc.sparseSegmentMean(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];case\"SparseSegmentSum\":return[Vc.sparseSegmentSum(I(\"data\",e,t,n),I(\"indices\",e,t,n),I(\"segmentIds\",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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j(()=>VW(a,i,o));case\"basic_math\":return j(()=>WW(a,i,o));case\"control\":return KW(a,i,o);case\"convolution\":return j(()=>XW(a,i,o));case\"creation\":return j(()=>YW(a,i,o));case\"dynamic\":return QW(a,i,o);case\"evaluation\":return j(()=>ZW(a,i,o));case\"image\":return j(()=>n4(a,i,o));case\"graph\":return j(()=>JW(a,i,o));case\"logical\":return j(()=>s4(a,i,o));case\"matrices\":return j(()=>r4(a,i,o));case\"normalization\":return j(()=>a4(a,i,o));case\"reduction\":return j(()=>i4(a,i,o));case\"slice_join\":return j(()=>o4(a,i,o));case\"sparse\":return j(()=>u4(a,i,o));case\"spectral\":return j(()=>l4(a,i,o));case\"string\":return j(()=>c4(a,i,o));case\"transformation\":return j(()=>d4(a,i,o));case\"hash_table\":return t4(a,i,o,s);case\"custom\":let u=W0(a.op);if(u&&u.customExecutor)return u.customExecutor(new BW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.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 w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Kx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(\"\"),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.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)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let 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))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}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(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Xx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>$n(d)[0]),c=[];s!=null&&(c=s.map(d=>$n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((cS(d)||g4(d)||b4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function p4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>$n(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var h4=[\"Switch\",\"Merge\",\"Enter\",\"Exit\",\"NextIteration\",\"StatelessIf\",\"StatelessWhile\",\"if\",\"While\"],f4=[\"NonMaxSuppressionV2\",\"NonMaxSuppressionV3\",\"NonMaxSuppressionV5\",\"Where\"],m4=[\"HashTable\",\"HashTableV2\",\"LookupTableImport\",\"LookupTableImportV2\",\"LookupTableFind\",\"LookupTableFindV2\",\"LookupTableSize\",\"LookupTableSizeV2\"];function cS(e){return h4.indexOf(e.op)>=0}function g4(e){return f4.indexOf(e.op)>=0}function b4(e){return m4.indexOf(e.op)>=0}var Om=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=\",\",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Om(e.functions[n],this)})}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){let t=Object.keys(e).map(n=>e[n].map(s=>s.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=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+\"--\"+s.join(this.SEPERATOR)}compile(e,t){let n=Xx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return p4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[$n(c)[0]]),r=t.map(c=>$n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Kx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=$n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=jx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>on(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category===\"control\"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!==\"control\"){let u=vW(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new Kx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>on(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[$n(y)[0]]),i=n.map(y=>$n(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=Xx(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=$n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);await Promise.all(y)}c==null&&!s&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let b=o.filter(y=>!cS(y)&&!on(y.name,h,t)).map(y=>y.name);if(b.length>0){let y=\"\";throw c!=null&&(y=`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 [${r}]. 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t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Om(Ux.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Ux.Instance.transformGraph(e.modelInitializer);this.initializer=new Om(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e==\"string\"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return 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empty.\");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},mS=class extends fS{constructor(){super(mS.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},gS=mS;gS.INITIAL_CAPACITY=32;function bS(e){return new D4(e)}function Ky(e){return new F4(e)}function E4(e,t){return new yS(e,t)}function R4(e,t=vS.FAIL){return new U4(e,t)}var Ut=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new V4(this,e)}filter(e){return new L4(this,e)}map(e){return new B4(this,e)}mapAsync(e){return new Yx(this,e)}serialMapAsync(e){return new Yx(this,e).serial()}flatmap(e){return new W4(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new M4(this,e,t)}columnMajorBatch(e,t=!0,n=pS){return this.rowMajorBatch(e,t).map(r=>N4(r,n))}concatenate(e,t){return new yS(bS([this,e]),t)}take(e){return e<0||e==null?this:new z4(this,e)}skip(e){return e<0||e==null?this:new P4(this,e)}prefetch(e){return new xS(this,e)}shuffle(e,t){return new G4(this,e,t)}serial(){return new O4(this)}},D4=class extends Ut{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async 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serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Re(e.value)}return this.upstream.next()}},z4=class extends Ut{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},M4=class extends Ut{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},L4=class extends Ut{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Re(e.value)}}},B4=class extends Ut{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},V4=class extends Ut{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Yx=class extends Ut{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=_s.getTensorsInContainer(e.value),n=await this.transform(e.value),s=_s.getTensorsInContainer(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Xy=class extends Ut{constructor(){super();this.outputQueue=new gS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},W4=class extends Xy{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=_s.getTensorsInContainer(e.value),n=this.transform(e.value),s=_s.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)_s.isTensorInList(r,s)||r.dispose();return!0}},yS=class extends Ut{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return\"TODO: fill in upstream of chained summaries -> Chained\"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},vS=(e=>(e[e.FAIL=0]=\"FAIL\",e[e.SHORTEST=1]=\"SHORTEST\",e[e.LONGEST=2]=\"LONGEST\",e))(vS||{}),U4=class extends Ut{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return\"{TODO: fill in upstream of zip summaries} -> Zip\"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof Ut?{value:a.next().then(o=>(t++,o.done&&n++,o.value)),recurse:!1}:{value:null,recurse:!0}}let r=await hS(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. 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q4(e){if(!Ji(e))throw new Error(\"The argument to zip() must be an object or array.\");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Tn(async()=>{let n=await hS(e,s=>{if(s instanceof Zo)return{value:s.iterator(),recurse:!1};if(Ji(s))return{value:null,recurse:!0};throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\")});return R4(n,1)},t)}function j4(e){if(e===null)return null;let t=e[0];return T4(t)?{value:K4(e),recurse:!1}:{value:null,recurse:!0}}function K4(e){if(e.length===0)throw new Error(\"Can't make a batch of zero elements.\");return e[0]instanceof et?Qn(e):hs(e)}var wS=class extends Zo{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`\n`).map(s=>(s.endsWith(\"\\r\")&&(s=s.slice(0,-1)),s))}},jc='\"',Nu=Symbol(\"out\"),Qx=Symbol(\"field\"),Kc=Symbol(\"quote\"),Gf=Symbol(\"quoteafterquote\"),Zx=Symbol(\"quoteinquote\"),kS=class extends Zo{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=\",\",this.delimWhitespace=!1,this.base=new wS(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>\"Delimiter should not be provided when delimWhitespace is true.\"),this.delimWhitespace=!0,this.delimiter=\" \"):this.delimiter=t.delimiter?t.delimiter:\",\"}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error(\"Column names must be provided if there is no header line.\");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>\"The length of provided columnNames (\"+this.fullColumnNames.length.toString()+\") does not match the length of the header line read from file (\"+e.length.toString()+\").\"),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>\"Duplicate column names found: \"+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key \"'+s+'\" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+\").\")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error(\"No data was found for CSV parsing.\");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o===\"\")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype===\"bool\"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else 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jc:a=Kc;break;default:}break;default:}if(a===Gf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},IS=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\")}summary(){return\"microphone\"}static async create(e={}){if(X().get(\"IS_NODE\"))throw new Error(\"microphone API is only supported in browser environment.\");let t=new IS(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error(\"Could not obtain audio from microphone.\");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error(\"Can not convert infinite audio stream to array.\")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),hs(n,t)}},SS=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Qt([0],\"int32\"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=ji([a,r,o,i],[1,4])}else this.cropBox=ji([0,0,1,1],[1,4])}summary(){return\"webcam\"}static async create(e,t={}){if(X().get(\"IS_NODE\"))throw new Error(\"tf.data.webcam is only supported in browser environment.\");if(!e){if(e=document.createElement(\"video\"),!t.resizeWidth||!t.resizeHeight)throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new SS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode===\"user\"||this.webcamConfig.facingMode===\"environment\",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:\"user\",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error(\"Could not obtain video from webcam.\");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=xk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=On(ce(e,\"float32\"),0),n;n=ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,\"bilinear\");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error(\"Can not convert infinite video stream to array.\")}},CS=class{},NS=class extends Ut{split(e){return new X4(this,e)}},X4=class extends NS{constructor(e,t){super();this.upstream=e,this.impl=new Y4(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y4=class extends Xy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=\"\"}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===\"\"?!1:(this.outputQueue.push(this.carryover),this.carryover=\"\",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Q4=class extends Ut{decodeUTF8(){return new Z4(this)}},Z4=class extends NS{constructor(e){super();this.upstream=e,this.impl=new J4(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},J4=class extends Xy{constructor(e){super();if(this.upstream=e,X().get(\"IS_BROWSER\"))this.decoder=new 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o=e.locToIndex(i);s.values[r]=e.values[o]}return s}var Eu=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function fC(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let o=s-n+1,u=t-n+1,l=Math.log(o),c=.5*Math.exp(2*l/3),p=.5*Math.sqrt(l*c*(o-c)/o)*Math.sign(u-o/2),d=Math.max(n,Math.floor(t-u*c/o+p)),h=Math.min(s,Math.floor(t+(o-u)*c/o+p));fC(e,t,d,h)}let r=e[t],a=n,i=s;for(w.swap(e,n,t),Eu(e[s],r)>0&&w.swap(e,n,s);a<i;){for(w.swap(e,a,i),a++,i--;Eu(e[a],r)<0;)a=a+1;for(;Eu(e[i],r)>0;)i=i-1}Eu(e[n],r)===0?w.swap(e,n,i):(i=i+1,w.swap(e,i,s)),i<=t&&(n=i+1),t<=i&&(s=i-1)}}function mC(e,t,n,s,r){let a=t[t.length-1],[i,o]=[e.length/a,a],u=w.getTypedArrayFromDType(n,i*s),l=w.getTypedArrayFromDType(\"int32\",i*s);for(let p=0;p<i;p++){let d=p*o,h=e.subarray(d,d+o),f=new Array(h.length);h.forEach((y,v)=>f[v]={value:y,index:v}),s<f.length&&(fC(f,s),f=f.slice(0,s)),r&&f.sort(Eu);let m=p*s,g=u.subarray(m,m+s),b=l.subarray(m,m+s);for(let y=0;y<s;y++)g[y]=f[y].value,b[y]=f[y].index}let c=t.slice();return c[c.length-1]=s,[De(c,n,u),De(c,\"int32\",l)]}function gC(e,t,n,s){let r=w.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let i={},o=new Int32Array(n[r]),u=new Vt(a,s,e),l=[],c=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(c)m=e[f].toString();else{let g=[];for(let b=0;b<a[0];b++)for(let y=0;y<a[2];y++)g.push(u.get(b,f,y));m=g.join(\",\")}if(i[m]!==void 0)o[f]=i[m];else{let g=Object.keys(i).length;i[m]=g,o[f]=g,l.push(f)}}let p=a.slice();p[1]=Object.keys(i).length;let d=new Vt(p,s);l.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let b=0;b<a[2];b++)d.set(u.get(g,f,b),g,m,b)});let h=n.slice();return h[r]=p[1],{outputValues:d.values,outputShape:h,indices:o}}var vpe=\"0.0.0\";dp(\"cpu\",()=>new RS,1);var bC=st(Ra,e=>e>=0?e:Math.exp(e)-1),uG={kernelName:Ra,backendName:\"cpu\",kernelFunc:bC};function yC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;ve([r],\"leakyRelu\");let i=w.sizeFromShape(r.shape),o=n.data.get(r.dataId).values,u=w.getTypedArrayFromDType(\"float32\",i);for(let l=0;l<o.length;l++)u[l]=o[l]<0?a*o[l]:o[l];return n.makeTensorInfo(r.shape,\"float32\",u)}var lG={kernelName:La,backendName:\"cpu\",kernelFunc:yC},cG=At((e,t)=>e<0?t*e:e);function vC(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;ve([s,r],\"prelu\");let a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,[o,u]=cG(s.shape,r.shape,a,i,\"float32\");return n.makeTensorInfo(u,\"float32\",o)}var dG={kernelName:Qa,backendName:\"cpu\",kernelFunc:vC},xC=st(Za,e=>Math.max(0,e)),pG={kernelName:Za,backendName:\"cpu\",kernelFunc:xC},wC=st(ei,e=>Math.min(Math.max(0,e),6)),hG={kernelName:ei,backendName:\"cpu\",kernelFunc:wC};function av(e,t,n,s,r){if(n===\"linear\")return Os({inputs:{x:t},backend:e});if(n===\"relu\")return xC({inputs:{x:t},backend:e});if(n===\"elu\")return bC({inputs:{x:t},backend:e});if(n===\"relu6\")return wC({inputs:{x:t},backend:e});if(n===\"prelu\")return vC({inputs:{x:t,alpha:s},backend:e});if(n===\"leakyrelu\")return yC({inputs:{x:t},backend:e,attrs:{alpha:r}});if(n===\"sigmoid\")return rC({inputs:{x:t},backend:e});throw new Error(`Activation ${n} has not been implemented for the CPU backend.`)}function mt(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=w.sizeFromShape(r.shape),o=w.inferFromImplicitShape(a,i),u=w.sizeFromShape(o);w.assert(i===u,()=>`The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. 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mG={kernelName:Ia,backendName:\"cpu\",kernelFunc:kC};function gG(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d,h,f,m=[];d=kC({inputs:{a:r,b:a},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=jl({inputs:{a:d,b:i},backend:n}),m.push(d),d=h),c&&(f=av(n,d,c,o,p),m.push(d),d=f);for(let b of m)n.disposeIntermediateTensorInfo(b);return d}var bG={kernelName:na,backendName:\"cpu\",kernelFunc:gG},yG=st(nl,e=>Math.acos(e)),vG={kernelName:nl,backendName:\"cpu\",kernelFunc:yG},xG=st(sl,e=>Math.acosh(e)),wG={kernelName:sl,backendName:\"cpu\",kernelFunc:xG};function kG(e){let{inputs:t,backend:n}=e,s=t;ve(t,\"addN\");let r=s.map(o=>n.data.get(o.dataId).values),a=De(s[0].shape,s[0].dtype),i=a.values;for(let o=0;o<s.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var IG={kernelName:xa,backendName:\"cpu\",kernelFunc:kG};function 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o=w.parseAxisParam(a,r.shape),u=o,l=N.getAxesPermutation(u,r.shape.length),c=r;l!=null&&(c=Vn({inputs:{x:r},backend:n,attrs:{perm:l}}),u=N.getInnerMostAxes(u.length,r.shape.length)),N.assertAxesAreInnerMostDims(\"any\",u,c.shape.length);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=w.sizeFromShape(d),f=w.makeZerosTypedArray(w.sizeFromShape(p),c.dtype),m=n.data.get(c.dataId).values;for(let b=0;b<f.length;++b){let y=b*h,v=m[y];for(let x=0;x<h;++x){let k=m[y+x];v=v||k}f[b]=v}l!=null&&n.disposeIntermediateTensorInfo(c);let g=n.makeTensorInfo(p,c.dtype,f);if(i){let b=N.expandShapeToKeepDim(p,o),y=mt({inputs:{x:g},backend:n,attrs:{shape:b}});return n.disposeIntermediateTensorInfo(g),y}return g}var TG={kernelName:al,backendName:\"cpu\",kernelFunc:NG};function $G(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;ve(r,\"argMax\");let 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EG={kernelName:il,backendName:\"cpu\",kernelFunc:AG},RG=st(ol,e=>Math.asin(e)),DG={kernelName:ol,backendName:\"cpu\",kernelFunc:RG},FG=st(ul,e=>Math.asinh(e)),OG={kernelName:ul,backendName:\"cpu\",kernelFunc:FG},PG=st(ll,e=>Math.atan(e)),zG={kernelName:ll,backendName:\"cpu\",kernelFunc:PG},MG=At((e,t)=>Math.atan2(e,t)),LG=Gt(dl,MG),BG={kernelName:dl,backendName:\"cpu\",kernelFunc:LG},VG=st(cl,e=>Math.atanh(e)),WG={kernelName:cl,backendName:\"cpu\",kernelFunc:VG};function iv(e,t,n,s,r,a){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a===\"max\"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=De(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,C=x*s[0];for(let T=0;T<r.inChannels;++T)for(let E=0;E<r.outHeight;++E){let 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W=z-T,q=m.get(g,F,z,b);q>P&&(P=q,r?R=a?((g*s.inHeight+F)*s.inWidth+z)*s.inChannels+b:(F*s.inWidth+z)*s.inChannels+b:R=$*d+W)}}i.set(R,g,y,C,b)}}return i}function SC(e,t,n,s,r,a){let i=r.strideDepth,o=r.strideHeight,u=r.strideWidth,l=r.dilationDepth,c=r.dilationHeight,p=r.dilationWidth,d=r.effectiveFilterDepth,h=r.effectiveFilterHeight,f=r.effectiveFilterWidth,m=r.padInfo.front,g=r.padInfo.top,b=r.padInfo.left,y=a===\"max\"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,v=De(r.outShape,n),x=v.values,k=r.outShape[1]*r.outShape[2]*r.outShape[3]*r.outShape[4],C=r.outShape[2]*r.outShape[3]*r.outShape[4],T=r.outShape[3]*r.outShape[4],E=r.outShape[4];for(let A=0;A<r.batchSize;++A){let P=A*k,R=A*s[0];for(let F=0;F<r.inChannels;++F)for(let $=0;$<r.outDepth;++$){let z=$*i-m,W=z;for(;W<0;)W+=l;let q=Math.min(r.inDepth,d+z),K=P+$*C;for(let Y=0;Y<r.outHeight;++Y){let Z=Y*o-g,te=Z;for(;te<0;)te+=c;let ee=Math.min(r.inHeight,h+Z),se=K+Y*T;for(let ne=0;ne<r.outWidth;++ne){let oe=ne*u-b,re=oe;for(;re<0;)re+=p;let le=Math.min(r.inWidth,f+oe),me=se+ne*E,we=y,Se=0,Ee=0;for(let Xe=W;Xe<q;Xe+=l){let Je=R+Xe*s[1];for(let Ye=te;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=re;Ce<le;Ce+=p){let ut=tt+Ce*s[3],rt=e[ut+F];if(a===\"max\"&&rt>we?we=rt:a===\"avg\"&&(Se+=rt,Ee++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Pe=me+F;x[Pe]=a===\"avg\"?Se/Ee:we}}}}return v}function UG(e,t){let n=De(t.outShape,\"int32\"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let C=k*r-h,T=C;for(;T<0;)T+=o;let E=Math.min(t.inHeight,c+C);for(let A=0;A<t.outWidth;++A){let P=A*a-f,R=P;for(;R<0;)R+=u;let 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c=N.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,C=c.effectiveFilterWidth,T=x-1-c.padInfo.front,E=C-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,\"float32\"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-T,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<C;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var XG={kernelName:ag,backendName:\"cpu\",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;ve([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,\"float32\"),C=1/(h*f),T=n.data.get(r.dataId).values,E=De(r.shape,\"float32\",T);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*C,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var QG={kernelName:rg,backendName:\"cpu\",kernelFunc:YG};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var JG={kernelName:Pa,backendName:\"cpu\",kernelFunc:ZG};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;ve([r],\"batchToSpaceND\");let o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Vn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=fa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var tH={kernelName:io,backendName:\"cpu\",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=Zy(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var sH={kernelName:ig,backendName:\"cpu\",kernelFunc:nH};function rH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],\"int32\",Int32Array.from(o))}var aH={kernelName:og,backendName:\"cpu\",kernelFunc:rH},iH=st(Ir,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),oH={kernelName:Ir,backendName:\"cpu\",kernelFunc:iH},uH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let c=o[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,\"float32\")},lH={kernelName:jd,backendName:\"cpu\",kernelFunc:uH};function 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l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let we=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)T[we+ut]+=Ce*C[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var yH={kernelName:Kd,backendName:\"cpu\",kernelFunc:bH};function vH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;ve([r,a],\"conv3dBackpropFilterV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,\"float32\"),v=y.values,[x,k,C,T]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),we=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*C+we;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*T+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let rt=ut*z,Zt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+rt,Jt=Nt*P+Zt;for(let Sn=le;Sn<me;++Sn){let Xt=(re+Sn*h-te)*q+Et,Rn=Sn*R+Jt;for(let en=Ee;en<Pe;++en){let Ls=(Se+en*f-Z)*K+Xt,xi=en*F+Rn;Ce+=$[Ls+Je]*E[xi+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var xH={kernelName:lg,backendName:\"cpu\",kernelFunc:vH};function wH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;ve([r],\"conv3dBackpropInputV2\");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,\"float32\"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,C]=l,T=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,we=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let Je=Xe-me,Ye=Math.max(0,Math.ceil(Je/oe)),tt=Math.min(ee,($+Je)/oe);for(let Ce=0;Ce<Y;++Ce){let ut=Ce-we,rt=Math.max(0,Math.ceil(ut/re)),Zt=Math.min(se,(z+ut)/re);for(let Nt=0;Nt<Z;++Nt){let In=Nt-Se,Et=Math.max(0,Math.ceil(In/le)),Jt=Math.min(ne,(W+In)/le),Sn=0;for(let Cn=Ye;Cn<tt;++Cn){let Xt=Cn*oe-Je;for(let Rn=rt;Rn<Zt;++Rn){let en=Rn*re-ut;for(let Ms=Et;Ms<Jt;++Ms){let Ls=Ms*le-In,xi=v*Ee+x*Cn+k*Rn+C*Ms,Js=E*($-1-Xt)+A*(z-1-en)+P*(W-1-Ls)+R*Pe;for(let Bs=0;Bs<te;++Bs){let 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$H={kernelName:lo,backendName:\"cpu\",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;ve(r,\"cumsum\");let u=N.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=N.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);let p=yn(l.dtype,\"int32\"),d=w.makeZerosTypedArray(w.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,f=l.shape[l.shape.length-1],m=o?(b,y)=>b+f-y-1:(b,y)=>b+y;for(let b=0;b<h.length;b+=f)for(let y=0;y<f;y++){let v=m(b,y);if(y===0)d[v]=i?0:h[v];else{let x=m(b,y-1);d[v]=i?h[x]+d[x]:h[v]+d[x]}}let g=n.makeTensorInfo(l.shape,p,d);if(u!=null){let b=N.getUndoAxesPermutation(u),y=Vn({inputs:{x:g},backend:n,attrs:{perm:b}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(l),y}return g}var AH={kernelName:uo,backendName:\"cpu\",kernelFunc:_H};function EH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=Zy(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=OS(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var RH={kernelName:dg,backendName:\"cpu\",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i===\"NHWC\",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,C=new Vt(h.outShape,r.dtype),T=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,A=C.values;for(let P=0;P<h.batchSize;++P){let R=P*c[0],F=P*C.strides[0];for(let $=0;$<h.outHeight;++$){let z=F+$*C.strides[1],W=$*h.strideHeight-x;for(let q=0;q<f;++q){let K=W+q*g;if(K<0||K>=h.inHeight)continue;let Y=q*p[0],Z=R+K*c[1];for(let te=0;te<h.outWidth;++te){let ee=z+te*C.strides[2],se=te*h.strideWidth-v;for(let ne=0;ne<m;++ne){let oe=se+ne*b;if(oe<0||oe>=h.inWidth)continue;let re=Y+ne*p[1],le=Z+oe*h.inChannels,me=ee,we=re;for(let Se=0;Se<h.inChannels;++Se){let Ee=T[le+Se];for(let Pe=0;Pe<k;++Pe)A[me+Pe]+=Ee*E[we+Pe];me+=k,we+=k}}}}}}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var OH={kernelName:Aa,backendName:\"cpu\",kernelFunc:NC};function PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],\"depthwiseConv2dNativeBackpropFilter\");let p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,\"float32\"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),C=n.data.get(a.dataId).values,T=new Vt(a.shape,a.dtype,C);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*T.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var zH={kernelName:pg,backendName:\"cpu\",kernelFunc:PH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;ve([r,a],\"depthwiseConv2DNativeBackpropInput\");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,\"float32\"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,C]=p,T=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let we=me-se,Se=Math.max(0,Math.ceil(we/te)),Ee=Math.min(Y,(F+we)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-we;for(let rt=Je;rt<Ye;++rt){let Zt=rt*ee-Xe,Nt=x*re+k*Ce+C*rt,In=E*(F-1-ut)+A*($-1-Zt)+P*le;for(let Et=0;Et<oe;++Et){let Jt=le*oe+Et,Sn=v[Nt+Jt],Cn=T[In+Et];tt+=Sn*Cn}}}m[g*re+b*me+y*Pe+le]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var LH={kernelName:hg,backendName:\"cpu\",kernelFunc:MH};function BH(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=De([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var VH={kernelName:fg,backendName:\"cpu\",kernelFunc:BH},WH={kernelName:Xd,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:C,filterWidth:T,dilationHeight:E,dilationWidth:A,outShape:P}=N.computeDilation2DInfo(s.shape,r.shape,a,i,\"NHWC\",o),R=w.sizeFromShape(P),F=P.length,$=w.getArrayFromDType(s.dtype,R);for(let W=0;W<h;++W)for(let q=0;q<b;++q){let K=q*x-v.top;for(let Y=0;Y<y;++Y){let Z=Y*k-v.left;for(let te=0;te<g;++te){let ee=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<C;++ne){let oe=K+ne*E;if(oe>=0&&oe<f)for(let re=0;re<T;++re){let le=Z+re*A;if(le>=0&&le<m){let me=w.locToIndex([W,oe,le,te],c,w.computeStrides(s.shape)),we=w.locToIndex([ne,re,te],d,w.computeStrides(r.shape)),Se=l[me]+p[we];Se>ee&&(ee=Se)}}}let se=w.locToIndex([W,q,Y,te],F,w.computeStrides(P));$[se]=ee}}}return{dataId:u.write(w.toTypedArray($,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},UH={kernelName:Xf,backendName:\"cpu\",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:C,dilationHeight:T,dilationWidth:E,outShape:A}=N.computeDilation2DInfo(s.shape,r.shape,i,o,\"NHWC\",u);w.assert(a.rank===A.length,()=>`Error in ${Xf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);let P=w.toNestedArray(A,l.data.get(a.dataId).values),R=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let $=0;$<d;++$)for(let z=0;z<g;++z){let W=z*v-y.top;for(let q=0;q<b;++q){let K=q*x-y.left;for(let Y=0;Y<m;++Y){let 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isnan_custom(value)\n    `,u=\"\",l=`\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `):(e=\"\",t=\"attribute\",n=\"varying\",s=\"varying\",r=\"texture2D\",a=\"gl_FragColor\",i=\"\",o=`\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `,u=`\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `,l=`\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function gi(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join(\"\")}function Hp(e,t,n=\"index\"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join(\"\")}function $5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function _5(e,t,n=\"index\"){let s=e.map((a,i)=>i),r=$5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join(\"\")}function cv(e){let t=w.computeStrides(e).map(n=>n.toString());return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;\n  }\n`}function dv(){return`\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`}var r1=`\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`,{getBroadcastDims:a1}=N;function A5(e,t,n){let s=[];if(e.forEach(h=>{let f=w.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:\"\"};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=pv(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push(\"uniform int outShape;\");break;case 2:s.push(\"uniform ivec2 outShape;\"),s.push(\"uniform int outShapeStrides;\");break;case 3:s.push(\"uniform ivec3 outShape;\"),s.push(\"uniform ivec2 outShapeStrides;\");break;case 4:s.push(\"uniform ivec4 outShape;\"),s.push(\"uniform ivec3 outShapeStrides;\");break;default:break}s.push(\"uniform ivec2 outTexShape;\")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:\"\"};`)});let r=s.join(`\n`),a=e.map(h=>E5(h,t,n.packedInputs,n.enableShapeUniforms)).join(`\n`),i=t.texShape,o=hn(),u=F5(o),l,c,p=z5(o);return t.isPacked?(l=R5(t.logicalShape,i,n.enableShapeUniforms),c=P5(o)):(l=D5(t.logicalShape,i,n.enableShapeUniforms),c=O5(o)),n.packedInputs&&(p+=V5),[p,u,c,r,l,a,n.userCode].join(`\n`)}function nu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return J5(e,t);case 1:return tK(e,t);case 2:return sK(e,t);case 3:return aK(e,t);case 4:return oK(e,t);case 5:return uK(e);case 6:return lK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function i1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Z5(e);case 1:return eK(e,t);case 2:return nK(e,t);case 3:return rK(e,t);default:return iK(e,t)}}function E5(e,t,n=!1,s){let r=\"\";n?r+=i1(e,s):r+=nu(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=cK(e,t):r+=dK(e,t)),r}function R5(e,t,n){switch(e.length){case 0:return o1();case 1:return W5(e,t,n);case 2:return Y5(e,t,n);case 3:return G5(e,t,n);default:return q5(e,t,n)}}function D5(e,t,n){switch(e.length){case 0:return o1();case 1:return U5(e,t,n);case 2:return Q5(e,t,n);case 3:return H5(e,t,n);case 4:return j5(e,t,n);case 5:return K5(e,t);case 6:return X5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function F5(e){return`\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${e.texture2D}(textureSampler, uv).r;\n    }\n  `}function O5(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}function P5(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}function z5(e){return`${e.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${e.varyingFs} vec2 resultUV;\n    ${e.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${e.defineSpecialNaN}\n    ${e.defineSpecialInf}\n    ${e.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${M5}\n    ${L5}\n    ${B5}\n  `}var M5=`\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,L5=`\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,B5=`\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`,V5=`\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;function o1(){return`\n    int getOutputCoords() {\n      return 0;\n    }\n  `}function W5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${s[1]}.0);\n      }\n    `:s[1]===1?n?`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `:`\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${s[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);\n    }\n  `}function U5(e,t,n){return t[0]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `:t[1]===1?n?`\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `:`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `:n?`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `:`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}function G5(e,t,n){if(n)return`\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}function H5(e,t,n){if(n)return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${Hp([\"r\",\"c\",\"d\"],e)}\n    return ivec3(r, c, d);\n  }\n`;let s=gi([\"r\",\"c\",\"d\"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec3(r, c, d);\n    }\n  `}function q5(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),i=a,o=\"\",u=\"b, r, c\";for(let l=2;l<e.length-1;l++)i*=e[e.length-l-1],o=`\n      int b${l} = index / ${i};\n      index -= b${l} * ${i};\n    `+o,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n\n      ${o}\n\n      int b = index / ${a};\n      index -= b * ${a};\n\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}function j5(e,t,n){if(n)return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${Hp([\"r\",\"c\",\"d\",\"d2\"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `;let s=gi([\"r\",\"c\",\"d\",\"d2\"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${s}\n      return ivec4(r, c, d, d2);\n    }\n  `}function K5(e,t){let n=gi([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}function X5(e,t){let n=gi([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}function Y5(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(w.arraysEqual(e,t))return n?`\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));\n      }\n    `;let r=Math.ceil(e[1]/2);return n?`\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${s[0]}, ${s[1]}));\n\n      int index = resTexRC.x * ${s[1]} + resTexRC.y;\n      int r = 2 * (index / ${r});\n      int c = imod(index, ${r}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}function Q5(e,t,n){return w.arraysEqual(e,t)?n?`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `:e[1]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `:e[0]===1?n?`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `:n?`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `:`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}function bi(e){return`offset${e}`}function Z5(e){let t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),s=hn();return`\n    vec4 ${n}() {\n      return ${s.texture2D}(${t}, halfCR);\n    }\n  `}function J5(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`\n      float ${s}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let i=bi(n);if(t)return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;let[o,u]=e.shapeInfo.texShape;return`\n    float ${s}() {\n      vec2 uv = uvFromFlat(${o}, ${u}, ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}function eK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=hn();if(t)return`\n    vec4 ${s}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `;let i=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`\n    vec4 ${s}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${i[0]}, ${i[1]}, index);\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}function tK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${s}(int index) {\n        ${su(e)}\n      }\n    `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`\n      float ${s}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;let o=bi(n);return i===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${o}) + 0.5) / ${a}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `:a===1?t?`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${s}(int index) {\n        vec2 uv = vec2((float(index + ${o}) + 0.5) / ${i}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:t?`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `:`\n    float ${s}(int index) {\n      vec2 uv = uvFromFlat(${a}, ${i}, index + ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}function nK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=a[0],o=a[1],u=hn();if(a!=null&&w.arraysEqual(n,a))return t?`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `:`\n      vec4 ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${o}.0, ${i}.0);\n\n        return ${u.texture2D}(${s}, uv);\n      }\n    `;if(t)return`\n    vec4 ${r}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `;let l=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${r}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${s}, uv);\n    }\n  `}function sK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&w.arraysEqual(n,a)){if(t)return`\n      float ${r}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `;let d=a[0],h=a[1];return`\n    float ${r}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ru(e,u),h=[\"row\",\"col\"];return`\n      ${nu(d,t)}\n      float ${r}(int row, int col) {\n        return ${r}(${au(h,o)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${su(e)}\n      }\n    `;let l=a[0],c=a[1],p=bi(s);return c===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `:l===1?t?`\n      float ${r}(int row, int col) {\n        float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col) {\n      float index = dot(vec3(row, col, ${p}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${s}, uv);\n    }\n  `:t?`\n      float ${r}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s}Shape[1] + col + ${p};\n        vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n  float ${r}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${p};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${s}, uv);\n  }\n`}function rK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ru(e,d),m=[\"b\",\"row\",\"col\"];return`\n        ${i1(f,t)}\n        vec4 ${r}(int b, int row, int col) {\n          return ${r}(${au(m,h)});\n        }\n      `}let o=hn();if(t)return`\n    vec4 ${r}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `;let u=i[0],l=i[1],c=Math.ceil(n[2]/2),p=c*Math.ceil(n[1]/2);return`\n    vec4 ${r}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${p}, ${c}, b, row, col);\n      return ${o.texture2D}(${s}, uv);\n    }\n  `}function aK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ru(e,l),g=[\"row\",\"col\",\"depth\"];return`\n        ${nu(m,t)}\n        float ${r}(int row, int col, int depth) {\n          return ${r}(${au(g,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${a}, ${i}, 1)));\n        ${su(e)}\n      }\n    `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        int stride1 = ${s}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n        float ${r}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${i}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${d}.0, ${p}.0);\n          return sampleTexture(${s}, uv);\n        }\n      `;if(d===i&&h==null)return t?`\n      float ${r}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n    float ${r}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);\n      return sampleTexture(${s}, uv);\n    }\n  `;let f=bi(s);return t?`\n    float ${r}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${s}Shape[1] * ${s}Shape[2];\n      int stride1 = ${s}Shape[2];\n      int index = row * ${a} + col * ${i} + depth + ${f};\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);\n      return sampleTexture(${s}, uv);\n    }\n    `:`\n      float ${r}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${a} + col * ${i} + depth + ${f};\n        vec2 uv = uvFromFlat(${p}, ${d}, index);\n        return sampleTexture(${s}, uv);\n      }\n  `}function iK(e,t){let n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=hn();if(t)return`\n    vec4 ${s}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);\n    }\n  `;let a=e.shapeInfo.logicalShape,i=a.length,o=e.shapeInfo.texShape,u=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],l=u[0],c=u[1],p=Math.ceil(a[i-1]/2),d=p*Math.ceil(a[i-2]/2),h=\"int b, int row, int col\",f=`b * ${d} + (row / 2) * ${p} + (col / 2)`;for(let m=2;m<i-1;m++)h=`int b${m}, `+h,d*=a[i-m-1],f=`b${m} * ${d} + `+f;return`\n    vec4 ${s}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${r.texture2D}(${n}, uv);\n    }\n  `}function oK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r=\"get\"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ru(e,u),v=[\"row\",\"col\",\"depth\",\"depth2\"];return`\n      ${nu(y,t)}\n      float ${r}(int row, int col, int depth, int depth2) {\n        return ${r}(${au(v,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${o}, ${i}, ${a}, 1)));\n        ${su(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${i}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;if(h===a&&c==null)return t?`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${s}TexShape[1], ${s}TexShape[0]);\n        return sampleTexture(${s}, uv);\n      }\n    `:`\n      float ${r}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${s}, uv);\n      }\n    `;let b=bi(s);return t?`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `:`\n    float ${r}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} +\n          depth * ${a} + depth2;\n      vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});\n      return sampleTexture(${s}, uv);\n    }\n  `}function uK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ru(e,u),g=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"];return`\n      ${nu(m)}\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        return ${s}(${au(g,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${o}, ${i}, ${a}, ${r})) +\n          depth3;\n        ${su(e)}\n      }\n    `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${i}, ${a}, ${r}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===r&&c==null)return`\n      float ${s}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${d}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let f=bi(n);return`\n    float ${s}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${o} + col * ${i} + depth * ${a} +\n          depth2 * ${r} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${d}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function lK(e){let t=e.shapeInfo.logicalShape,n=e.name,s=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ru(e,r),b=[\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"];return`\n      ${nu(g)}\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${s}(${au(b,a)});\n      }\n    `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${o})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${i}, 1)));\n        ${su(e)}\n      }\n    `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${o}, ${i})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===i&&p==null)return`\n      float ${s}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;let m=bi(n);return`\n    float ${s}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${o} + depth3 * ${i} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}function su(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function cK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=a1(e.shapeInfo.logicalShape,t.logicalShape),u=ot(i),l=i-a,c,p=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];a===0?c=\"\":i<2&&o.length>=1?c=\"coords = 0;\":c=o.map(y=>`coords.${p[y+l]} = 0;`).join(`\n`);let d=\"\";i<2&&a>0?d=\"coords\":d=e.shapeInfo.logicalShape.map((y,v)=>`coords.${p[v+l]}`).join(\", \");let h=\"return outputValue;\",m=w.sizeFromShape(e.shapeInfo.logicalShape)===1,b=w.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!b)h=`\n      return vec4(outputValue.xy, outputValue.xy);\n    `;else if(m&&!b)i===1?h=`\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `:h=`\n        return vec4(outputValue.x);\n      `;else if(o.length){let y=a-2,v=a-1;o.indexOf(y)>-1&&o.indexOf(v)>-1?h=\"return vec4(outputValue.x);\":o.indexOf(y)>-1?h=\"return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);\":o.indexOf(v)>-1&&(h=\"return vec4(outputValue.xx, outputValue.zz);\")}return`\n    vec4 ${r}() {\n      ${u} coords = getOutputCoords();\n      ${c}\n      vec4 outputValue = get${s}(${d});\n      ${h}\n    }\n  `}function dK(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r=\"get\"+s+\"AtOutCoords\",a=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&w.arraysEqual(i,a))return`\n      float ${r}() {\n        return sampleTexture(${n}, resultUV);\n      }\n    `;let l=ot(u),c=a1(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,d,h=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];o===0?d=\"\":u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`\n`);let f=\"\";return u<2&&o>0?f=\"coords\":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(\", \"),`\n    float ${r}() {\n      ${l} coords = getOutputCoords();\n      ${d}\n      return get${s}(${f});\n    }\n  `}function ot(e){if(e<=1)return\"int\";if(e===2)return\"ivec2\";if(e===3)return\"ivec3\";if(e===4)return\"ivec4\";if(e===5)return\"ivec5\";if(e===6)return\"ivec6\";throw Error(`GPU for rank ${e} is not yet supported`)}function pv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ru(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function au(e,t){return t.map(n=>e[n]).join(\", \")}function pK(e,t,n,s){let r=n.map((x,k)=>{let C={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(C.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:C}}),a=r.map(x=>x.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=A5(r,i,t),u=zC(e.gl,o),l=e.createProgram(u),c=null,p=e.getUniformLocation(l,\"NAN\",!1);X().getNumber(\"WEBGL_VERSION\")===1&&(c=e.getUniformLocation(l,\"INFINITY\",!1));let d=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,d),h[`offset${k}`]=e.getUniformLocation(l,`offset${k}`,d),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(l,`${k}Shape`,d),m[`${k}TexShape`]=e.getUniformLocation(l,`${k}TexShape`,d))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,\"outShape\",d),y=e.getUniformLocation(l,\"outShapeStrides\",d),b=e.getUniformLocation(l,\"outTexShape\",d));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(l,x.name,d)}),{program:t,fragmentShader:u,source:o,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function tw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function hK(e,t,n,s,r){t.program.enableShapeUniforms||(tw(t.inShapeInfos,n),tw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber(\"WEBGL_VERSION\")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=pv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type===\"float\")e.gl.uniform1fv(c,p);else if(u.type===\"vec2\")e.gl.uniform2fv(c,p);else if(u.type===\"vec3\")e.gl.uniform3fv(c,p);else if(u.type===\"vec4\")e.gl.uniform4fv(c,p);else if(u.type===\"int\")e.gl.uniform1iv(c,p);else if(u.type===\"ivec2\")e.gl.uniform2iv(c,p);else if(u.type===\"ivec3\")e.gl.uniform3iv(c,p);else if(u.type===\"ivec4\")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function fK(e,t,n){let s=\"\";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=pv(e.packedInputs,i.shape,u),d=\"\",h=\"\",f=\"\";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?\"\":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:\"\"}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?\"uniform\":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+=\"_\"+s+\"_\"+r+`${X().getNumber(\"WEBGL_VERSION\")}`,a}function kn(e){return X().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&e<=4}var mK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Hp([\"r\",\"c\",\"d\"],e):gi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}},gK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Hp([\"r\",\"c\",\"d\"],e):gi([\"r\",\"c\",\"d\"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}},bK=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=3;let t=hn();this.outputShape=e,this.userCode=`\n      ${r1}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}},yK=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=3;let t=hn();this.outputShape=e,this.userCode=`\n      ${r1}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}},vK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\"),this.userCode=`\n      ${this.enableShapeUniforms?dv():cv(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${n.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${n.output} = vec4(${s}, 0., 0., 0.);\n      }\n    `}},xK=class{constructor(e,t=!1){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s=\"\",r=\"result\";t&&(r=\"floor(result * 255. + 0.5)\");for(let a=0;a<=1;a++)for(let i=0;i<=1;i++){let o=a*2+i;s+=`\n          localCoords = coords;\n          if(localCoords[2] + ${i} < ${this.enableShapeUniforms?\"outShape[2]\":`${e[2]}`}) {\n          localCoords[2] += ${i};\n          if (localCoords[1] + ${a} < ${this.enableShapeUniforms?\"outShape[1]\":`${e[1]}`}) {\n            localCoords[1] += ${a};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${o}] = values[0];\n            } else if (offset == 1) {\n              result[${o}] = values[1];\n            } else if (offset == 2) {\n              result[${o}] = values[2];\n            } else {\n              result[${o}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?dv():cv(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${s}\n\n          ${n.output} = ${r};\n        }\n    `}},wK={};Ae(wK,{bindVertexProgramAttributeStreams:()=>g1,createBufferFromOutputTexture:()=>v1,createFloat16MatrixTexture:()=>p1,createFloat16PackedMatrixTexture:()=>m1,createFloat32MatrixTexture:()=>d1,createIndexBuffer:()=>c1,createPackedMatrixTexture:()=>f1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>l1,createVertexShader:()=>u1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>w1,downloadFloat32MatrixFromBuffer:()=>x1,downloadMatrixFromPackedOutputTexture:()=>I1,downloadPackedMatrixFromBuffer:()=>k1,getInternalFormatForFloat16MatrixTexture:()=>fv,getInternalFormatForFloat16PackedMatrixTexture:()=>bv,getInternalFormatForFloat32MatrixTexture:()=>hv,getInternalFormatForPackedMatrixTexture:()=>gv,getInternalFormatForUnsignedBytesMatrixTexture:()=>mv,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>y1});function u1(e){let t=hn(),n=`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    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This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  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fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),LC(t,n),this.debug&&sd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=g1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&sd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?qC(this.gl,e,t):jC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),KC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=eu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&sd(this.gl,this.program),Fu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Du(this.gl,X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await 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e=kK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),rd(this.gl,e,this.framebuffer),this.debug&&Fu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Fu(this.gl)):Lm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;rd(s,e,this.framebuffer),this.debug&&Fu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function kK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:IK,bincountImpl:S1,bincountReduceImpl:SK,ceilImpl:CK,concatImpl:NK,equalImpl:TK,expImpl:$K,expm1Impl:_K,floorImpl:AK,gatherNdImpl:EK,gatherV2Impl:RK,greaterImpl:DK,greaterEqualImpl:FK,lessImpl:OK,lessEqualImpl:PK,linSpaceImpl:zK,logImpl:MK,maxImpl:LK,maximumImpl:BK,minimumImpl:VK,multiplyImpl:WK,negImpl:UK,notEqualImpl:GK,prodImpl:HK,rangeImpl:qK,rsqrtImpl:jK,sigmoidImpl:KK,simpleAbsImpl:C1,sliceImpl:XK,sparseFillEmptyRowsImpl:YK,sparseReshapeImpl:QK,sparseSegmentReductionImpl:N1,sqrtImpl:ZK,stridedSliceImpl:JK,stringNGramsImpl:eX,stringSplitImpl:tX,stringToHashBucketFastImpl:nX,subImpl:sX,tileImpl:rX,topKImpl:aX,transposeImpl:yv,uniqueImpl:iX}=Yy;function T1(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:T1(e,t)}function oX(e,t){if(e===1)return\"rc\";let n=\"\";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=\",\");return n}var uX=class{constructor(e){if(this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=kn(this.outputShape.length),this.rank===0)this.userCode=`\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;else{let t=un(\"rc\",this.rank),n=ot(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`\n        void main() {\n          ${n} rc = getOutputCoords();\n\n          if(${s}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?\"r\":\"rp1\"}, ${s===0?\"c\":\"cp1\"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+=\"||\");return t}getSetup(e){if(this.rank===1)return\"\";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${s};\n    `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}},$1=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let n=\"\";for(let s=0;s<4;s++){let r=\"thisRC = rc;\";s%2===1&&(r+=\"thisRC.z += 1;\"),s>1&&(r+=\"thisRC.y += 1;\"),n+=`\n        ${r}\n        ${s>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?\"}\":\"\"}\n      `}this.userCode=`\n      ${lX(t,this.enableShapeUniforms)}\n      ${this.enableShapeUniforms?dv():cv(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n        int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}};function lX(e,t){return`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${t?_5([\"r\",\"c\",\"d\"],\"inputShape\"):gi([\"r\",\"c\",\"d\"],e)}\n      return ivec3(r, c, d);\n    }\n  `}var cX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=sw(t,n),r=rw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=nw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=sw(n,s),a=rw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=nw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function dX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nw(e,t,n,s,r){let a=pX(t,s),i;if(r){let[u,l]=eu(e[0],e[1]);i=u*l}else{let[u,l]=Xl(e[0],e[1]);i=u*l}let o=dX(n,a);return i*o}function pX(e,t){switch(e){case 3:return gv(t);case 4:return bv(t);case 1:return hv(t);case 0:return fv(t);case 2:return mv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function hX(e){return X().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?e?3:1:e?4:0}function sw(e,t){if(e===1)return 3;if(e===0||e==null)return hX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function rw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},ts=\"if (isnan(x)) return x;\",fX=\"return x;\",aw=\"return abs(x);\",mX=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",gX=ts+`\n  return (x < 0.0) ? 0.0 : x;\n`,bX=ts+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Pi=\"return x;\",yX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",vX=\"return x;\",xX=`\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`,wX=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,kX=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,IX=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Yr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}},SX=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let t=e.length,n=un(\"rc\",t),s=ot(t),r=oX(t,n),a=n.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n      void main() {\n        ${s} rc = getOutputCoords();\n        vec4 packedInput = getA(${r});\n\n        setOutput(getChannel(packedInput, ${i}));\n      }\n    `}},CX=xs.whereImpl,NX=1e-7,TX=1e-4,Qc={};function $X(e){return e in Qc||(Qc[e]={}),Qc[e]}var _X=X().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),AX=600;function EX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*AX/1024/1024}var _1=class extends tl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof qf)t=e;else{let n=vs(X().getNumber(\"WEBGL_VERSION\"),e);t=new qf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber(\"WEBGL_VERSION\"));t=new qf(n),this.binaryCache=$X(X().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new cX(this.gpgpu),this.numMBBeforeWarning=EX(),this.texData=new Wd(this,Ss())}nextDataId(){return _1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||X().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Yr(i,Pi):p=new Hs(i,Pi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s===\"string\")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s===\"complex64\"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=N.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Yr(s,Pi):h=new Hs(s,Pi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool(\"DEBUG\")&&!X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&X().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,l;if(a!==\"complex64\"&&X().get(\"WEBGL_BUFFER_SUPPORTED\")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Xc(s))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let c;if(a===\"complex64\"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=N.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;o?d=new Yr(r,Pi):d=new Hs(r,Pi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!FC(n))throw X().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Xc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool(\"WEBGL_PACK\")&&s===!0,i=a?ad(t):t,o=a?new yK(i):new bK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],\"float32\"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=_X){return X().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");let t=e.dataSync();return CX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Yr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!==\"complex64\"){let s=C1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\"))return this.packedUnaryOp(e,aw,e.dtype);let t=new Hs(e.shape,aw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new SX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new uX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ma(e.shape),...ga(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ma(t),...ga(t)],a=new $1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=ad(r),o;s?o=new gK(i):o=new mK(i);let u=!0,l=[t!=null?t:Xc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Xc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Ju(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=fK(e,l,c),d=this.getAndSaveBinary(p,()=>pK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),hK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get(\"WEBGL_FLUSH_THRESHOLD\");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool(\"WEBGL_LAZILY_UNPACK\")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=X().getBool(\"DEBUG\");X().set(\"DEBUG\",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(X().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?NX:TX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=QC(n,o),t.texShape=c),r!=null){let p=ad(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=eu(c[0],c[1])),o?d=new xK(p,m):d=new vK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),C=this.texData.get(k.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=RX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},A1=_1;A1.nextDataId=0;function RX(e,t){if(t===\"float32\"||t===\"complex64\")return e;if(t===\"int32\"||t===\"bool\"){let n=t===\"int32\"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var xpe=\"0.0.0\";function DX(){X().set(\"WEBGL_FORCE_F16_TEXTURES\",!0)}cp.isBrowser()&&dp(\"webgl\",()=>new A1,2);var wpe={forceHalfFloat:DX},E1=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,no=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}},qp=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`,Ql=class{constructor(e,t,n,s=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=kn(r);let a=\"\";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;else if(a=`\n          ${ot(r)} coords = getOutputCoords();\n        `,r===1)this.enableShapeUniforms?a+=`\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `:a+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{let o=un(\"coords\",r);this.enableShapeUniforms?a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= outShape[${r} - 2];\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= outShape[${r} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:a+=`\n            bool nextRowOutOfBounds =\n              (${o[r-2]} + 1) >= ${this.outputShape[r-2]};\n            bool nextColOutOfBounds =\n              (${o[r-1]} + 1) >= ${this.outputShape[r-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${a}\n\n        setOutput(result);\n      }\n    `}};function En(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var FX={kernelName:Ma,backendName:\"webgl\",kernelFunc:En};function Er(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.texData.get(a.dataId),o=En({inputs:{x:s},backend:n}),u=En({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var OX={kernelName:qd,backendName:\"webgl\",kernelFunc:Er},R1=\"return (a < 0.) ? b * a : a;\",D1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function PX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],\"float32\",w.createScalarValue(a,\"float32\")),o=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Ql(D1,r.shape,i.shape):new no(R1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],\"float32\");return n.disposeIntermediateTensorInfo(i),u}var zX={kernelName:La,backendName:\"webgl\",kernelFunc:PX},F1=\"return (a < 0.) ? b * a : a;\",O1=`\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function MX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Ql(O1,s.shape,r.shape):new no(F1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],\"float32\")}var LX={kernelName:Qa,backendName:\"webgl\",kernelFunc:MX},iu=\"if (isnan(x)) return x;\",BX=`\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`,VX=`\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&t!=null,c;return l?c=new Yr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype===\"complex64\"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,C={dataId:x.dataId,dtype:x.dtype,shape:u.shape},T={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new no(e,u.shape,l.shape);return c.runWebGLProgram(E,[C,T],yn(x.dtype,k.dtype))}),y=Er({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||yn(u.dtype,l.dtype);if((u.dtype===\"string\"||l.dtype===\"string\"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype===\"string\"?N.fromUint8ToStringArray(f):f,b=u.dtype===\"string\"?N.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&t!=null,h;return d?h=new Ql(t,u.shape,l.shape,n):h=new no(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function jp(e,t=!1){if(e===\"linear\")return t?vX:fX;if(e===\"relu\")return t?wX:gX;if(e===\"elu\")return t?xX:mX;if(e===\"relu6\")return t?kX:bX;if(e===\"prelu\")return t?O1:F1;if(e===\"leakyrelu\")return t?D1:R1;if(e===\"sigmoid\")return t?IX:yX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=kn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?\"i * 2, rc.y\":\"rc.y, i * 2\",d=r?\"rc.z, i * 2\":\"i * 2, rc.z\",h=s?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],f=r?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],m=\"\",g=\"\";i&&(o?m=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${i}\n        }`:u?m=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${i}\n        }`:m=`vec4 activation(vec4 x) {\n          ${i}\n        }`,g=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let y=\"rc.x\",v=\"rc.x\";e[0]<t[0]?y=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(v=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${c}; i++) {\n          int batchA = ${y};\n          int batchB = ${v};\n          vec4 a = getMatrixA(batchA, ${p});\n          vec4 b = getMatrixB(batchB, ${d});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${b}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}},iw={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},ow=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}},uw=\"return a * b;\";function vv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype===\"complex64\"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new ow(iw.REAL,s.shape,r.shape),c=new ow(iw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Er({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=WK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?i=new Ql(uw,s.shape,r.shape):i=new no(uw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var WX={kernelName:Ka,backendName:\"webgl\",kernelFunc:vv};function UX(e,t,n){let s=[ma(e.shape),...ga(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ma(t),...ga(t)],i=new $1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!Ju(r.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?UX(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var GX={kernelName:Ao,backendName:\"webgl\",kernelFunc:he},lw=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l=\"\";r%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${o===1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${o===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${o===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}},HX=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=\"0.0\",o=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",o=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",o=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let l=Math.floor(n/4)*4,c=n%4,p=`\n      if (${t===\"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${t===\"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${o}(values, minMaxValue);\n        if (${t===\"min\"} || ${t===\"max\"}) {\n          minMaxValue = ${o}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,d=\"vec4\";t===\"all\"?(i=\"1.0\",p=`\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",p=`\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `,d=\"bvec4\");let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${i};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${i});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          ${d} values = ${d}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function qX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yi(e,t,n,s){let r=qX(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n===\"mean\"?c=i===0?new lw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new lw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new HX({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var jX=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=KX(t);this.userCode=`\n    void main() {\n      ${s} resRC = getOutputCoords();\n      setOutput(getA(${r}));\n    }\n    `}};function KX(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var XX=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=ot(this.rank),r=T1(\"rc\",this.rank),a=new Array(this.rank);for(let l=0;l<t.length;l++)a[t[l]]=r[l];let i=`vec2(${a.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`\n    void main() {\n      ${s} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${o}) {\n        result[1] = ${u};\n      }\n      --${r[this.rank-1]};\n      if(++${r[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${o}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}};function Kp(e,t,n){let s=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new XX(e.shape,t):new jX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function YX(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=N.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Kp(e,u,s),o=N.getInnerMostAxes(o.length,a)),N.assertAxesAreInnerMostDims(\"sum\",o,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=N.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=lp(e.dtype),v=yi(b,y,\"sum\",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Xp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return YX(r,a,i,n)}var QX={kernelName:ai,backendName:\"webgl\",kernelFunc:Xp};function cn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=yv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Kp(r,a,i);return l}var ZX={kernelName:ci,backendName:\"webgl\",kernelFunc:cn},z1=1e3;function Pd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=n?T.shape[1]:T.shape[2],F=a!=null,$=i!=null,z=u===\"leakyrelu\",W=u!=null?jp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>z1&&q===!1){let Z=T,te=E;n&&(Z=cn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=cn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=vv({inputs:{a:ne,b:re},backend:r});K=Xp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=yn(e.dtype,t.dtype),te=new P1(k,C,[P,h,f],n,s,F,W,$,z),ee=[T,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],\"float32\",w.createScalarValue(o,\"float32\"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function JX(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Pd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var e8={kernelName:na,backendName:\"webgl\",kernelFunc:JX},cw=\"return abs(x);\";function t8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!==\"complex64\"){let a=n.texData.get(s.dataId),i=C1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?r=new Yr(s.shape,cw):r=new Hs(s.shape,cw),n.runWebGLProgram(r,[s],s.dtype)}var n8={kernelName:ao,backendName:\"webgl\",kernelFunc:t8},s8=ts+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`,r8=Ke({opSnippet:s8}),a8={kernelName:nl,backendName:\"webgl\",kernelFunc:r8},i8=ts+`\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:sl,backendName:\"webgl\",kernelFunc:o8},dw=\"return a + b;\",l8=qt({opSnippet:dw,packedOpSnippet:dw,supportsComplex:!0,cpuKernelImpl:IK}),c8={kernelName:kr,backendName:\"webgl\",kernelFunc:l8},d8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(\" + \");this.userCode=`\n      void main() {\n        ${n.join(`\n        `)}\n\n        float result = ${s};\n        setOutput(result);\n      }\n    `}},p8=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(\" + \");this.userCode=`\n      void main() {\n        ${n.join(`\n        `)}\n\n        vec4 result = ${s};\n        setOutput(result);\n      }\n    `}};function ud(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return En({inputs:{x:s[0]},backend:n});if(s.length>X().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let u=Math.floor(s.length/2),l=ud({inputs:s.slice(0,u),backend:n}),c=ud({inputs:s.slice(u),backend:n});return ud({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>yn(u,l)),a=s.map(u=>u.shape),o=X().getBool(\"WEBGL_PACK\")?new p8(s[0].shape,a):new d8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var h8={kernelName:xa,backendName:\"webgl\",kernelFunc:ud};function f8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims(\"all\",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,\"all\",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var m8={kernelName:rl,backendName:\"webgl\",kernelFunc:f8};function g8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims(\"any\",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,\"any\",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var b8={kernelName:al,backendName:\"webgl\",kernelFunc:g8},y8=class{constructor(e,t,n){this.variableNames=[\"A\"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[r,a];let i=t===\"max\"?\">\":\"<\",o=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${s};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${s}; i++) {\n          int inIdx = ${o};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${i} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}},v8=class{constructor(e,t,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,w.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,o=i.length,u=ot(o),l=un(\"coords\",o),c,p;if(a===1){p=o+1;let T=ot(p);c=`\n        ${T} sourceLocR = ${T}(${l.join()}, 0);\n        ++${l[o-1]};\n        ${T} sourceLocG = ${T}(${l.join()}, 0);\n        ++${l[o-2]};\n        ${T} sourceLocA = ${T}(${l.join()}, 0);\n        --${l[o-1]};\n        ${T} sourceLocB = ${T}(${l.join()}, 0);\n        --${l[o-2]};`}else p=o,c=`\n        ${u} sourceLocR = coords;\n        ++${l[o-1]};\n        ${u} sourceLocG = coords;\n        ++${l[o-2]};\n        ${u} sourceLocA = coords;\n        --${l[o-1]};\n        ${u} sourceLocB = coords;\n        --${l[o-2]};`;let d=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,p),h=\".\"+d[p-1],f=d.map(T=>\"int \"+T),m=un(\"sourceLocR\",p-1).concat(\"inIdx.r\"),g=un(\"sourceLocG\",p-1).concat(\"inIdx.g\"),b=un(\"sourceLocB\",p-1).concat(\"inIdx.b\"),y=un(\"sourceLocA\",p-1).concat(\"inIdx.a\"),v=n===\"max\"?\"greaterThan\":\"lessThan\",x=s?\"\":`\n          inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),\n                             getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${y.join()})));`,k=`vec4(\n            getAChannel(${m.join()}),\n            hasNextCol ? getAChannel(${g.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${y.join()}) : 0.)`,C=s?\"\":`\n      float getBestIndicesAChannel(${f.join()}) {\n        return getChannel(getBestIndicesA(${d.join()}),\n                                          vec2(${d.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${f.join()}) {\n        return getChannel(getA(${d.join()}),\n                               vec2(${d.slice(-2).join()}));\n      }\n      ${C}\n      void main() {\n        ${u} coords = getOutputCoords();\n        bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};\n        bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};\n        ${c}\n        ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},\n          sourceLocB${h}, sourceLocA${h}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${x}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}};function M1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=N.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new y8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,\"int32\");if(c.shape[1]===1)return c;let p=M1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function L1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=N.computeOptimalWindowSize(a),o=new v8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,\"int32\");if(l.shape.length===t.shape.length){let c=L1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function B1(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims(\"arg\"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool(\"WEBGL_PACK_REDUCE\")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=N.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=M1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return L1(e,t,s)}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=B1(n,u,i[0],\"max\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var w8={kernelName:wa,backendName:\"webgl\",kernelFunc:x8};function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=B1(n,u,i[0],\"min\");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var I8={kernelName:il,backendName:\"webgl\",kernelFunc:k8},S8=ts+`\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`,C8=Ke({opSnippet:S8}),N8={kernelName:ol,backendName:\"webgl\",kernelFunc:C8},T8=ts+\"return log(x + sqrt(x * x + 1.0));\",$8=Ke({opSnippet:T8}),_8={kernelName:ul,backendName:\"webgl\",kernelFunc:$8},A8=ts+`\n  return atan(x);\n`,E8=Ke({opSnippet:A8}),R8={kernelName:ll,backendName:\"webgl\",kernelFunc:E8},D8=BX+`\n  return atan(a, b);\n`,F8=`\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  `+VX+`\n  return result;\n`,O8=qt({opSnippet:D8,packedOpSnippet:F8}),P8={kernelName:dl,backendName:\"webgl\",kernelFunc:O8},z8=ts+`\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelName:cl,backendName:\"webgl\",kernelFunc:M8},el=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t===\"avg\",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(f||(b=\"-1.0 / 1e-20\"),n){let T=\">=\";this.userCode=`\n        const ivec2 strides = ivec2(${i}, ${o});\n        const ivec2 pads = ivec2(${d}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${T} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${s?r?m:g:`wR * ${p} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let y=\"max\",v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(v=\"avgValue / count\");let x=Math.floor(a/4)*4,k=a%4,C=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${y}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${i}, ${o});\n      const ivec2 pads = ivec2(${d}, ${h});\n      const float initializationValue = ${b};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${b});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${C}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${k===1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${C}\n          } else if (${k===2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${C}\n          } else if (${k===3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${C}\n          }\n        }\n        setOutput(${v});\n      }\n    `}},xv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&n)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t===\"avg\",v=\"0.0\";if(y||(v=\"-1.0 / 1e-20\"),n){let A=\">=\";this.userCode=`\n        const ivec3 strides =\n            ivec3(${i}, ${o}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${b});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${d};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${p}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${A} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;return}let x=\"max\",k=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(k=\"avgValue / count\");let C=Math.floor(a/4)*4,T=a%4,E=`\n      if (${y}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${x}(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${i}, ${o}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${b});\n      const float initializationValue = ${v};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${v});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${d};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${C}; wC += 4) {\n              int xC = xCCorner + wC * ${p};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${p}, ch)\n              );\n\n              ${E}\n            }\n\n            int xC = xCCorner + ${C};\n            if (${T===1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${T===2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${E}\n            } else if (${T===3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${p}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${p}, ch),\n                initializationValue\n              );\n\n              ${E}\n            }\n          }\n          setOutput(${k});\n        }\n      }\n    `}};function B8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,\"avgPool\");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new el(c,\"avg\",!1);return n.runWebGLProgram(p,[r],\"float32\")}var V8={kernelName:ka,backendName:\"webgl\",kernelFunc:B8};function W8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new xv(p,\"avg\",!1);return n.runWebGLProgram(d,[r],\"float32\")}var U8={kernelName:Hd,backendName:\"webgl\",kernelFunc:W8},G8=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${p});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${o};\n            wR += ${a}) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${i}) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},H8=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${o}) {\n          float dyD = float(dyDCorner + wD) / ${r}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${p};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function q8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new H8(d);return n.runWebGLProgram(h,[r],i.dtype)}var j8={kernelName:ag,backendName:\"webgl\",kernelFunc:q8};function K8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;tu([r,a],\"avgPoolGrad\");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=new G8(c);return n.runWebGLProgram(p,[r],i.dtype)}var X8={kernelName:rg,backendName:\"webgl\",kernelFunc:K8};function Y8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return 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So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `}};function hw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function NY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new CY(s.shape),i=[hw(s,r.complexTensorInfos.real),hw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var TY={kernelName:jd,backendName:\"webgl\",kernelFunc:NY},$Y=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join(`\n        `)}\n      }\n    `}},_Y=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=ot(s),a=un(\"coords\",s),i=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let f=1;f<o.length;f++)o[f]=o[f-1]+e[f][t];let u=i[t],l=i.slice(-2),c=i.join(),p=`if (${u} < ${o[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`\n        if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {\n          return getChannel(\n            getT${f}(${Zc(i,u,m)}),\n            vec2(${Zc(l,u,m)}));\n        }`}let d=o.length,h=o[o.length-1];p+=`\n        return getChannel(\n          getT${d}(${Zc(i,u,h)}),\n          vec2(${Zc(l,u,h)}));`,this.userCode=`\n      float getValue(${i.map(f=>\"int \"+f)}) {\n        ${p}\n      }\n\n      void main() {\n        ${r} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${a}), 0., 0., 0.);\n\n        ${a[s-1]} = ${a[s-1]} + 1;\n        if (${a[s-1]} < ${n[s-1]}) {\n          result.g = getValue(${a});\n        }\n\n        ${a[s-2]} = ${a[s-2]} + 1;\n        if (${a[s-2]} < ${n[s-2]}) {\n          result.a = getValue(${a});\n        }\n\n        ${a[s-1]} = ${a[s-1]} - 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pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${p}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${d}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${f===1}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${f===2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${f===3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${k}\n        ${x}\n        setOutput(result);\n      }\n    `}},DY=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${r}, ${a}, ${i});\n      const ivec3 pads = ivec3(${t}, ${n}, ${s});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${o};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${p}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${f===1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${f===2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${f===3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},FY=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"},{name:\"pad\",type:\"ivec2\"},{name:\"stride\",type:\"ivec2\"},{name:\"dilation\",type:\"ivec2\"},{name:\"inChannels\",type:\"int\"},{name:\"itemsPerBlockRow\",type:\"int\"},{name:\"outWidth\",type:\"int\"}],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let{dataFormat:n}=t,s=hn(),r=n===\"channelsLast\",a=r?0:1,i=r?1:2,o=this.enableShapeUniforms?\"if(blockIndex < outShape[1] && pos < outShape[0]) {\":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,u=\"\";for(let l=0;l<=1;l++)for(let c=0;c<=1;c++)u+=`\n          blockIndex = rc.y + ${c};\n          pos = rc.x + ${l};\n\n          ${o}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${a}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${i}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${r}) {\n                  innerDims = vec2(d1, ch);\n                  result[${l*2+c}] = getChannel(\n                    getA(d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${l*2+c}] = getChannel(\n                    getA(ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${s.output} = result;\n      }\n    `}};function G1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat===\"channelsLast\",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>z1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},C=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(Ju(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(T);let E=Pd({a:k,b:T,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>\"batchMatMul result is expected to be packed\"),l.shape=C,A.shape=n.outShape,g=En({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),T=Pd({a:k,b:C,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:T},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(C),b.push(T)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function H1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h===\"channelsLast\",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(C);let T=new FY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(T,[k],\"float32\",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o===\"leakyrelu\",z=o?jp(o,!0):null,W=new P1(P.shape,C.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,C];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],\"float32\",w.createScalarValue(i,\"float32\"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,\"float32\"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function OY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type===\"SAME\"||d.padInfo.type===\"VALID\"))h=G1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool(\"WEBGL_CONV_IM2COL\")&&r.shape[0]===1)h=H1({x:r,filter:a,convInfo:d,backend:n});else{let m=new U1(d);h=n.runWebGLProgram(m,[r,a],\"float32\")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var PY={kernelName:Na,backendName:\"webgl\",kernelFunc:OY},zY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              if (${a}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},MY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=a?1:2,l=a?2:3,c=a?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${i}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${a}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},LY=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${r};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${a};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${s} - ${i};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},BY=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=s-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${o}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${r}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${a}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${s}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${i}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${s} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new zY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var WY={kernelName:ug,backendName:\"webgl\",kernelFunc:VY};function UY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new MY(d);return n.runWebGLProgram(h,[r,a],\"float32\")}var GY={kernelName:Ta,backendName:\"webgl\",kernelFunc:UY};function HY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new DY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var qY={kernelName:Kd,backendName:\"webgl\",kernelFunc:HY};function jY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=N.computeConv3DInfo(r.shape,u,i,1,o),c=new LY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var KY={kernelName:lg,backendName:\"webgl\",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=N.computeConv3DInfo(u,a.shape,o,1,i),c=new BY(l);return n.runWebGLProgram(c,[r,a],\"float32\")}var YY={kernelName:cg,backendName:\"webgl\",kernelFunc:XY},QY=iu+`\n  return cos(x);\n`,ZY=Ke({opSnippet:QY}),JY={kernelName:$a,backendName:\"webgl\",kernelFunc:ZY},e9=`\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,t9=Ke({opSnippet:e9}),n9={kernelName:_a,backendName:\"webgl\",kernelFunc:t9},s9=class{constructor(e,t,n,s,r){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s===\"bilinear\"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,\"(y2-y1) * height_ratio\",`y1*${h} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,\"(x2-x1) * width_ratio\",`x1*${f} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${y});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${a}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${v};\n\n        float in_y = ${b};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${r}));\n          return;\n        }\n        float in_x = ${x};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${r}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${d} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}},r9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new s9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],\"float32\")},a9={kernelName:lo,backendName:\"webgl\",kernelFunc:r9},fw=class{constructor(e,t,n){this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}],this.outputShape=e;let s=e.length,r=t?\"0.0\":`getX(${mw(s,\"coords\")})`,a=e[e.length-1],i=\"\",o=\"\";t?(i=n?`end != ${a-1}`:\"end != 0\",o=n?\"end + 1\":\"end - 1\"):(i=n?`end + pow2 < ${a}`:\"end >= pow2\",o=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n      void main() {\n        ${ot(s)} coords = getOutputCoords();\n        int end = ${gw(s,\"coords\")};\n        float val = ${r};\n        int pow2 = int(pow(2.0, index));\n        if (${i}) {\n          int idx = ${o};\n          ${gw(s,\"coords\")} = idx;\n          val += getX(${mw(s,\"coords\")});\n        }\n        setOutput(val);\n      }\n    `}};function mw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function gw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function i9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=N.getAxesPermutation([a],u),c=r;l!=null&&(c=cn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=En({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new fw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new fw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=cn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var o9={kernelName:uo,backendName:\"webgl\",kernelFunc:i9};function u9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=S1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=SK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var l9={kernelName:dg,backendName:\"webgl\",kernelFunc:u9},c9=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function d9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=new c9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var p9={kernelName:co,backendName:\"webgl\",kernelFunc:d9},q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u=\"\",l=\"\";n&&(s?u=`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?u=`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:u=`\n          float activation(float x) {\n            ${n}\n          }\n        `,l=\"result = activation(result);\");let c=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${o};\n        int q = d2 - d1 * ${o};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${a}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${i}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}},j1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=c,d=`\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`\n          vec4 xTexelC${g*2};\n          int xTexelC${g*2}Ready;\n          vec4 xTexelC${g*2+1};\n          int xTexelC${g*2+1}Ready;\n          vec4 xC${g};`;d+=`\n    for (int r = 0; r < ${l}; r++) {\n      `;for(let g=0;g<c;g++)d+=`\n          xTexelC${g*2} = vec4(0.0);\n          xTexelC${g*2}Ready = 0;\n          xTexelC${g*2+1} = vec4(0.0);\n          xTexelC${g*2+1}Ready = 0;\n          xC${g} = vec4(0.0);`;d+=`\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;for(let g=0;g<(p+1)/2;g++){let b=g*2;if(d+=`\n          xC = xCCorner + ${b*u};\n          `,o===1){if(b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n              `,u===1&&b>0?d+=`\n                xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n                `:d+=`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n                  } else {\n                    xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n                  }\n                  `):d+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xC${b} = xTexelC${b};\n                `,b+1<c)){let y=i%2===0?w.nearestLargerEven(u):u;u%2===0&&i%2===1||u%2!==0&&i%2!==1?(d+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${y};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                    xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${b+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${b+1}Ready = 1;\n                  }\n                  `,u>1&&(d+=`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                      xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                      xTexelC${b}Ready = 1;\n                    }\n                    `),d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n                  `):y===1?d+=`\n                    xC${b+1} = xTexelC${b};\n                    `:d+=`\n                    xCOffset = xC + ${y};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                      xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${b+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${b+1}Ready = 1;\n                    }\n\n                    xC${b+1} = xTexelC${b+1};\n                    `}}else b<c&&(i%2===1?(d+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n              `,b+1<c&&(d+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n                `)):(d+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n                  xTexelC${b} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${b}.zw = vec2(0.0);\n                  }\n                  xTexelC${b}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n                  xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${b+1}.zw = vec2(0.);\n                  }\n                  xTexelC${b+1}Ready = 1;\n                }\n\n                xC${b} = vec4(\n                  xTexelC${b}.xy, xTexelC${b+1}.xy);\n              `,b+1<c&&(d+=`\n                  xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n                `)));b<c&&(d+=`\n            wTexel = getW(r, ${b}, d1, q);\n            dotProd += xC${b} * vec4(wTexel.xz, wTexel.xz);\n          `,b+1<c&&(d+=`\n              wTexel = getW(r, ${b+1}, d1, q);\n              dotProd += xC${b+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}d+=`\n    }\n  `,d+=`\n      }\n    `;let h=\"\",f=\"\";n&&(s?h=`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:r?h=`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:h=`vec4 activation(vec4 x) {\n          ${n}\n        }`,f=\"result = activation(result);\");let m=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),r&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n      ${h}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${a};\n        int q = d2 - d1 * ${a};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${d}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${m}\n        ${f}\n        setOutput(result);\n      }\n    `}};function h9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new j1(p):d=new q1(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],\"float32\",h)}var f9={kernelName:Aa,backendName:\"webgl\",kernelFunc:h9},m9=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${a} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${s};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${r};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}},g9=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${a}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${s}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${r}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${o}; dm++) {\n              int d2 = d1 * ${o} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}};function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new m9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var y9={kernelName:pg,backendName:\"webgl\",kernelFunc:b9};function v9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new g9(p);return n.runWebGLProgram(d,[r,a],\"float32\")}var x9={kernelName:hg,backendName:\"webgl\",kernelFunc:v9},w9=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `}};function k9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new w9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var I9={kernelName:fg,backendName:\"webgl\",kernelFunc:k9},S9=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`\n      const ivec2 strides = ivec2(${r}, ${a});\n      const ivec2 pads = ivec2(${c}, ${p});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${i}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${o}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}};function C9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeDilation2DInfo(r.shape,a.shape,i,o,\"NHWC\",u),c,p=new S9(l);c=n.runWebGLProgram(p,[r,a],\"float32\");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var N9={kernelName:Xd,backendName:\"webgl\",kernelFunc:C9};function T9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=cn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=vv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Xp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var $9={kernelName:Yd,backendName:\"webgl\",kernelFunc:T9},_9=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",A9=`\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`,E9=Ke({opSnippet:_9,packedOpSnippet:A9}),R9={kernelName:Ra,backendName:\"webgl\",kernelFunc:E9},D9=\"return (b >= 1.0) ? a : a * (b + 1.0);\",F9=`\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,O9=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new Ql(F9,s.shape,r.shape):new no(D9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},P9={kernelName:mg,backendName:\"webgl\",kernelFunc:O9},z9=`\n  return vec4(equal(a, b));\n`,M9=\"return float(a == b);\",L9=qt({opSnippet:M9,packedOpSnippet:z9,dtype:\"bool\",cpuKernelImpl:TK}),B9={kernelName:po,backendName:\"webgl\",kernelFunc:L9},V9=`\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${N.ERF_P};\n  float a1 = ${N.ERF_A1};\n  float a2 = ${N.ERF_A2};\n  float a3 = ${N.ERF_A3};\n  float a4 = ${N.ERF_A4};\n  float a5 = ${N.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,W9=Ke({opSnippet:V9}),U9={kernelName:pl,backendName:\"webgl\",kernelFunc:W9},G9=iu+`\n  return exp(x);\n`,H9=`\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,K1=Ke({opSnippet:G9,packedOpSnippet:H9,cpuKernelImpl:$K,dtype:\"float32\"}),q9={kernelName:Da,backendName:\"webgl\",kernelFunc:K1};function Gm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var j9={kernelName:ho,backendName:\"webgl\",kernelFunc:Gm},bw=\"return exp(x) - 1.0;\",K9=Ke({opSnippet:bw,packedOpSnippet:bw,cpuKernelImpl:_K}),X9={kernelName:fo,backendName:\"webgl\",kernelFunc:K9},yw=class{constructor(e,t,n){this.variableNames=[\"real\",\"imag\"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n      const float exponentMultiplier = ${r};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${i}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${s});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${s}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${a};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}};function X1(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new yw(\"real\",u,t),c=new yw(\"imag\",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,\"float32\"),h=n.runWebGLProgram(c,p,\"float32\"),f=Er({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Y9(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!1,n)}var 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i < ${s}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${u};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n          }\n        }\n        ${r} coords = outC - start;\n        setOutput(getX(${o}));\n      }\n    `}},XZ=class{constructor(e,t,n){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ot(s),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,f)=>h[0]+e[f]).join(\",\"),o=un(\"rc\",s),u=un(\"source\",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?\"source\":`vec2(${u.slice(-2).join()})`,p=n===\"reflect\"?0:1,d=\"\";if(s===1){let h=`\n        ${r} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${p};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${p};\n        }\n        source -= start;\n      `;d=`\n        ${r} rc = outputLoc;\n        ${h}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${o[s-1]} += 1;\n        if(${l}) {\n          ${h}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n      `}else{let h=`\n        ${r} source = rc;\n        ${r} lt = ${r}(lessThan(source, start));\n        ${r} gte = ${r}(greaterThanEqual(source, end));\n        ${r} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${p}) +\n                gte * ((end - 1) * 2 - source + ${p});\n        source -= start;\n      `;d=`\n        ${r} rc = outputLoc;\n        ${h}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${o[s-1]} += 1;\n        if(${l}) {\n          ${h}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n        rc = outputLoc;\n        ${o[s-2]} += 1;\n        if(${o[s-2]} < ${this.outputShape[s-2]}) {\n          ${h}\n          result[2] = getChannel(getX(${u.join()}), ${c});\n          ${o[s-1]} += 1;\n          if(${l}) {\n            ${h}\n            result[3] = getChannel(getX(${u.join()}), ${c});\n          }\n        }\n      `}this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${d}\n        setOutput(result);\n      }\n    `}},YZ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new XZ(s.shape,r,a):new KZ(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},QZ={kernelName:ja,backendName:\"webgl\",kernelFunc:YZ},ZZ=`if (b == 0.0) return NAN;\n  return mod(a, b);`,JZ=`\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  `+qp+`\n  return result;\n`,e7=qt({opSnippet:ZZ,packedOpSnippet:JZ}),t7={kernelName:vl,backendName:\"webgl\",kernelFunc:e7},n7=class{constructor(e,t,n){this.variableNames=[\"probs\"],this.customUniforms=[{name:\"seed\",type:\"float\"}],this.outputShape=[e,n],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; 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Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=f7(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var g7={kernelName:So,backendName:\"webgl\",kernelFunc:m7},b7=xs.nonMaxSuppressionV4Impl;function y7(e){N.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=b7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],\"int32\",new Int32Array(d)),n.makeTensorInfo([],\"int32\",new Int32Array([h]))]}var v7={kernelName:xl,backendName:\"webgl\",kernelFunc:y7},x7=xs.nonMaxSuppressionV5Impl;function w7(e){N.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=x7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var k7={kernelName:Co,backendName:\"webgl\",kernelFunc:w7},I7=class{constructor(e,t,n,s){this.variableNames=[\"indices\"],this.outputShape=[e,t],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${s}), float(${n}),\n                      float(index == coords.y)));\n      }\n    `}},S7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new 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float(value);\n        } else {\n          ${r} source = rc - start;\n          result[${f}] = getChannel(getX(${u.join()}), ${c});\n        }\n      `;h+=s===1?\"} \":\"}}\",this.userCode=`\n      const ${r} start = ${r}(${a});\n      const ${r} end = ${r}(${i});\n\n      void main() {\n        ${r} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${h}\n        setOutput(result);\n      }\n    `}},n2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return Jl({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new E7(r.shape,a,i):new A7(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},R7={kernelName:Xa,backendName:\"webgl\",kernelFunc:n2},D7=`\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : 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t.makeTensorInfo([o.length],i,o)},L7={kernelName:wl,backendName:\"webgl\",kernelFunc:s2},B7=\"return 1.0 / x;\",V7=Ke({opSnippet:B7}),W7={kernelName:kl,backendName:\"webgl\",kernelFunc:V7},U7=ts+`\n  return (x < 0.0) ? 0.0 : x;\n`,G7=`\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,H7=Ke({opSnippet:U7,packedOpSnippet:G7}),q7={kernelName:Za,backendName:\"webgl\",kernelFunc:H7},j7=ts+`\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`,K7=`\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,X7=Ke({opSnippet:j7,packedOpSnippet:K7}),Y7={kernelName:ei,backendName:\"webgl\",kernelFunc:X7},Q7=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":p=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}},Z7=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":p=\"vec3(yRC) * 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sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}};function J7(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Z7(r.shape,u,l,a,i):new Q7(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],\"float32\")}var eJ={kernelName:Ja,backendName:\"webgl\",kernelFunc:J7},tJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function nJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new tJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var sJ={kernelName:Cg,backendName:\"webgl\",kernelFunc:nJ},rJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}},aJ=class{constructor(e,t,n,s,r){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?\"0.5\":\"0.0\",d;r?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,\n                                     ${o}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new aJ(r.shape,u,l,a,i):new rJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var oJ={kernelName:Il,backendName:\"webgl\",kernelFunc:iJ},uJ=class{constructor(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${p});\n        const float invWidthScale = float(${d});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${a}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${i}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${o[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${o[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${s}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}};function lJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new uJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var cJ={kernelName:Sg,backendName:\"webgl\",kernelFunc:lJ},dJ=class{constructor(e,t){this.variableNames=[\"x\"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `;return}let s=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>s(o)).join(\",\"),a=ot(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}},pJ=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=un(\"rc\",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ot(n);n===1?this.userCode=`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${r}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:this.userCode=`\n        void main() {\n          ${i} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${o(s.slice())};\n          if(${r}){\n            result.g = ${u(s.slice())};\n          }\n          if(${a}) {\n            result.b = ${l(s.slice())};\n            if(${r}) {\n              result.a = ${c(s.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;function o(h){return p(h)}function u(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",p(h)}function l(h){return h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function c(h){return h[n-1]=\"(\"+h[n-1]+\" + 1)\",h[n-2]=\"(\"+h[n-2]+\" + 1)\",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(\",\"),g=f.slice(-2).join(\",\");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function hJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return En({inputs:{x:r},backend:n});let u=X().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new pJ(r.shape,o):new dJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var fJ={kernelName:Eo,backendName:\"webgl\",kernelFunc:hJ},mJ=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let n=e[1],s=e[2];this.outputShape=e;let r=\"\";typeof t==\"number\"?r=`float outputValue = ${t.toFixed(2)};`:r=`\n        vec3 fill = vec3(${t.join(\",\")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${r}\n          if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}},gJ={kernelName:Ho,backendName:\"webgl\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new mJ(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},bJ=`\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`,yJ=Ke({opSnippet:bJ}),vJ={kernelName:Ro,backendName:\"webgl\",kernelFunc:yJ},xJ=\"return inversesqrt(x);\",wJ=Ke({opSnippet:xJ,cpuKernelImpl:jK}),kJ={kernelName:ti,backendName:\"webgl\",kernelFunc:wJ},r2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\");let c=`getIndices(${l})`,p=\"\";s===1?p=\"i\":s===2&&(p=\"i, coords[1]\");let d=`getUpdates(${p})`,h=t>1?\"strides[j]\":\"strides\";this.userCode=`\n        ${o} strides = ${o}(${r});\n\n        void main() {\n          ${u} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${c});\n              flattenedIndex += index * ${h};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${d};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `}};function IJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],\"float32\",new Float32Array([0])),g=new r2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var SJ={kernelName:Do,backendName:\"webgl\",kernelFunc:IJ},CJ=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r=\"resRC\",s=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`\n      void main() {\n        ${a} resRC = getOutputCoords();\n        float cVal = getC(${s});\n        if (cVal >= 1.0) {\n          setOutput(getA(${r}));\n        } else {\n          setOutput(getB(${r}));\n        }\n      }\n    `}};function NJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new CJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var TJ={kernelName:Fo,backendName:\"webgl\",kernelFunc:NJ},$J=`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${N.SELU_SCALEALPHA};\n  float scale = ${N.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,_J=Ke({opSnippet:$J}),AJ={kernelName:Sl,backendName:\"webgl\",kernelFunc:_J},EJ=iu+`\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`,RJ=`\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`,DJ=Ke({opSnippet:EJ,packedOpSnippet:RJ,cpuKernelImpl:KK}),FJ={kernelName:si,backendName:\"webgl\",kernelFunc:DJ},OJ=`\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`,PJ=Ke({opSnippet:OJ}),zJ={kernelName:Cl,backendName:\"webgl\",kernelFunc:PJ},MJ=iu+`\n  return sin(x);\n`,LJ=Ke({opSnippet:MJ}),BJ={kernelName:ni,backendName:\"webgl\",kernelFunc:LJ},VJ=`\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:Po,backendName:\"webgl\",kernelFunc:WJ},GJ=`\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`,HJ=Ke({opSnippet:GJ}),qJ={kernelName:Nl,backendName:\"webgl\",kernelFunc:HJ},jJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=n2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},KJ={kernelName:zo,backendName:\"webgl\",kernelFunc:jJ};function XJ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n         ${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n         ${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:\n         ${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n        ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=n.readSync(i.dataId)[0],[p,d,h,f,m]=YK(o,s.shape,s.dtype,u,r.dtype,l,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var YJ={kernelName:np,backendName:\"webgl\",kernelFunc:XJ};function QJ(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(s.dataId),u=Array.from(n.readSync(a.dataId)),[l,c,p]=QK(o,s.shape,s.dtype,i,u);return[n.makeTensorInfo(c,s.dtype,l),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var ZJ={kernelName:Tl,backendName:\"webgl\",kernelFunc:QJ};function JJ(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var eee={kernelName:sp,backendName:\"webgl\",kernelFunc:JJ};function tee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n             ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var nee={kernelName:rp,backendName:\"webgl\",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=new r2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var ree={kernelName:ap,backendName:\"webgl\",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=ou({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var iee={kernelName:Mo,backendName:\"webgl\",kernelFunc:aee},ww=\"return sqrt(x);\",oee=Ke({opSnippet:ww,packedOpSnippet:ww,cpuKernelImpl:ZK}),uee={kernelName:ri,backendName:\"webgl\",kernelFunc:oee},lee=\"return x * x;\",cee=Ke({opSnippet:lee}),dee={kernelName:$l,backendName:\"webgl\",kernelFunc:cee},kw=\"return (a - b) * (a - b);\",pee=qt({opSnippet:kw,packedOpSnippet:kw}),hee={kernelName:oi,backendName:\"webgl\",kernelFunc:pee};function fee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ts+`\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var mee={kernelName:di,backendName:\"webgl\",kernelFunc:fee},gee=class{constructor(e,t,n){this.variableNames=[\"x\"],this.outputShape=n;let s=n.length,r=ot(n.length),a=ot(n.length),i=\"\";if(s===1)i=\"coords * strides + begin\";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(\",\")}this.userCode=`\n      ${r} begin = ${r}(${e});\n      ${r} strides = ${r}(${t});\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${i}));\n      }\n    `}};function bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),E=ou({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=JK(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new gee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let C=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),C}var yee={kernelName:Lo,backendName:\"webgl\",kernelFunc:bee};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=eX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var xee={kernelName:ip,backendName:\"webgl\",kernelFunc:vee};function wee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=tX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],\"int32\",l),n.makeTensorInfo([d],\"string\",c),n.makeTensorInfo([2],\"int32\",new Int32Array(p))]}var kee={kernelName:Ng,backendName:\"webgl\",kernelFunc:wee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(r<=0)throw new Error(\"Number of buckets must be at least 1\");let i=n.readSync(a.dataId),o=nX(i,r);return n.makeTensorInfo(a.shape,\"int32\",o)}var See={kernelName:Tg,backendName:\"webgl\",kernelFunc:Iee},Cee=\"return tan(x);\",Nee=Ke({opSnippet:Cee}),Tee={kernelName:Bo,backendName:\"webgl\",kernelFunc:Nee},$ee=`\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,_ee=Ke({opSnippet:$ee}),Aee={kernelName:li,backendName:\"webgl\",kernelFunc:_ee},Eee=class{constructor(e,t){this.variableNames=[\"A\"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=Ree(e);this.userCode=`\n      void main() {\n        ${s} resRC = getOutputCoords();\n        setOutput(getA(${r}));\n      }\n    `}};function Ree(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function a2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype===\"string\"||r.shape.length>5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=rX(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new Eee(r.shape,a);return n.runWebGLProgram(i,[r],r.dtype)}var Dee={kernelName:Sr,backendName:\"webgl\",kernelFunc:a2},Fee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `}},Oee=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `}};function Wr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Iw(e){let t=1;for(;t<e;)t*=2;return t}function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\"),u=X().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=aX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,\"int32\",[])];if(c===1)return[r,Jl({attrs:{shape:l,dtype:\"int32\",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Wr(n,h);let b=Iw(a),y=Iw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new Fee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,\"int32\",q),Wr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Oee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,\"int32\",z),Wr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let C=v;v=ou({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Wr(n,C);let T=Y1({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Wr(n,g);let E=l.slice(0,-1);E.push(a),C=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Wr(n,C);let A=T;return T=he({inputs:{x:T},attrs:{shape:E},backend:n}),Wr(n,A),[T,v]}var zee={kernelName:Vo,backendName:\"webgl\",kernelFunc:Pee},Mee=class{constructor(e,t,n,s,r,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=n===\"nearest\"?1:2,o;switch(s){case\"constant\":o=1;break;case\"reflect\":o=2;break;case\"wrap\":o=3;break;case\"nearest\":o=4;break;default:o=1;break}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${o} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${o} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${r});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${r});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${i} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}};function Lee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Mee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],\"float32\")}var Bee={kernelName:Wo,backendName:\"webgl\",kernelFunc:Lee};function Vee(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tu(a,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=iX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],\"int32\",l)]}var Wee={kernelName:$g,backendName:\"webgl\",kernelFunc:Vee};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=ou({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Gee={kernelName:Uo,backendName:\"webgl\",kernelFunc:Uee},Hee=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o=\"0.0\",u=\"sumValue\",l=Math.floor(n/4)*4,c=n%4,p=`\n        sumValue += dot(values, segFilter);\n    `,d=\"\";r%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return initializationValue;\n        }\n      `);let h=\"\";r%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${r}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n\n      float getValue(int batch, int inIdx) {\n        ${d}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${h}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${a})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${a})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${p}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${c===1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${p}\n        } else if (${c===2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${p}\n        } else if (${c===3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${p}\n        }\n        setOutput(${u});\n      }\n    `}};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=N.getAxesPermutation([l],o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=N.getInnerMostAxes(1,o)[0]);let d=N.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=lp(r.dtype),g=(x,k,C,T,E)=>{let A=x.shape[0],P=x.shape[1],R=N.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new Hee(F,k),z=n.compileAndRun($,[x,C],T);if(u.push(z),z.shape[1]===E)return z;let W=s2({backend:n,attrs:{start:0,stop:E,step:1,dtype:\"float32\"}}),q=a2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,T,E)},b=g(f,\"unsortedSegmentSum\",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=N.getUndoAxesPermutation(c);v=cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jee={kernelName:op,backendName:\"webgl\",kernelFunc:qee},Kee=[e8,n8,a8,u8,c8,h8,m8,b8,w8,I8,N8,_8,R8,P8,L8,V8,U8,j8,X8,Q8,tY,uY,cY,pY,yY,xY,SY,OX,TY,RY,PY,WY,GY,qY,KY,YY,JY,n9,a9,o9,l9,p9,f9,y9,x9,I9,N9,$9,R9,P9,B9,U9,q9,j9,X9,Q9,J9,tQ,sQ,oQ,cQ,hQ,mQ,yQ,wQ,CQ,_Q,FX,EQ,AY,FQ,zQ,BQ,zX,GQ,KQ,YQ,eZ,sZ,oZ,cZ,fZ,yZ,wZ,IZ,TZ,_Z,EZ,OZ,zZ,LZ,VZ,UZ,jZ,QZ,t7,l7,WX,h7,g7,v7,k7,fY,C7,T7,_7,R7,P7,LX,M7,L7,mY,a7,W7,q7,Y7,GX,eJ,sJ,oJ,cJ,fJ,gJ,vJ,kJ,SJ,TJ,AJ,FJ,zJ,BJ,UJ,iY,o7,qJ,KJ,YJ,ZJ,eee,nee,ree,iee,uee,dee,hee,mee,yee,xee,kee,See,i7,QX,Tee,Aee,Dee,zee,Bee,ZX,Wee,Gee,jee,N7];for(let e of Kee)_l(e);var zs=X();zs.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\",()=>15);zs.registerFlag(\"WEBGPU_CPU_FORWARD\",()=>!0);zs.registerFlag(\"WEBGPU_MATMUL_WORK_PER_THREAD\",()=>4);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D\",()=>!1);zs.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\",()=>!1);zs.registerFlag(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\",()=>!1);zs.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\",()=>!1);zs.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\",()=>1e3);zs.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\",()=>!1);zs.registerFlag(\"WEBGPU_USE_IMPORT\",()=>!1);function Xee(e,t){if(Math.max(...e)>3)throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return\"i32\";if(e===2)return\"vec2<i32>\";if(e===3)return\"vec3<i32>\";if(e===4)return\"vec4<i32>\";throw Error(`GPU for rank ${e} is not yet supported`)}function ld(e,t){return e===\"float32\"?t?\"vec4<f32>\":\"f32\":e===\"int32\"||e===\"bool\"?t?\"vec4<i32>\":\"i32\":e}function wv(){return`\n  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`}function Rr(){return`\n  ${wv()}\n  fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,\n          @builtin(global_invocation_id) GlobalId : vec3<u32>,\n          @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n`}function Ue(){return`\n    ${Rr()}\n      let index = getGlobalIndex();\n`}function Yee(e,t,n,s=!1){let r=[];if(r.push(`\n    let workGroupSizeX = ${n.workGroupSize[0]}u;\n    let workGroupSizeY = ${n.workGroupSize[1]}u;\n    let workGroupSizeZ = ${n.workGroupSize[2]}u;\n\n    var<private> localId: vec3<u32>;\n    var<private> globalId: vec3<u32>;\n    var<private> numWorkgroups: vec3<u32>;\n\n    // Only used when the y/z dimension of workgroup size is 1.\n    fn getGlobalIndex() -> i32 {\n      if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {\n        return i32(globalId.x);\n      }\n\n      let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n          localId.y * workGroupSizeX + localId.x;\n      let workGroupID = (globalId - localId)/vec3<u32>(\n          workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n      return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n        workGroupID.y * numWorkgroups.x + workGroupID.x) *\n        (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n        localInvocationIndex);\n    }\n  `),s===!0)return r.push(`\n      struct Matrix0 {\n        numbers: array<${ld(t.dtype,n.isVec4)}>;\n      };\n      struct Uniform {\n        size            : i32;\n        numChannels     : i32;\n        outShapeStrides : vec2<i32>;\n        dispatchSize    : vec3<u32>;\n      };\n\n      @group(0) @binding(0) var<storage, write> result : Matrix0;\n      @group(0) @binding(2) var<uniform> uniforms: Uniform;\n    `),[Sw,r.join(`\n`),Cw(t.shape),n.getUserCode()].join(`\n`);let a=\"struct Uniforms { NAN : f32; \";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}; `}),a+=`outShape : ${Wt(t.shape.length)} ; `;let i=t.shape.length-1;a+=`\n       outShapeStrides: ${Wt(i)}; `,n.size&&(a+=\"size : i32; \"),n.uniforms&&(a+=n.uniforms),a+=\"};\",r.push(a),n.atomic?r.push(`\n    struct Matrix0 {\n        numbers: array<atomic<i32>>;\n    };\n\n    @group(0) @binding(0) var<storage, read_write> result : Matrix0;\n  `):r.push(`\n    struct Matrix0 {\n        numbers: array<${ld(t.dtype,n.isVec4)}>;\n    };\n\n    @group(0) @binding(0) var<storage, write> result : Matrix0;\n  `),n.variableNames.forEach((p,d)=>{r.push(`\n    struct Matrix${1+d} {\n      numbers: array<${ld(e[d].dtype,n.isVec4)}>;\n    };\n    @group(0) @binding(${1+d}) var<storage, read> ${p} : Matrix${1+d};\n    `)}),a!==\"\"&&r.push(`\n    @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms : Uniforms;\n    `);let[o,u]=nte(t.shape,n.dispatchLayout),l=[Sw,r.join(`\n`),Cw(t.shape),o,Qee(t.shape.length)];if(n.atomic||l.push(Zee(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Jee(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`\n`);l.push(p)}return l.push(n.getUserCode()),l.join(`\n`)}var Sw=`\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let mod: i32 = a % b;\n    if (sign < 0. && mod != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;function Qee(e){let t=\"\";switch(e){case 0:case 1:t+=`\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;break;case 2:t+=`\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;break;case 3:t+=`\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;break;case 4:t+=`\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Zee(e,t,n){let s=e.length,r=ld(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result.numbers[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result.numbers[flatIndex] = ${r}(value);\n    }`:a=`fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result.numbers[flatIndex] = ${r}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result.numbers[flatIndex] = ${r}(value);\n    }`,s>=2){let i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=Wt(s);n?a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `:a+=`\n      fn setOutputAtCoords(${i.map(u=>`${u} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${i.map(u=>`${u} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${o}(${i.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `}return a}function Jee(e,t,n,s){let r=ete(e,n);return e.shape.length<=t.length&&(r+=tte(e,t,n,s)),r}function ete(e,t){let n=e.name,s=e.shape.length,r=Wt(s),a=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),i=[\"d0\",\"d1\",\"d2\",\"d3\"].slice(0,s),o=i.map(c=>`${c} : i32`).join(\", \");if(s<1)return t?`\n        fn ${a}() -> vec4<f32> {\n          return vec4<f32>(${n}.numbers[0]);\n        }\n      `:`\n      fn ${a}() ->f32 {\n        return f32(${n}.numbers[0]);\n      }\n    `;let u=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,l=`${s}D`;return s===0&&(l=\"1D\"),t?`\n      fn ${a}(${o}) -> vec4<f32> {\n        return vec4<f32>(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n          ${u}) / 4]);\n      }\n      `:`\n    fn ${a}(${o}) -> f32 {\n      return f32(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(\",\")}),\n        ${u})]);\n    }\n   `}function tte(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),i=\"get\"+a+\"ByOutput\",o=e.shape.length,u=t.length,l=Wt(u);if(w.arraysEqual(e.shape,t)&&s)return n?`\n        fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n          return vec4<f32>(${r}.numbers[globalIndex]);\n        }\n\n        fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n          return vec4<f32>(${r}.numbers[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"} / 4]);\n        }\n        `:`\n      fn ${i}Index(globalIndex : i32) -> f32 {\n        return f32(${r}.numbers[globalIndex]);\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32 {\n        return f32(${r}.numbers[${u>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}]);\n      }\n      `;let c=N.getBroadcastDims(e.shape,t),p=u-o,d=\"\";if(o===0)return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> vec4<f32> {\n        return get${a}();\n      }\n    `:`\n      fn ${i}Index(globalIndex : i32) -> f32{\n        return get${a}();\n      }\n\n      fn ${i}Coords(coords : ${l}) -> f32{\n        return get${a}();\n      }\n    `;u<2&&c.length>=1?d=\"coords = 0;\":d=c.map(g=>`coords[${g+p}] = 0;`).join(`\n`);let h=\"\";if(u<2&&o>0)h=\"coords\";else if(u>1){let g=Wt(o),b=e.shape.map((y,v)=>`coords[${v+p}]`).join(\", \");h=`${g}(${b})`}else h=\"coords\";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${o}D`;return n?`\n      fn ${i}Index(globalIndex : i32) -> vec4<f32> {\n        var coords = getCoordsFromIndex(globalIndex);\n        ${d}\n        return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n\n      fn ${i}Coords(coordsIn : ${l}) -> vec4<f32> {\n        var coords = coordsIn;\n        ${d}\n        return ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];\n      }\n    `:`\n    fn ${i}Index(globalIndex : i32) -> f32 {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${d}\n      return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n\n    fn ${i}Coords(coordsIn : ${l}) -> f32 {\n      var coords = coordsIn;\n      ${d}\n      return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);\n    }\n  `}function nte(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${Wt(a)}{\n      let globalIndex = getGlobalIndex();\n      return getCoordsFromIndex(globalIndex);\n    }\n    `,a];let i=\"\",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Xee(h,\"uniforms.outShape\");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {\n    ${i}\n  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(\",\")}); }`,[p,u]}function Cw(e){let t=e.length;if(t<=1)return\"fn getCoordsFromIndex(index : i32) -> i32 { return index; }\";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;let a=\"var index2 = index;\"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join(\"\");return`\n    fn getCoordsFromIndex(index : i32) -> ${s} {\n      ${a}\n      return ${s}(${r.join(\",\")});\n    }\n  `}var i2={};Ae(i2,{ArrayBufferToTypedArray:()=>u2,GPUBytesPerElement:()=>Hm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>kv,computeWorkGroupSizeForMatMul:()=>o2,computeWorkPerThreadForConv2d:()=>Iv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Sv,tilesFitEvenlyIntoShape:()=>Ks});var ea=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ea(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(ea(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(ea(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function kv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function o2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Iv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function Hm(e){if(e===\"float32\"||e===\"int32\"||e===\"bool\"||e===\"string\")return 4;if(e===\"complex64\")return 8;throw new Error(`Unknown dtype ${e}`)}function u2(e,t){if(t===\"float32\")return new Float32Array(e);if(t===\"int32\")return new Int32Array(e);if(t===\"bool\"||t===\"string\")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Sv(){return(typeof window!=\"undefined\"||typeof WorkerGlobalScope!=\"undefined\")&&!!navigator.gpu}var ste=\"return a + b;\",rte=\"return areal * breal - aimag * bimag;\",ate=\"return areal * bimag + aimag * breal;\",ite=\"return a / b;\",ote=\"return a * b;\",ute=\"return (a - b) * (a - b);\",lte=\"return a - b;\",cte=\"return f32(a == b);\",dte=\"return vec4<f32>(a == b);\",pte=\"return f32(a > b);\",hte=\"return vec4<f32>(a > b);\",fte=\"return f32(a >= b);\",mte=\"return vec4<f32>(a >= b);\",gte=\"return f32(a < b);\",bte=\"return vec4<f32>(a < b);\",yte=\"return f32(a <= b);\",vte=\"return vec4<f32>(a <= b);\",xte=\"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\",wte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`,kte=`\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `,l2=`\n  if (isNaN.r) {\n    resultTemp.r = uniforms.NAN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = uniforms.NAN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = uniforms.NAN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = uniforms.NAN;\n  }\n  `,Ite=`\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `,Ste=`\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `,Cte=\"return f32(a != b);\",Nte=\"return vec4<f32>(a != b);\",Tte=`\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `,$te=`\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  ${l2}\n  return resultTemp;\n  `,_te=\"if (a < 0.0) { return b * a; }  return a;\",Ate=`\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;function Nw(e,t){let n=t?l2:kte;return t?`\n    var resultTemp = vec4<f32>(${e}(a, b));\n    let isNaN = isnanVec4(a) | isnanVec4(b);\n    `+n+`\n    return resultTemp;\n  `:n+`\n    return ${e}(a, b);\n  `}function ec(e,t){switch(e){case 0:return ote;case 1:return ste;case 2:return lte;case 3:return ite;case 4:return t?dte:cte;case 5:return t?hte:pte;case 6:return t?mte:fte;case 7:return t?bte:gte;case 8:return t?vte:yte;case 9:return t?wte:xte;case 10:return t?Nte:Cte;case 11:return ute;case 12:return t?Ste:Ite;case 14:return t?Ate:_te;case 15:return Nw(\"max\",t);case 16:return Nw(\"min\",t);case 13:return t?$te:Tte;case 17:return rte;case 18:return ate;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Ete=\"return abs(a);\",Rte=\"return ceil(a);\",Dte=\"return cos(a);\",Fte=`\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`,Ote=\"return exp(a) - 1.0;\",Pte=\"if (a >= 0.0) { return a; }  return (exp(a) - 1.0);\",zte=`\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Mte=\"return exp(a);\",Lte=\"return floor(a);\",Bte=\"return a;\",Vte=`if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`,Wte=\"return f32(!(a >= 1.0));\",Ute=\"return -a;\",Gte=\"return (a < 0.0) ? b * a : a;\",Hte=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",qte=\"if(a < 0.0) { return 0.0; } return a;\",jte=\"return clamp(a, 0.0, 6.0);\",Kte=\"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\",Xte=`\n  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));\n  let isNaN = isnanVec4(a);\n\n  if (isNaN.r) {\n    resFloat.r = a.r;\n  }\n  if (isNaN.g) {\n    resFloat.g = a.g;\n  }\n  if (isNaN.b) {\n    resFloat.b = a.b;\n  }\n  if (isNaN.a) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`,Yte=\"return 1.0/sqrt(a);\",Qte=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Zte=\"return sin(a);\",Jte=`\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`,ene=\"return sqrt(a);\",tne=\"return a * a;\",nne=`\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,sne=\"return f32(i32((a)));\";function Ui(e,t){switch(e){case 0:return Ete;case 2:return Dte;case 3:return Fte;case 1:return Rte;case 4:return t?zte:Pte;case 5:return Mte;case 6:return Ote;case 7:return Lte;case 8:return Bte;case 9:return Vte;case 10:return Wte;case 11:return Ute;case 12:return Gte;case 15:return Hte;case 13:return t?Xte:qte;case 14:return t?Kte:jte;case 16:return Yte;case 19:return Qte;case 17:return Zte;case 18:return Jte;case 20:return ene;case 21:return tne;case 22:return nne;case 23:return sne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e===\"linear\")return Ui(8);if(e===\"relu\")return Ui(13,t);if(e===\"elu\")return Ui(4,t);if(e===\"relu6\")return Ui(14,t);if(e===\"prelu\")return ec(14,t);if(e===\"sigmoid\")return Ui(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function c2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>\"tileInner must be divisible by 4. And ColPerThread must be 4\"),`\n  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;\n\n  let RowPerThread = ${e[1]};\n  let ColPerThread = ${e[0]};\n  let TileInner = ${s};\n\n  ${Rr()}\n\n    let tileRow = ${t===1?\"0\":\"i32(localId.y) * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${t===1?\"0\":\"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let numTiles = (uniforms.dimInner - 1) / TileInner + 1;\n\n    var acc: array<vec4<f32>, RowPerThread>;\n    var ACached : vec4<f32>;\n    var BCached : array<vec4<f32>, 4>;\n\n    // Loop over shared dimension.\n    var globalColA = tileCol;\n    let RowPerThreadB = TileInner / i32(workGroupSizeY);\n    let tileRowB = i32(localId.y) * RowPerThreadB;\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);\n        }\n        globalColA = globalColA + TileInner / ColPerThread;\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {\n            BCached[0] = mm_Bsub[k * ColPerThread][tileCol];\n            BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];\n            BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];\n            BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];\n\n            for (var i = 0; i < RowPerThread; i = i + 1) {\n                ACached = mm_Asub[tileRow + i][k];\n                acc[i] = BCached[0] * ACached.x + acc[i];\n                acc[i] = BCached[1] * ACached.y + acc[i];\n                acc[i] = BCached[2] * ACached.z + acc[i];\n                acc[i] = BCached[3] * ACached.w + acc[i];\n            }\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(globalRow + innerRow,\n                 globalCol,\n                 acc[innerRow], globalId);\n    }\n  }`}var rne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.tileAOuter=t[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,this.aShape=e,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=[this.tileAOuter,this.tileInner],r=[this.tileInner,this.tileBOuter];return[Ks(s,this.aShape.slice(1)),Ks(r,n.slice(1))]}getUserCode(){let e=this.fitA?\"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]\":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n            return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];\n        }\n        return vec4<f32>(0.0)`,t=this.fitB?\"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];\n        }\n        return vec4<f32>(0.0)`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${i}\n                }`:n=`\n            fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {\n              ${i}\n            }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${e};\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> vec4<f32> {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / 4;\n        let batch = i32(globalId.z);\n        ${t};\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {\n        if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])\n        {\n          var value = valueIn;\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col * 4);\n          ${r}\n          ${s}\n          setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);\n        }\n      }\n      ${c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}\n    `}};function Cv(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`\n    var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;\n    ${Rr()}\n      let tileRow = i32(localId.y) * ${e[1]};\n      let tileCol = i32(localId.x) * ${e[0]};\n\n      let globalRow = i32(globalId.y) * ${e[1]};\n      let globalCol = i32(globalId.x) * ${e[0]};\n\n      let numTiles = (uniforms.dimInner - 1) / ${r} + 1;\n\n      var acc : array<array<f32, ${e[0]}>, ${e[1]}>;\n      var ACached : f32;\n      var BCached : array<f32, ${e[0]}>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let ColPerThreadA = ${r} / ${t[0]};\n      let tileColA = i32(localId.x) * ColPerThreadA;\n      let RowPerThreadB = ${r} / ${t[1]};\n      let tileRowB = i32(localId.y) * RowPerThreadB;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileColA + innerCol;\n\n            mm_Asub[inputRow][inputCol] = mm_readA(\n                globalRow + innerRow,\n                t * ${r} + inputCol, globalId);\n          }\n        }\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n\n            mm_Bsub[inputRow][inputCol] = mm_readB(\n              t * ${r} + inputRow,\n              globalCol + innerCol, globalId);\n          }\n        }\n\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < ${r}; k = k + 1) {\n          for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n            ACached = mm_Asub[tileRow + innerRow][k];\n            for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {\n\n          if ((globalCol + innerCol) < uniforms.dimBOuter &&\n              (globalRow + innerRow) < uniforms.dimAOuter) {\n            mm_write(globalRow + innerRow,\n                     globalCol + innerCol,\n                     acc[innerRow][innerCol], globalId);\n          }\n        }\n      }\n    }\n  `}function ane(e){return`\n    let TileSize = ${e[0]*4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;\n\n    ${Rr()}\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),\n                                mm_readA(globalRow, colA + 1, globalId),\n                                mm_readA(globalRow, colA + 2, globalId),\n                                mm_readA(globalRow, colA + 3, globalId));\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),\n                              mm_readB(rowB + 1, globalCol, globalId),\n                              mm_readB(rowB + 2, globalCol, globalId),\n                              mm_readB(rowB + 3, globalCol, globalId));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {\n        mm_write(globalRow, globalCol, acc, globalId);\n      }\n    }\n  `}var d2=class{constructor(e,t,n,s=!1,r=!1,a=null,i=null,o=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=s?e[1]:e[2];this.workGroupSize=o2(t[1],u,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let l=a!=null,c=o!=null;l&&this.variableNames.push(\"bias\"),c&&this.variableNames.push(\"preluActivationWeights\"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=i,this.hasPreluActivationWeights=c;let p=this.outputShape[2],d=this.transposeB?[this.outputShape[0],p,u]:[this.outputShape[0],u,p];[this.fitA,this.fitB]=this.getShapeFit(d),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),w.assert(s%this.workGroupSize[0]===0&&s%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let r=[t,s],a=[s,n];return[Ks(r,this.aShape.slice(1)),Ks(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?\"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\n           }\n           return 0.0;`:e=this.fitA?\"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n             return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];\n           }\n           return 0.0;`;let t;this.transposeB===!1?t=this.fitB?\"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\n           }\n           return 0.0;`:t=this.fitB?\"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n             return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];\n           }\n           return 0.0;`;let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        var value = valueIn;\n        let batch = i32(globalId.z);\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${this.outputShape[1]>1?Cv([this.workPerThread,this.workPerThread,1],this.workGroupSize):ane(this.workGroupSize)}\n    `}};function ine(){return`\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${Rr()}\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `}var one=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize);let i=s!=null,o=a!=null;i&&this.variableNames.push(\"bias\"),o&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=n,this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e=\"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\":e=\"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];\";let t;this.transposeB===!1?t=\"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\":t=\"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];\";let n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${i}\n            }`:n=`\n              fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n                ${i}\n              }\n            `,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        ${e}\n      }\n\n      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n\n      fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {\n        var value = valueIn;\n        let outCoord = vec3<i32>(batch, row, col);\n        ${r}\n        ${s}\n        setOutputAtCoords(batch, row, col, value);\n      }\n      ${ine()}\n    `}};function une(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`\n  var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;\n  var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;\n  var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Introduces two shared memory buffers, some logical threads could handle\n  // arithmetic operations and others handle IO operations between barrier api,\n  // makes ALUs and load/store units work simultaneously, could improves\n  // the performance.\n  ${Rr()}\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${s} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = tileRow;\n    for (var t = 0; t < numTiles; t = t + 1) {\n      if (t == 0) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        }\n      } else {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub1[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n      if (t != 0) {\n        t = t + 1;\n      }\n\n      if (t < numTiles) {\n        if (tileRow < ${t}) {\n          // Load one tile of A and B into local memory.\n          // globalRow is always greater than or equal tileRow.\n          mm_Asub2[tileRow][tileCol] =\n              mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);\n          globalColA = globalColA + ${s};\n          mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);\n          globalRowB = globalRowB + ${s};\n        } else {\n          // Compute acc values for a single thread.\n          for (var k = 0; k < ${s}; k = k + 1) {\n            let subRow = tileRow - ${t};\n            if (subRow < 0) {\n              continue;\n            }\n            acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];\n          }\n        }\n      }\n      workgroupBarrier();\n    }\n    let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};\n    if (tileRow >= ${t} && writeCol >= 0) {\n      mm_write(writeCol, globalCol, acc, globalId);\n    }\n  }\n  `}var lne=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.workGroupSize=[8,16,1],w.assert(e[1]<=16||t[2]<=16,()=>\"This program can be only used when A width or B Height are small\"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let i=s!=null;i&&this.variableNames.push(\"bias\");let o=a!=null;o&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=i,this.activation=r,this.hasPreluActivationWeights=o,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {\n          return A.numbers[batch * batchASize + row * uniforms.dimInner + col];\n        }\n        return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];\n         }\n         return 0.0;`,n=\"\",s=\"\";if(this.activation){let i=Zs(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            let b = getPreluActivationWeightsByOutputCoords(outCoord);\n            ${i}\n            }`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {\n            ${i}\n        }`,s=\"value = activation(value, outCoord);\"}let r=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${n}\n\n      fn mm_readA(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n        let batch = i32(globalId.z);\n        ${e}\n      }\n      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {\n        let batch = i32(globalId.z);\n        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n        ${t}\n      }\n      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {\n        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {\n          let batch = i32(globalId.z);\n          let outCoord = vec3<i32>(batch, row, col);\n          var value = valueIn;\n          ${r}\n          ${s}\n          setOutputAtCoords(batch, row, col, value);\n        }\n      }\n      ${une(this.workGroupSize)}\n    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var cne={kernelName:Ao,backendName:\"webgpu\",kernelFunc:Me};function Nv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new one([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new lne(k,C,[P,h,f],a,u,i):R?F=new rne(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),a,u,i):F=new d2(k,[P,h,f],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),n,s,a,u,i);let $=[T,E];a&&$.push(a),i&&$.push(i);let z=[{type:\"int32\",data:[h]},{type:\"int32\",data:[f]},{type:\"int32\",data:[p]}],W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function dne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Nv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var pne={kernelName:na,backendName:\"webgpu\",kernelFunc:dne},Tw=class{constructor(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${ec(this.op,!1)}\n      }\n\n      ${Ue()}\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `}},hne=class{constructor(e,t,n,s){this.variableNames=[\"A\",\"B\"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",t=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);\n         let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];\n         let b = getBByOutputCoords(coords);`;return`\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${ec(this.op,!1)}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${Ue()}\n\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}.numbers[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${t}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `}},fne=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`\n      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {\n        ${ec(this.op,this.isVec4)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n    `}},p2=class{constructor(e,t,n){this.variableNames=[\"A\",\"B\"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`\n      fn binaryOperation(a : f32, b : f32) -> f32 {\n        ${ec(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          let b = getBByOutputIndex(index);\n          setOutputAtIndex(index, binaryOperation(a, b));\n        }\n      }\n      `}};function $w(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new fne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new hne(e,t,n,a):new p2(e,t,n)}function Zn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var mne={kernelName:Ma,backendName:\"webgpu\",kernelFunc:Zn};function uu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,\"complex64\"),i=n.tensorMap.get(a.dataId),o=Zn({inputs:{x:s},backend:n}),u=Zn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var gne={kernelName:qd,backendName:\"webgpu\",kernelFunc:uu},tc=class{constructor(e,t){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`\n      fn unaryOperation(a : f32) -> f32 {\n        ${Ui(this.op,!1)}\n      }\n      ${Ue()}\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new tc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function fn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype===\"complex64\"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=$w(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],yn(b.dtype,y.dtype))});else{let g=new Tw(17,i.shape,o.shape),b=new Tw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,\"float32\"),f=u.runWebGPUProgram(b,y,\"float32\")}let m=uu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||yn(i.dtype,o.dtype);if((i.dtype===\"string\"||o.dtype===\"string\"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype===\"string\"?N.fromUint8ToStringArray(p):p,f=i.dtype===\"string\"?N.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=$w(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:bne,ceilImpl:yne,concatImpl:vne,equalImpl:xne,expImpl:wne,expm1Impl:kne,floorImpl:Ine,gatherNdImpl:Sne,gatherV2Impl:Cne,greaterEqualImpl:Nne,greaterImpl:Tne,lessEqualImpl:$ne,lessImpl:_ne,logImpl:Ane,maxImpl:Ene,maximumImpl:Rne,minimumImpl:Dne,multiplyImpl:Fne,negImpl:One,notEqualImpl:Pne,prodImpl:zne,rangeImpl:Mne,rsqrtImpl:Lne,simpleAbsImpl:Bne,sliceImpl:Vne,stridedSliceImpl:Wne,stringNGramsImpl:Une,subImpl:Gne,tileImpl:Hne,topKImpl:qne,transposeImpl:jne,uniqueImpl:kpe}=Yy,Kne=jt({opType:0,cpuKernelImpl:Bne}),Xne={kernelName:ao,backendName:\"webgpu\",kernelFunc:Kne},Yne=fn({opSnippet:1,cpuKernelImpl:bne,supportsComplex:!0}),Qne={kernelName:kr,backendName:\"webgpu\",kernelFunc:Yne},Zne=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(\" + \");return`\n      ${Ue()}\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${e.join(`\n        `)}\n            setOutputAtIndex(flatIndex, ${t});\n          }\n        }\n      }\n    `}};function Jne(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Zn({inputs:{x:s[0]},backend:n});let r=s.map(o=>o.dtype).reduce((o,u)=>yn(o,u)),a=s.map(o=>o.shape),i=new Zne(a);return n.runWebGPUProgram(i,s,r)}var ese={kernelName:xa,backendName:\"webgpu\",kernelFunc:Jne},h2=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"axis : i32; infinityValue : f32;\",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims(\"arg\"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n===\"min\"?\"<\":\">\";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape[${r}]`;return`\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${e}\n\n      // In order to get a flattened index into the input tensor, we need to\n      // add back the index along the reduced dimension to |outputCoords|.\n      // This function outputs the offset to the first value along\n      // |axis| and the stride to get the next value of the input along |axis|.\n      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        var i = ${this.outputShape.length-1};\n\n        var stride = 1;\n        var inputStride = 1;\n        var offset = 0;\n\n        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {\n          let length = ${n(`${this.inputShape.length} - r`)};\n          if (${this.inputShape.length} - r == uniforms.axis) {\n            inputStride = stride;\n          } else {\n            offset = offset + ${t(\"outputCoords\",\"i\")} * stride;\n            i = i - 1;\n          }\n          stride = stride * length;\n        }\n\n        return vec2<i32>(offset, inputStride);\n      }\n\n      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{\n        return coordInfo[0] + coordInfo[1] * index;\n      }\n\n      ${Ue()}\n        let outputIndex = index / i32(workGroupSizeX);\n        let coordInfo = getInputCoordInfo(outputIndex);\n        let Length = ${n(\"uniforms.axis\")};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n\n        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(Length), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `}},tse=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey=\"transposeShared\"}getUserCode(){return`\n      let TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;\n      ${wv()}\n      fn main(@builtin(local_invocation_id) localId : vec3<u32>,\n              @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] =\n              A.numbers[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `}},nse=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Wt(this.outputShape.length),t=sse(this.newDim);return`\n      ${Ue()}\n\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(\n              ${e}(${t}), uniforms.aShape)]);\n          }\n        }\n      }\n    `}};function sse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=jne(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new tse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new nse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var rse={kernelName:ci,backendName:\"webgpu\",kernelFunc:vi};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMax\",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],\"max\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var ise={kernelName:wa,backendName:\"webgpu\",kernelFunc:ase};function ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims(\"argMin\",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],\"min\"),p=[{type:\"int32\",data:[i[0]]},{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],\"int32\",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var use={kernelName:il,backendName:\"webgpu\",kernelFunc:ose},f2=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;\",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e=\"resultValue = max(value, resultValue);\";this.poolType===\"avg\"&&(e=\"resultValue = resultValue + value; count = count + 1.0;\");let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / count\"),`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${e}\n            }\n          }\n\n          setOutputAtIndex(index, ${t});\n        }\n      }\n    `}},m2=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"stride : vec2<i32>;\",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `}};function lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});let p,d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new m2(c):(p=new f2(c,\"avg\"),d.push({type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var cse={kernelName:ka,backendName:\"webgpu\",kernelFunc:lse};function dse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Nv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var pse={kernelName:Ia,backendName:\"webgpu\",kernelFunc:dse},hse=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}; `,this.shaderKey=\"slice\"}getUserCode(){let e=Wt(this.rank),t=fse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>\"sourceLoc = uniforms.start + coords;\"):n=this.outputShape.map((r,a)=>`sourceLoc.${qm[a]} = uniforms.start[${a}] + coords.${qm[a]};`),`\n      ${Ue()}\n        if (index < uniforms.size) {\n          var sourceLoc : ${e};\n          let coords = getCoordsFromIndex(index);\n          ${n.join(`\n`)}\n          setOutputAtIndex(index, getSource(${t}));\n        }\n      }\n    `}},qm=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function fse(e){if(e===1)return\"sourceLoc\";if(e<=6)return qm.slice(0,e).map(t=>`sourceLoc.${t}`).join(\",\");throw Error(`Slicing for rank ${e} is not yet supported`)}function lu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype===\"string\"){let p=n.tensorMap.get(r.dataId),d=Vne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new hse(o,u),c=[{type:\"int32\",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var mse={kernelName:Oo,backendName:\"webgpu\",kernelFunc:lu},gse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},bse={kernelName:io,backendName:\"webgpu\",kernelFunc:gse},g2=fn({opSnippet:10,dtype:\"bool\",cpuKernelImpl:Pne}),yse={kernelName:Io,backendName:\"webgpu\",kernelFunc:g2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.real},backend:n})}var vse={kernelName:tp,backendName:\"webgpu\",kernelFunc:nc};function xse(e,t){let n=new tc(e.shape,23),s=t.runWebGPUProgram(n,[e],\"int32\");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a===\"complex64\"){if(r.dtype===\"complex64\")return Zn({inputs:{x:r},backend:n});let i=$t(r.shape),o=jm({inputs:{x:r},backend:n,attrs:{dtype:\"float32\"}}),u=uu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype===\"complex64\"){let i=nc({inputs:{input:r},backend:n}),o=jm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Zn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a===\"int32\")return xse(r,n);if(a===\"bool\"){let i=n.makeTensorInfo([],\"bool\",w.getTypedArrayFromDType(\"bool\",1)),u=g2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var wse={kernelName:Sa,backendName:\"webgpu\",kernelFunc:jm},kse=jt({opType:1,cpuKernelImpl:yne}),Ise={kernelName:Ca,backendName:\"webgpu\",kernelFunc:kse},Sse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32; maxVal : f32;\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `}},Cse=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32; maxVal : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n      ${Ue()}\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `}};function Nse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o,u=[{type:\"float32\",data:[a]},{type:\"float32\",data:[i]}];return w.sizeFromShape(r.shape)%4===0?o=new Sse(r.shape):o=new Cse(r.shape),n.runWebGPUProgram(o,[r],r.dtype,u)}var Tse={kernelName:Ir,backendName:\"webgpu\",kernelFunc:Nse},$se=class{constructor(e){this.uniforms=\"\",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey=\"concat\"}getUserCode(){let e=[];if(this.offsetLength>0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push(\"setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));\");return`\n      ${Ue()}\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${e.join(`\n        `)}\n          }\n        }\n      }\n    `}};function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var _se={kernelName:Qd,backendName:\"webgpu\",kernelFunc:Qp};function Km(e,t,n){let s=e[0].dtype;if(s===\"complex64\"){let h=e.map(y=>nc({inputs:{input:y},backend:n})),f=e.map(y=>Qp({inputs:{input:y},backend:n})),m=Km(h,t,n),g=Km(f,t,n),b=uu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s===\"string\"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=vne(f,m,s,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Ase(e,t,n),o=a.map(h=>h.shape),u=new $se(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:\"int32\",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:\"int32\",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Ase(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function b2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Zn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return N.assertParamsConsistent(u,a),Km(o,a,n)}var Ese={kernelName:oo,backendName:\"webgpu\",kernelFunc:b2},Rse=class{constructor(e,t){this.variableNames=[\"A\"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;\n      inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`\n    ${Ue()}\n\n      for(var i = 0; i<${this.workPerThread}; i = i + 1) {\n        let flatIndex = index * ${this.workPerThread} + i;\n\n        let rc = getCoordsFromIndex(flatIndex);\n\n        if(flatIndex < uniforms.size) {\n          let blockIndex = rc[0];\n          let pos = rc[1];\n\n          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];\n          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;\n          var value = 0.0;\n          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {\n            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -\n              uniforms.pad[0];\n            let d1 = offsetX + uniforms.dilation[0] * ((pos %\n              uniforms.itemsPerBlockRow) / uniforms.inChannels);\n            let ch = pos % uniforms.inChannels;\n            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {\n              value = getA(d0, d1, ch);\n            }\n          }\n          setOutputAtIndex(flatIndex, value);\n        }\n      }\n    }\n  `}},Dse=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;\n      dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.workGroupSize=[8,8,1],this.isVec4=!0,this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.outputShape[1]===1?this.elementsPerThread=[4,1,1]:this.elementsPerThread=[4,4,1],this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.hasLeakyreluAlpha&&this.variableNames.push(\"leakyreluAlpha\"),this.tileAOuter=this.outputShape[1]===1?1:this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=this.tileBOuter,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`}getShapeFit(){let e=[this.tileAOuter,this.tileInner],t=[this.tileInner,this.tileBOuter],n=this.outputShape[1]*this.outputShape[2],s=this.outputShape[3],r=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(e,[n,r]),Ks(t,[r,s])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);\n    let divBy4Remainder${e} = flatIndex${e} % 4;\n    let divBy4Index${e} = flatIndex${e} / 4;\n    let curData${e} = x.numbers[divBy4Index${e}];\n    if (divBy4Remainder${e} == 0) {\n      temp = curData${e};\n    } else {\n      // TODO: This could end up being a redundant load with another one in\n      // the same shader invocation. Perhaps there's an opportunity for\n      // optimization\n      let nextData${e} = x.numbers[divBy4Index${e} + 1];\n      if (divBy4Remainder${e} == 1) {\n        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);\n      } else if (divBy4Remainder${e} == 2) {\n        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);\n      } else if (divBy4Remainder${e} == 3) {\n        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);\n      }\n    }\n    `}getUserCode(){let e=c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),s=`let outRow = r / uniforms.outShape[2];\n        let outCol = r % uniforms.outShape[2];\n        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);\n        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];\n        let inChCoord = c % uniforms.xShape[3];\n        var coord = vec4<i32>(\n            batch,\n            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n            inChCoord);\n        var resData = vec4<f32>(0.0);\n        ${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for\n          // the 'same' padding type.\n          if (coordsInBounds4D(coord, uniforms.xShape)) {\n            resData = x.numbers[getIndexFromCoords4D(coord, uniforms.xShape) / 4];\n          } else {\n            resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);\n          ${this.getSampleAWithRemainder(1)}\n          resData = temp;\n          if (WCol == (uniforms.filterDims[1] - 1)) {\n            coord = vec4<i32>(\n              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);\n              ${this.getSampleAWithRemainder(2)}\n            if (inChCoord == 0) {\n              resData = vec4<f32>(resData.xyz, temp.x);\n            } else if (inChCoord == 1) {\n              resData = vec4<f32>(resData.xy, temp.xy);\n            } else {\n              resData = vec4<f32>(resData.x, temp.xyz);\n            }\n          }\n          `}\n        return resData;`,r=this.fitA?`${s}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {\n          ${s}\n         }\n         return vec4<f32>(0.0);\n        `,a=this.fitB?\"return W.numbers[row * uniforms.dimBOuter / 4 + col];\":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W.numbers[row * uniforms.dimBOuter / 4 + col];\n         }\n         return vec4<f32>(0.0);\n        `,i=\"\",o=\"\";if(this.activation){let c=Zs(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${c}\n        }`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(outCoord: vec4<f32>) -> vec4<f32> {\n          let b = getLeakyreluAlphaByOutputCoords(outCoord);\n          ${c}\n        }`,new Error(\"Leakyrelu is not supported.\");i=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          ${c}\n        }`}o=\"value = activation(value, outCoord);\"}let u=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n        ${i}\n        fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          let r = row;\n          let c = col * 4;\n          var batch = i32(globalId.z);\n          ${r}\n        }\n\n        fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {\n          ${a}\n        }\n\n        fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {\n          var batch = i32(globalId.z);\n          var value = valueInput;\n          if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)\n          {\n            let outCoord = vec4<i32>(\n              batch,\n              row / uniforms.outShape[2],\n              row % uniforms.outShape[2],\n              col * 4);\n            ${u}\n            ${o}\n            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],\n              value);\n          }\n        }\n        ${e}\n      `}},Fse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.outputShape=e.outShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Iv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>\"tileInner must be multiple of workgroupsize.x and workgroupsize.y\");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Cv(this.elementsPerThread,this.workGroupSize),t=`\n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);\n    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];\n    let coord = vec4<i32>(\n        batch,\n        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],\n        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],\n        col % uniforms.xShape[3]);\n    // The bounds checking is always needed since we use it to pad zero for the\n    // 'same' padding type.\n    if(coordsInBounds4D(coord, uniforms.xShape)) {\n      return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      ${t}\n    }\n    return 0.0;\n    `,s=this.fitB?\"return W.numbers[row * uniforms.dimBOuter + col];\":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {\n           return W.numbers[row * uniforms.dimBOuter + col];\n\t }\n\t return 0.0;\n\t `,r=\"\",a=\"\";if(this.activation){let u=Zs(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {\n                  let b = getPreluActivationWeightsByOutputCoords(outCoord);\n                  ${u}\n                }`:r=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n                    ${u}\n                  }\n                `,a=\"value = activation(value, outCoord);\"}let i=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n    ${r}\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      ${n}\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      ${s}\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      ${i}\n      ${a}\n      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n    ${e}\n  `}},Ose=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;\",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n               let b = getPreluActivationWeightsByOutputCoords(outCoord);\n               ${r}\n             }`:e=`\n                  fn activation(a : f32, outCoord : vec4<i32>) -> f32{\n                    ${r}\n                  }\n                `,t=\"value = activation(value, outCoord);\"}let n=this.addBias?\"value = value + getBiasByOutputCoords(outCoord);\":\"\";return`\n      ${e}\n      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if(coordsInBounds4D(coord, uniforms.xShape)) {\n          return getX(batch, row, col, chan);\n        }\n        return 0.0;\n      }\n\n      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n        let coord = vec4<i32>(row, col, xChannel, outChannel);\n        if(coordsInBounds4D(coord, uniforms.wShape)) {\n          return getW(row, col, xChannel, outChannel);\n        }\n        return 0.0;\n      }\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          ${n}\n          ${t}\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Rr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let outChannel = coords[3];\n\n        var acc = 0.0;\n\n        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {\n              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];\n              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];\n              let v = readInp(batch, coordRow, coordCol, xChannel);\n              let f = readFilt(row, col, xChannel, outChannel);\n              acc = acc + v * f;\n            }\n          }\n        }\n\n        writeResult(batch, coords[1], coords[2], outChannel, acc);\n      }\n    `}};function Pse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat===\"channelsLast\",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Nv({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function zse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y===\"channelsLast\",x=u*l*c,k=m*f,C=[k,x],T=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Rse(C,v),$=[{type:\"int32\",data:[h.left,h.top]},{type:\"int32\",data:[p,d]},{type:\"int32\",data:[g,b]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*u]},{type:\"int32\",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,C[0],C[1]]}});A.push(z),A.push(W);let q=[1,C[0],C[1]],K=new d2(q,[1,k,n.outChannels],X().get(\"WEBGPU_MATMUL_WORK_PER_THREAD\"),T,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:\"int32\",data:[Y]},{type:\"int32\",data:[te]},{type:\"int32\",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a);let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function y2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type===\"VALID\"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type===\"SAME\"||n.padInfo.type===\"VALID\"))return Pse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")&&e.shape[0]===1)return zse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool(\"WEBGPU_USE_NAIVE_CONV2D\"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type===\"VALID\")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:\"int32\",data:[n.filterHeight,n.filterWidth]},{type:\"int32\",data:[...f]},{type:\"int32\",data:[n.strideHeight,n.strideWidth]},{type:\"int32\",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new Ose(n,u,o,l);else{h?c=new Dse(n,u,o,l):c=new Fse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:\"int32\",data:[b]},{type:\"int32\",data:[y]},{type:\"int32\",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),s.runWebGPUProgram(c,g,e.dtype,m)}function Mse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return y2({x:r,filter:a,convInfo:d,backend:s})}var Lse={kernelName:Na,backendName:\"webgpu\",kernelFunc:Mse},Bse=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;\",this.outputShape=e.inShape,w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Iv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`\n    fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      var batch = i32(globalId.z);\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n      \n    let outRow = row / uniforms.outShape[2];\n    let outCol = row % uniforms.outShape[2];\n\n    let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n    let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n    let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n    if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n      return 0.0;\n    }\n    if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n      return 0.0;\n    }\n    let coord = vec4<i32>(\n        batch,\n        i32(xR),\n        i32(xC),\n        col % uniforms.outBackprop[3]);\n    return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];\n    }\n    return 0.0;\n    }\n\n    fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {\n      let coordX = uniforms.filterDims.x - 1 -\n          row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let coordY = uniforms.filterDims.y - 1 -\n          (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n          coordX >= 0 && coordY >= 0) {\n        let coord = vec4<i32>(coordX, coordY, col,\n            row % uniforms.outBackprop[3]);\n        return W.numbers[getIndexFromCoords4D(coord, uniforms.wShape)];\n      }\n      return 0.0;\n    }\n\n    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {\n      var batch = i32(globalId.z);\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;\n    }\n\n    ${Cv(this.elementsPerThread,this.workGroupSize)}\n  `}},Vse=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`\n    ${Ue()} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${n}];\n\n        let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `}};function Wse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=[{type:\"int32\",data:[d.filterHeight,d.filterWidth]},{type:\"int32\",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(X().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\"))f=new Vse(d);else{f=new Bse(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],b=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:\"uint32\",data:[m]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[b]})}return n.runWebGPUProgram(f,[r,a],\"float32\",h)}var Use={kernelName:Ta,backendName:\"webgpu\",kernelFunc:Wse},Gse=jt({opType:2}),Hse={kernelName:$a,backendName:\"webgpu\",kernelFunc:Gse},qse=jt({opType:3}),jse={kernelName:_a,backendName:\"webgpu\",kernelFunc:qse},Kse=class{constructor(e,t,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32;\",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,o]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n      ${Ue()}\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${n});\n        let width_ratio = f32(${a});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${s};\n        let width_scale = ${i};\n        let in_y = ${r};\n        if( in_y < 0.0 || in_y > ${e} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${o};\n        if( in_x < 0.0 || in_x > ${t} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `}},Xse=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Kse(r.shape[3],a.shape,o,u),p=[{type:\"float32\",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],\"float32\",p)},Yse={kernelName:lo,backendName:\"webgpu\",kernelFunc:Xse},Qse=class{constructor(e,t){this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32;\",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=[{type:\"int32\",data:[a]}],g=new Qse(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Jse={kernelName:co,backendName:\"webgpu\",kernelFunc:Zse},v2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;\",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {\n            ${r}\n          }\n        `,t=\"dotProd[i] = activation(dotProd[i], coords);\"}let n=this.addBias?\"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      ${wv()}\n      fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = 0;\n        let r = i32(globalId.x);\n        let c = i32(globalId.y) * 4;\n        let d2 = i32(globalId.z) * 4;\n        let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;\n        let d1 = d2;\n        let q = 0;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n\n        var wVals : array<vec4<f32>, 9>;\n        wVals[0] = getW(0, 0, d1, q);\n        wVals[1] = getW(0, 1, d1, q);\n        wVals[2] = getW(0, 2, d1, q);\n        wVals[3] = getW(1, 0, d1, q);\n        wVals[4] = getW(1, 1, d1, q);\n        wVals[5] = getW(1, 2, d1, q);\n        wVals[6] = getW(2, 0, d1, q);\n        wVals[7] = getW(2, 1, d1, q);\n        wVals[8] = getW(2, 2, d1, q);\n\n        var xVals : array<array<vec4<f32>, 6>, 3>;\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          let xR = xRCorner + wR * uniforms.dilation[0];\n          for (var wC = 0; wC < 6; wC = wC + 1) {\n            let xC = xCCorner + wC * uniforms.dilation[1];\n            if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {\n              xVals[wR][wC] = vec4<f32>(0.0);\n            } else {\n              xVals[wR][wC] = getX(batch, xR, xC, d1);\n            }\n          }\n        }\n\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        for (var wR = 0; wR < 3; wR = wR + 1) {\n          for (var wC = 0; wC < 3; wC = wC + 1) {\n            let indexW = wR * 3 + wC;\n            dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];\n            dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];\n            dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];\n            dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d2);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            ${n}\n            ${t}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n          }\n        }\n      }\n    `}},x2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;\n      inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;\n      channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),s&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e=\"\",t=\"\";if(this.activation){let r=Zs(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n          let b = getPreluActivationWeightsByOutputCoords(outCoord);\n          ${r}\n        }`:e=`\n          fn activation(a : f32, outCoord : vec4<i32>) -> f32 {\n            ${r}\n          }\n        `,t=\"dotProd = activation(dotProd, coords);\"}let n=this.addBias?\"dotProd = dotProd + getBiasByOutputCoords(coords);\":\"\";return`\n      ${e}\n\n      fn writeResult(batch : i32, row : i32, col : i32, chan : i32,\n          value : f32) {\n        let coord = vec4<i32>(batch, row, col, chan);\n        if (coordsInBounds4D(coord, uniforms.outShape)) {\n          setOutputAtCoords(batch, row, col, chan, value);\n        }\n      }\n\n      ${Rr()}\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n        let d2 = coords[3];\n        let d1 = d2 / uniforms.channelMul;\n        let q = d2 - d1 * uniforms.channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            // Here using a constant value |this.convInfo.filterHeight| instead\n            // of uniform value is in order to loop unrolling.\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = getX(batch, xR, xC, d1);\n                let wVal = getW(wR, wC, d1, q);\n                dotProd = dotProd + xVal * wVal;\n              }\n            }\n          }\n\n        ${n}\n        ${t}\n        writeResult(batch, coords[1], coords[2], d2, dotProd);\n      }\n    `}};function ere(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:\"int32\",data:[p.padInfo.top,p.padInfo.left]},{type:\"int32\",data:[p.strideHeight,p.strideWidth]},{type:\"int32\",data:[p.dilationHeight,p.dilationWidth]},{type:\"int32\",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.filterHeight===3&&p.inChannels%4===0?h=new v2(p):(h=new x2(p),d.push({type:\"int32\",data:[p.filterHeight]},{type:\"int32\",data:[p.filterWidth]},{type:\"int32\",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var tre={kernelName:Aa,backendName:\"webgpu\",kernelFunc:ere},w2=fn({opSnippet:0,cpuKernelImpl:Fne,supportsComplex:!0}),nre={kernelName:Ka,backendName:\"webgpu\",kernelFunc:w2},sre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"reduceSize : i32;\",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\";this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n           {  bestValue = candidate; }`,t=\"f32(x.numbers[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"&&(e=\" bestValue = bestValue * candidate; \",t=\"1.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${`\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${Ue()}\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${t};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x.numbers[offset + k]);\n           ${e}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${e}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${n}\n        }\n       }\n     `}};function sc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=vi({inputs:{x:e},attrs:{perm:l},backend:r}),u=N.getInnerMostAxes(u.length,a),i.push(c)),N.assertAxesAreInnerMostDims(s,u,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=N.expandShapeToKeepDim(p,o));let f;if((s===\"max\"||s===\"prod\")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case\"max\":let g=Ene(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case\"prod\":let{outVals:b,outShape:y,outDtype:v}=zne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s===\"mean\"?\"float32\":lp(e.dtype),x=[{type:\"int32\",data:[m]}],k=new sre(y,s),C=r.runWebGPUProgram(k,[c],v,x);i.push(C),f=Me({inputs:{x:C},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Tv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,\"sum\",n)}var rre={kernelName:ai,backendName:\"webgpu\",kernelFunc:Tv};function are(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=vi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=w2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Tv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var ire={kernelName:Yd,backendName:\"webgpu\",kernelFunc:are},ore=jt({opType:4}),ure={kernelName:Ra,backendName:\"webgpu\",kernelFunc:ore},lre=fn({opSnippet:4,dtype:\"bool\",cpuKernelImpl:xne}),cre={kernelName:po,backendName:\"webgpu\",kernelFunc:lre},k2=jt({opType:5,cpuKernelImpl:wne,dtype:\"float32\"}),dre={kernelName:Da,backendName:\"webgpu\",kernelFunc:k2};function Xm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var pre={kernelName:ho,backendName:\"webgpu\",kernelFunc:Xm},hre=jt({opType:6,cpuKernelImpl:kne}),fre={kernelName:fo,backendName:\"webgpu\",kernelFunc:hre},mre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n    ${Ue()}\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `}};function cu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a===\"string\"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new mre(s),o=[{type:\"float32\",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var gre={kernelName:hl,backendName:\"webgpu\",kernelFunc:cu},bre=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `}},yre={kernelName:mo,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bre(n.shape);return 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Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,\"int32\"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Nre=class{constructor(e,t,n,s,r){this.uniforms=\"varianceEpsilon : f32;\",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"offset\")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push(\"scale\")),this.offsetShape=s,this.scaleShape=r,this.shaderKey=\"batchNorm\"}getUserCode(){let e=\"0.0\";this.offsetShape!=null&&(e=\"getOffsetByOutputIndex(index)\");let t=\"1.0\";return this.scaleShape!=null&&(t=\"getScaleByOutputIndex(index)\"),`\n      ${Ue()}\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${e};\n          let scaleValue = ${t};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `}},Tre={kernelName:Pa,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:i,variance:o}=e,{varianceEpsilon:u}=t,l=n,c=[s,i,o],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new Nre(s.shape,i.shape,o.shape,p,d),f=[{type:\"float32\",data:[u]}];return l.runWebGPUProgram(h,c,s.dtype,f)}};function $re(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m);return y2({x:r,filter:a,convInfo:g,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:h})}var _re={kernelName:sa,backendName:\"webgpu\",kernelFunc:$re};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d}=s,h=c;h==null&&(h=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Ere={kernelName:ra,backendName:\"webgpu\",kernelFunc:Are},Rre=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${Wt(e)};`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n        ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${e};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `}};function Dre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype===\"string\"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Sne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Rre(i,[l,c]),m=[{type:\"int32\",data:[i]},{type:\"int32\",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Fre={kernelName:bo,backendName:\"webgpu\",kernelFunc:Dre},Ore=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=Pre(this.aShape,\"i32\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function Pre(e,t=\"int\"){let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),C=n.tensorMap.get(d.dataId).values,T=De(d.shape,d.dtype,C),E=Cne(T,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Ore(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var zre={kernelName:go,backendName:\"webgpu\",kernelFunc:C2},Mre=fn({opSnippet:5,cpuKernelImpl:Tne,dtype:\"bool\"}),Lre={kernelName:yo,backendName:\"webgpu\",kernelFunc:Mre},Bre=fn({opSnippet:6,dtype:\"bool\",cpuKernelImpl:Nne}),Vre={kernelName:za,backendName:\"webgpu\",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:\"float32\",data:[a]}],o=new tc(r.shape,15);return o.uniforms=\"alpha : f32;\",n.runWebGPUProgram(o,[r],\"float32\",i)}var Ure={kernelName:La,backendName:\"webgpu\",kernelFunc:Wre},Gre=fn({opSnippet:7,dtype:\"bool\",cpuKernelImpl:_ne}),Hre={kernelName:vo,backendName:\"webgpu\",kernelFunc:Gre},qre=fn({opSnippet:8,dtype:\"bool\",cpuKernelImpl:$ne}),jre={kernelName:xo,backendName:\"webgpu\",kernelFunc:qre},Kre=jt({opType:9,cpuKernelImpl:Ane}),Xre={kernelName:Ba,backendName:\"webgpu\",kernelFunc:Kre},Yre=fn({opSnippet:9,dtype:\"bool\"}),Qre={kernelName:wo,backendName:\"webgpu\",kernelFunc:Yre},Zre=jt({opType:10}),Jre={kernelName:yl,backendName:\"webgpu\",kernelFunc:Zre};function N2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return sc(r,a,i,\"max\",n)}var eae={kernelName:Va,backendName:\"webgpu\",kernelFunc:N2},tae=fn({opSnippet:15,cpuKernelImpl:Rne}),nae={kernelName:Wa,backendName:\"webgpu\",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});p=new m2(c),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]})}else p=new f2(c,\"max\"),d.push({type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var rae={kernelName:Ua,backendName:\"webgpu\",kernelFunc:sae};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return sc(r,i,a,\"mean\",n)}var iae={kernelName:Ga,backendName:\"webgpu\",kernelFunc:aae};function oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,\"min\",n)}var uae={kernelName:Ha,backendName:\"webgpu\",kernelFunc:oae},lae=fn({opSnippet:16,cpuKernelImpl:Dne}),cae={kernelName:qa,backendName:\"webgpu\",kernelFunc:lae},dae=class{constructor(e,t,n){this.uniforms=\"\",this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(\",\"),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + 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i.runWebGPUProgram(u,[s],s.dtype,o)}};function hae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=One(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new tc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var fae={kernelName:ko,backendName:\"webgpu\",kernelFunc:hae};function mae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],\"int32\",new Int32Array(p))}var gae={kernelName:So,backendName:\"webgpu\",kernelFunc:mae};function bae(e){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),n.makeTensorInfo([b.length],\"float32\",new Float32Array(b))]}var yae={kernelName:Co,backendName:\"webgpu\",kernelFunc:bae};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"complex64\"){let r=nc({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype===\"string\"?\"\":0},backend:n})}var vae={kernelName:Go,backendName:\"webgpu\",kernelFunc:Md};function T2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(s.dtype===\"complex64\"){let r=nc({inputs:{input:s},backend:n}),a=T2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xae={kernelName:No,backendName:\"webgpu\",kernelFunc:T2};function wae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Xm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let o=[],u=t.map(c=>{let p=Xm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=b2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var kae={kernelName:$o,backendName:\"webgpu\",kernelFunc:wae},Iae=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(\",\"),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:\"\"}`).join(\",\"),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?\"any(outC < start)\":\"outC < start\",o=e>1?\"any(outC >= end)\":\"outC >= end\",u=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let start = ${r};\n          let end = ${a};\n          let outC = getCoordsFromIndex(index);\n\n          if (${i} || ${o}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${u}));\n          }\n        }\n      }\n    `}},$2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Zn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return cu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:\"float32\",data:[i]}];a.map(l=>o.push({type:\"int32\",data:[l[0],l[1]]}));let u=new Iae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Sae={kernelName:Xa,backendName:\"webgpu\",kernelFunc:$2},Cae=fn({opSnippet:13}),Nae={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new p2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],\"float32\")}var $ae={kernelName:Qa,backendName:\"webgpu\",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,\"prod\",n)}var Aae={kernelName:_o,backendName:\"webgpu\",kernelFunc:_ae},Eae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Mne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Rae={kernelName:wl,backendName:\"webgpu\",kernelFunc:Eae},_2=fn({opSnippet:3}),Dae={kernelName:Ea,backendName:\"webgpu\",kernelFunc:_2},Fae=jt({opType:13}),Oae={kernelName:Za,backendName:\"webgpu\",kernelFunc:Fae},Pae=jt({opType:14}),zae={kernelName:ei,backendName:\"webgpu\",kernelFunc:Pae},Mae=class{constructor(e,t,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n      ${Ue()}\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Lae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:i,halfPixelCenters:o}=s,[u,l]=i,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[o?.5:0]}],f=new Mae(r.shape,u,l);return n.runWebGPUProgram(f,[r],\"float32\",h)}var Bae={kernelName:Ja,backendName:\"webgpu\",kernelFunc:Lae},Vae=class{constructor(e,t,n,s){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2<f32>; roundBase : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))\":e=\"vec2<f32>(rc) * effectiveInputOverOutputRatioRC\",`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${e};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `}};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:\"float32\",data:[c,p]},{type:\"float32\",data:[a?.5:0]}],f=new Vae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Uae={kernelName:Il,backendName:\"webgpu\",kernelFunc:Wae},Gae=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;\n          cosRadians : f32;`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32;\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3<f32>;\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n        ${Ue()}\n\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `}},Hae={kernelName:Ho,backendName:\"webgpu\",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Gae(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:\"float32\",data:[l]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(r)]},{type:\"float32\",data:[Math.cos(r)]}];return typeof a==\"number\"?p.push({type:\"float32\",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:\"float32\",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},qae=jt({opType:16,cpuKernelImpl:Lne}),jae={kernelName:ti,backendName:\"webgpu\",kernelFunc:qae},Kae=class{constructor(e,t,n,s,r,a,i){this.variableNames=[\"updates\",\"indices\"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",s=\"\",r=\"\",a=\"\";this.updatesRank===1?(s=\"coords[0]\",r=\"flattenedIndex\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `):this.updatesRank===2&&(s=\"coords[0], coords[1]\",r=\"vec2<i32>(flattenedIndex, coords[1])\",a=`\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        let d0 = index / uniforms.updatesShape[1];\n        let d1 = index - d0 * uniforms.updatesShape[1];\n        return vec2<i32>(d0, d1);\n      }\n      `);let i=`getUpdates(${s})`,o=this.type===\"int32\"?\"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));\":`\n     var assumed = atomicLoad(&(result.numbers[flatIndex]));\n     var success = 0;\n     for (; success == 0;) {\n       let new = bitcast<f32>(assumed) + updateValue;\n       let newI32 = bitcast<i32>(new);\n       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);\n       assumed = resValue[0];\n       success = resValue[1];\n     }\n     `;return`\n    ${a}\n\n      ${Ue()}\n\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${t}));\n            flattenedIndex = flattenedIndex + indexInside * ${n};\n          }\n          let updateValue = ${i};\n          let flatIndex = getOutputIndexFromCoords(${r});\n\n         ${o}\n        }\n      }`}};function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=cu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:\"int32\",data:[o]},{type:\"int32\",data:c},{type:\"int32\",data:[b]}],v=new Kae(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Yae={kernelName:Do,backendName:\"webgpu\",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=[\"c\",\"a\",\"b\"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let s=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${e});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${t}));\n          } else {\n            setOutputAtIndex(index, getB(${t}));\n          }\n        }\n      }\n    `}};function Zae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Qae(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var Jae={kernelName:Fo,backendName:\"webgpu\",kernelFunc:Zae},eie=jt({opType:19}),tie={kernelName:si,backendName:\"webgpu\",kernelFunc:eie},nie=jt({opType:17}),sie={kernelName:ni,backendName:\"webgpu\",kernelFunc:nie},rie=jt({opType:18}),aie={kernelName:Po,backendName:\"webgpu\",kernelFunc:rie},A2=fn({opSnippet:2,cpuKernelImpl:Gne,supportsComplex:!0}),iie={kernelName:ui,backendName:\"webgpu\",kernelFunc:A2};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=N2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=A2({inputs:{a:r,b:l},backend:n}),p=k2({inputs:{x:c},backend:n}),d=Tv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=_2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var uie={kernelName:ii,backendName:\"webgpu\",kernelFunc:oie},lie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=$2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},cie={kernelName:zo,backendName:\"webgpu\",kernelFunc:lie},die=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let l=\"\";n===1?l=\"i\":n===2&&(l=\"i, j\"),this.indicesSnippet=`getIndices(${l})`;let c=\"\";s===1?c=\"i\":s===2&&(c=\"i, coords[1]\"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?\"uniforms.strides[j]\":\"uniforms.strides\"}getUserCode(){return`\n      ${Ue()}\n\n        let globalIndex = index * ${this.workPerThread};\n        if (globalIndex < uniforms.size) {\n          var sum = vec4<f32>(0.0);\n          var found = vec4<bool>(false);\n          for (var i = 0; i < uniforms.updateSize; i = i + 1) {\n            var flattenedIndex = 0;\n            for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n              let indexInside = i32(round(${this.indicesSnippet}));\n              flattenedIndex = flattenedIndex + indexInside * ${this.strideString};\n            }\n            for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n              let curIndex = globalIndex + innerIndex;\n              let coords = getCoordsFromIndex(curIndex);\n              if (flattenedIndex == coords[0]) {\n                sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};\n                found[innerIndex] = true;\n              }\n            }\n          }\n          for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {\n            let curIndex = globalIndex + innerIndex;\n            if (curIndex < uniforms.size)\n            {\n              setOutputAtIndex(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));\n            }\n          }\n        }\n      }`}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=[{type:\"int32\",data:[l]},{type:\"int32\",data:[u]},{type:\"int32\",data:c}],f=new die(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var hie={kernelName:ap,backendName:\"webgpu\",kernelFunc:pie};function fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=lu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var mie={kernelName:Mo,backendName:\"webgpu\",kernelFunc:fie},gie=jt({opType:20}),bie={kernelName:ri,backendName:\"webgpu\",kernelFunc:gie},yie={kernelName:$l,backendName:\"webgpu\",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new tc(n.shape,21);return s.runWebGPUProgram(r,[n],n.dtype)}},vie=fn({opSnippet:11}),xie={kernelName:oi,backendName:\"webgpu\",kernelFunc:vie},wie=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n       ${Ue()}\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${t}));\n         }\n       }\n     `}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=wt.computeOutShape(y,v,x),T=lu({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});k=Me({inputs:{x:T},backend:n,attrs:{shape:f}}),n.disposeData(T.dataId)}else if(n.shouldExecuteOnCPU([r])){let T=n.readSync(r.dataId),E=De(r.shape,r.dtype,T),A=Wne(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let T=new wie(h),E=[{type:\"int32\",data:y},{type:\"int32\",data:x}],A=n.runWebGPUProgram(T,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Iie={kernelName:Lo,backendName:\"webgpu\",kernelFunc:kie};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Une(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],\"string\",f),n.makeTensorInfo(p.shape,\"int32\",m)]}var Cie={kernelName:ip,backendName:\"webgpu\",kernelFunc:Sie},Nie=jt({opType:22}),Tie={kernelName:li,backendName:\"webgpu\",kernelFunc:Nie},$ie=class{constructor(e,t){this.variableNames=[\"A\"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey=\"tile\"}getUserCode(){let e=_ie(this.rank,\"uniforms.\");return`\n      ${Ue()}\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${e}));\n        }\n      }\n    `}};function _ie(e,t=\"\"){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function Aie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype===\"string\"||r.shape.length>=5){let u=n.readSync(r.dataId),l=r.dtype===\"string\"?u.map(d=>w.decodeString(d)):u,c=De(r.shape,r.dtype,l),p=Hne(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let i=new $ie(r.shape,a);return n.runWebGPUProgram(i,[r],r.dtype)}var Eie={kernelName:Sr,backendName:\"webgpu\",kernelFunc:Aie},Rie=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;\n        dir : i32; inc : i32;`,this.shaderKey=\"swap\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}},Die=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=\"inputSize : i32; firstPass : i32; k : i32;\",this.shaderKey=\"merge\"}getUserCode(){return`\n        ${Ue()}\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `}};function Li(e,t){t!==null&&e.disposeData(t.dataId)}function Aw(e){let t=1;for(;t<e;)t*=2;return t}function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[C,T]=qne(k,o,r.dtype,a,i);return[n.makeTensorInfo(C.shape,C.dtype,C.values),n.makeTensorInfo(T.shape,T.dtype,T.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,\"int32\",[])];if(u===1)return[r,cu({attrs:{shape:o,dtype:\"int32\",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Aw(a),h=Aw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,C,T)=>{let E=m(),A=new Rie(T),R=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[C]}],F=f;f=n.runWebGPUProgram(A,E,\"int32\",R),Li(n,F)};for(let k=1;k<d;k*=2){let C=k*2;for(let T=k;T>=1;T/=2)g(C,T,[c,h])}for(let k=h;k>d;k/=2){let C=m(),T=new Die([c,k/2]),A=[{type:\"int32\",data:[u]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[d]}],P=f;f=n.runWebGPUProgram(T,C,\"int32\",A),Li(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=lu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Li(n,b);let y=C2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Li(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Li(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Li(n,x),[y,f]}var Oie={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Fie},Pie=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32; fillModeId : i32; fillValue : f32;\",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${Ue()}\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `}};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Pie(g),y=i===\"nearest\"?1:2,v;switch(o){case\"constant\":v=1;break;case\"reflect\":v=2;break;case\"wrap\":v=3;break;case\"nearest\":v=4;break;default:v=1;break}let x=[{type:\"int32\",data:[y]},{type:\"int32\",data:[v]},{type:\"float32\",data:[u]}];return n.runWebGPUProgram(b,[r,a],\"float32\",x)}var Mie={kernelName:Wo,backendName:\"webgpu\",kernelFunc:zie};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Bie={kernelName:Uo,backendName:\"webgpu\",kernelFunc:Lie},Vie=[pne,Xne,Qne,ese,ise,use,cse,pse,bse,wse,Ise,Tse,gne,Ese,Lse,Use,Hse,jse,Yse,Jse,tre,ire,ure,cre,dre,pre,fre,gre,yre,Sre,xre,kre,Tre,_re,Ere,Fre,zre,Lre,Vre,mne,_se,Ure,Hre,jre,Xre,Qre,Jre,eae,nae,rae,iae,uae,cae,pae,nre,fae,gae,yae,yse,xae,kae,Sae,Nae,$ae,Aae,Rae,vse,Dae,Oae,zae,cne,Bae,Uae,Hae,jae,Yae,Jae,tie,sie,aie,mse,Iie,Cie,uie,cie,hie,mie,bie,yie,xie,iie,rre,Tie,Eie,Oie,Mie,rse,Bie,vae];for(let e of Vie)_l(e);var Wie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Ew(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Ew(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Ew(e,t){return`${e}_${t}`}var E2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey=\"fromPixels\",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?\"textureLoad(src, vec2<i32>(coords.yx));\":\"textureLoad(src, vec2<i32>(coords.yx), 0)\";return`\n      @binding(1) @group(0) var src: ${this.useImport?\"texture_external\":\"texture_2d<f32>\"};\n\n      ${Ue()}\n        let flatIndexBase = index * uniforms.numChannels;\n        for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n          let flatIndex = flatIndexBase + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndexBase);\n            let values = ${e};\n            result.numbers[flatIndex] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:\"rgba8unorm\",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Uie=class extends E2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gie=X().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Rw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[a,a,a]):[a,a,1]},R2=class extends tl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Sv())throw new Error(\"WebGPU is not supported on this device\");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Wie(this.device),this.tensorMap=new Wd(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:2})),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return R2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*Hm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let a=w.sizeFromShape(n)*Hm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case\"copyExternal\":return this.fromPixelProgram||(this.fromPixelProgram=new E2),this.fromPixelProgram;case\"import\":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Uie),this.fromPixelImportProgram;default:w.assert(!1,()=>\"Unsupported fromPixels shape\");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(w.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype===\"complex64\"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=N.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=u2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype===\"string\")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t===\"string\"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type===\"int32\"?new Int32Array(s,o,a.data.length).set(a.data):a.type===\"uint32\"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"storage\"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"read-only-storage\"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:\"uniform\"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let T=this.tensorMap.get(r.dataId);return T.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Rw(this.device,e);let a=[{type:\"float32\",data:[NaN]}],i=t.concat(r).map(T=>T.shape),o=\"int32\";i.map(T=>{a.push({type:o,data:T})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let T=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?T/4:T]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((T,E)=>{if(T.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),p=c.map(T=>T.dtype).concat(r.dtype),d=c.map(T=>N.getBroadcastDims(T.shape,r.shape)),h=c.map(T=>w.arraysEqual(T.shape,r.shape)).join(\"_\"),f=d.map(T=>T.join(\"_\")).join(\";\"),m=S2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>I2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ire(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let C={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(C),X().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Rw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Gie){return X().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},$v=R2;$v.nextDataId=0;var Hie={};Ae(Hie,{WebGPUBackend:()=>$v,webgpu_util:()=>i2});Sv()&&dp(\"webgpu\",async()=>{X().set(\"CHECK_COMPUTATION_FOR_ERRORS\",!1);let e={powerPreference:X().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has(\"timestamp-query\");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=[\"timestamp-query\"]:console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.\");let a=await t.requestDevice(s);return new $v(a,r)},3);var Ct=(e=>(e[e.float32=0]=\"float32\",e[e.int32=1]=\"int32\",e[e.bool=2]=\"bool\",e[e.string=3]=\"string\",e[e.complex64=4]=\"complex64\",e))(Ct||{}),Zp=(e=>(e[e.linear=0]=\"linear\",e[e.relu=1]=\"relu\",e[e.relu6=2]=\"relu6\",e[e.prelu=3]=\"prelu\",e[e.leakyrelu=4]=\"leakyrelu\",e[e.sigmoid=5]=\"sigmoid\",e[e.elu=6]=\"elu\",e))(Zp||{}),D2;function qie(e){D2=e.wasm.cwrap(na,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Zp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,C=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(a.shape).buffer);return D2(d,C,r.shape.length,h,T,a.shape.length,u,l,g,f,m,p||0,k),x}var Kie={kernelName:na,backendName:\"wasm\",setupFunc:qie,kernelFunc:jie};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,[\"number\",\"number\",\"number\"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,Ct[o.dtype],c),l}return{kernelName:e,backendName:\"wasm\",setupFunc:s,kernelFunc:r}}var Xie=Kt(ao);function mn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=N.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,Ct[l.dtype],y))(),m}return{kernelName:e,backendName:\"wasm\",setupFunc:r,kernelFunc:a}}var Yie=!0,Qie=mn(kr,Yie),F2;function Zie(e){F2=e.wasm.cwrap(xa,null,[\"array\",\"number\",\"number\",\"number\"])}function Jie(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return F2(a,r.length,Ct[s.dtype],i),s}var eoe={kernelName:xa,backendName:\"wasm\",setupFunc:Zie,kernelFunc:Jie};function Jp(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var toe={kernelName:Ma,backendName:\"wasm\",kernelFunc:Jp},O2;function noe(e){O2=e.wasm.cwrap(ci,null,[\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function so(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=roe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=soe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=Jp({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return O2(c,h,u.shape.length,Ct[u.dtype],p,d,a.length),l}function soe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function roe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var aoe={kernelName:ci,backendName:\"wasm\",kernelFunc:so,setupFunc:noe};function Dr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=N.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=N.getInnerMostAxes(i.length,r),u=so({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var P2;function ioe(e){P2=e.wasm.cwrap(rl,null,[\"number, number, number\"])}function ooe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims(\"all\",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;P2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var uoe={kernelName:rl,backendName:\"wasm\",setupFunc:ioe,kernelFunc:ooe},z2;function loe(e){z2=e.wasm.cwrap(al,null,[\"number, number, number\"])}function coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims(\"any\",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;z2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var doe={kernelName:al,backendName:\"wasm\",setupFunc:loe,kernelFunc:coe},M2;function poe(e){M2=e.wasm.cwrap(wa,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function hoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Dr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,\"int32\"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return M2(o,Ct[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var foe={kernelName:wa,backendName:\"wasm\",kernelFunc:hoe,setupFunc:poe},L2;function moe(e){L2=e.wasm.cwrap(ka,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function goe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=N.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,\"float32\"),k=s.dataIdMap.get(x.dataId).id;return L2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var boe={kernelName:ka,backendName:\"wasm\",setupFunc:moe,kernelFunc:goe};function bn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var yoe={kernelName:Ao,backendName:\"wasm\",kernelFunc:bn},B2;function voe(e){B2=e.wasm.cwrap(Ia,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\"])}function xoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"BatchMatMul for non non-float32 tensors not yet supported.\");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],C=bn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=bn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(C.dataId).id,A=n.dataIdMap.get(T.dataId).id,P=i?C.shape[2]:C.shape[1],R=o?T.shape[1]:T.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],C.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(C.shape).buffer),q=new Uint8Array(new Int32Array(T.shape).buffer);return B2(E,W,C.shape.length,A,q,T.shape.length,i,o,z),n.disposeData(C.dataId),n.disposeData(T.dataId),$.shape=v,$}var woe={kernelName:Ia,backendName:\"wasm\",setupFunc:voe,kernelFunc:xoe};function ba(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype===\"string\"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype===\"string\"){let f=Dd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)koe(u,c[0],d,a,i);else if(h===3)Ioe(u,c[0],c[1],d,a,i);else if(h===4)Soe(u,c[0],c[1],c[2],d,a,i);else{let f=Dd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function koe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ioe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Soe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Coe={kernelName:Oo,backendName:\"wasm\",kernelFunc:ba};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=bn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=so({inputs:{x:h},backend:n,attrs:{perm:l}}),m=bn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Toe={kernelName:io,backendName:\"wasm\",kernelFunc:Noe};function rc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var $oe={kernelName:Sa,backendName:\"wasm\",kernelFunc:rc},_oe=Kt(Ca),V2;function Aoe(e){V2=e.wasm.cwrap(Ir,null,[\"number\",\"number\",\"number\",\"number\"])}function Eoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return V2(o,a,i,l),u}var Roe={kernelName:Ir,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Eoe};function W2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return Jp({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(N.assertParamsConsistent(o,s),a[0].dtype===\"string\"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return bn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=N.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=Jy(f,r,t[0].dtype,m),b=N.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Doe={kernelName:oo,backendName:\"wasm\",kernelFunc:W2},U2;function Foe(e){U2=e.wasm.cwrap(Na,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Ooe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,C=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type===\"SAME\"?1:0;if(f.dataFormat!==\"channelsLast\")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let F=s.makeOutput(f.outShape,\"float32\"),$=s.dataIdMap.get(F.dataId).id;return U2(i,r.shape[0],r.shape[1],r.shape[2],o,m,g,b,y,v,x,R,k,C,T,E,A,P,$),F}var Poe={kernelName:Na,backendName:\"wasm\",setupFunc:Foe,kernelFunc:Ooe},G2;function zoe(e){G2=e.wasm.cwrap(Ta,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Moe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=N.convertConv2DDataFormat(u),h=N.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:C,strideHeight:T,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat===\"channelsLast\",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,\"float32\"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return G2(me,we,f,m,g,y,v,b,k,C,x,T,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Loe={kernelName:Ta,backendName:\"wasm\",setupFunc:zoe,kernelFunc:Moe},Boe=Kt($a),Voe=Kt(_a),H2=(e=>(e[e.bilinear=0]=\"bilinear\",e[e.nearest=1]=\"nearest\",e))(H2||{}),q2;function Woe(e){q2=e.wasm.cwrap(lo,null,[\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Uoe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!==\"float32\"&&(m=rc({backend:t,inputs:{x:o},attrs:{dtype:\"float32\"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,\"float32\"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return q2(g,b,y,c,k,p,d,H2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Goe={kernelName:lo,backendName:\"wasm\",setupFunc:Woe,kernelFunc:Uoe},j2;function Hoe(e){j2=e.wasm.cwrap(uo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function qoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype===\"float32\"||r.dtype===\"int32\",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=N.getAxesPermutation([a],u),c=r;l!==null&&(c=so({inputs:{x:r},attrs:{perm:l},backend:n}));let p=N.getInnerMostAxes(1,u)[0];N.assertAxesAreInnerMostDims(\"cumsum\",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;j2(f,i?1:0,o?1:0,h,m,Ct[r.dtype]);let g=d;if(l!==null){let b=N.getUndoAxesPermutation(l);g=so({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var joe={kernelName:uo,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:qoe},K2;function Koe(e){K2=e.wasm.cwrap(co,null,[\"number\",\"number\",\"number\",\"array\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function Xoe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i===\"NHWC\"?r.shape[1]:r.shape[2],l=i===\"NHWC\"?r.shape[2]:r.shape[3],c=i===\"NHWC\"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i===\"NHWC\"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,\"float32\"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return K2(b,a,i===\"NHWC\"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var Yoe={kernelName:co,backendName:\"wasm\",setupFunc:Koe,kernelFunc:Xoe},X2;function Qoe(e){X2=e.wasm.cwrap(Aa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Zoe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=N.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,C=h.strideHeight,T=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type===\"SAME\"?1:0;if(h.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,\"float32\"),F=s.dataIdMap.get(R.dataId).id;return X2(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,C,T,E,A,F),R}var Joe={kernelName:Aa,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe},eue=Kt(Ra),tue=!1,nue=mn(po,tue,\"bool\"),sue=Kt(Da,\"float32\");function Ym(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),bn({inputs:{x:r},backend:s,attrs:{shape:o}})}var rue={kernelName:ho,backendName:\"wasm\",kernelFunc:Ym};function Y2(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var aue={kernelName:hl,backendName:\"wasm\",kernelFunc:Y2},Q2;function iue(e){Q2=e.wasm.cwrap(mo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function oue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return Q2(a,o,u,l,c,i),r}var uue={kernelName:mo,backendName:\"wasm\",kernelFunc:oue,setupFunc:iue},lue=Kt(Fa),cue=!1,due=mn(Oa,cue),Z2;function pue(e){Z2=e.wasm.cwrap(Pa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function hue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return Z2(c,p,d,h,f,r,g),m}var fue={kernelName:Pa,backendName:\"wasm\",setupFunc:pue,kernelFunc:hue},J2;function mue(e){J2=e.wasm.cwrap(sa,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Zp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,\"float32\"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return J2(b,K,Y,Z,y,k,C,x,T,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var bue={kernelName:sa,backendName:\"wasm\",setupFunc:mue,kernelFunc:gue},eN;function yue(e){eN=e.wasm.cwrap(ra,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function vue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Zp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type===\"SAME\"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. 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x=t.dataIdMap.get(v.dataId).id;iN(u,b,x)}if(h&&t.disposeData(c.dataId),a){let x=N.expandShapeToKeepDim(v.shape,d);v.shape=x}return l.dtype!==\"float32\"&&t.disposeData(y.dataId),v}var Que={kernelName:Ga,backendName:\"wasm\",setupFunc:Xue,kernelFunc:Yue},oN;function Zue(e){oN=e.wasm.cwrap(Ha,null,[\"number\",\"number\",\"number\",\"number\"])}function Jue(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v)}let f=l.shape.length;N.assertAxesAreInnerMostDims(\"min\",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;oN(u,Ct[i.dtype],b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var ele={kernelName:Ha,backendName:\"wasm\",setupFunc:Zue,kernelFunc:Jue},tle=!1,nle=mn(qa,tle),uN=(e=>(e[e.reflect=0]=\"reflect\",e[e.symmetric=1]=\"symmetric\",e))(uN||{}),lN;function sle(e){lN=e.wasm.cwrap(ja,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"array\",\"number\",\"number\"])}function rle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return lN(i,l,t.shape.length,Ct[t.dtype],d,h,uN[r],u),o}var ale={kernelName:ja,backendName:\"wasm\",kernelFunc:rle,setupFunc:sle},ile=!0,ole=mn(Ka,ile),ule=Kt(ko);function _v(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var cN;function lle(e){cN=e.wasm.cwrap(So,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\"])}function cle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=cN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=_v(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],\"int32\",d)}var dle={kernelName:So,backendName:\"wasm\",setupFunc:lle,kernelFunc:cle},dN;function ple(e){dN=e.wasm.cwrap(xl,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"bool\"])}function hle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=dN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_v(t,d);t.wasm._free(m);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([],\"int32\",g);return[b,y]}var fle={kernelName:xl,backendName:\"wasm\",setupFunc:ple,kernelFunc:hle},pN;function mle(e){pN=e.wasm.cwrap(Co,\"number\",[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=pN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_v(t,d);t.wasm._free(g);let b=t.makeOutput([f],\"int32\",h),y=t.makeOutput([f],\"float32\",m);return[b,y]}var ble={kernelName:Co,backendName:\"wasm\",setupFunc:mle,kernelFunc:gle},yle=!1,vle=mn(Io,yle,\"bool\"),hN;function xle(e){hN=e.wasm.cwrap(To,null,[\"number\",\"number\",\"number\",\"number\",\"number\"])}function wle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],\"int32\"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return hN(p,a,i,o,l),u}var kle={kernelName:To,backendName:\"wasm\",setupFunc:xle,kernelFunc:wle};function Ile(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Sle={kernelName:No,backendName:\"wasm\",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Ym({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,\"All tensors passed to stack must have matching shapes\"),w.assert(i===c.dtype,()=>\"All tensors passed to stack 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mN={kernelName:Xa,backendName:\"wasm\",kernelFunc:$le,setupFunc:Tle},_le=!1,Ale=mn(Ya,_le),gN;function Ele(e){gN=e.wasm.cwrap(Qa,null,[\"number\",\"number\",\"number\"])}function Rle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!==\"float32\"&&(l=rc({backend:n,inputs:{x:s},attrs:{dtype:\"float32\"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,\"float32\"),p=n.dataIdMap.get(c.dataId).id;return gN(o,i,p),u.dtype!==\"float32\"&&n.disposeData(l.dataId),c}var Dle={kernelName:Qa,backendName:\"wasm\",setupFunc:Ele,kernelFunc:Rle},bN;function Fle(e){bN=e.wasm.cwrap(_o,null,[\"number\",\"number\",\"number\",\"number\"])}function Ole(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t),f=p;if(h){let 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this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),\"PThread\"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=w.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=w.sizeFromShape(e);switch(t){case\"float32\":return new Float32Array(s,r,a);case\"int32\":return new Int32Array(s,r,a);case\"bool\":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function ode(e){return(t,n)=>(w.fetch(e,{credentials:\"same-origin\"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function Fw(e,t,n){if(Ld!=null)return Ld;let s=\"tfjs-backend-wasm.wasm\";return e&&t?s=\"tfjs-backend-wasm-threaded-simd.wasm\":e&&(s=\"tfjs-backend-wasm-simd.wasm\"),Mu!=null&&Mu[s]!=null?Mu[s]:n+s}async function ude(){let[e,t]=await Promise.all([X().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),X().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")]);return new Promise((n,s)=>{let r={};r.locateFile=(o,u)=>{if(o.endsWith(\".worker.js\")){let l=rde,c=new Blob([l],{type:\"application/javascript\"});return URL.createObjectURL(c)}return o.endsWith(\".wasm\")?Fw(e,t,Ou!=null?Ou:u):u+o},Av&&(r.instantiateWasm=ode(Fw(e,t,Ou!=null?Ou:\"\")));let a=!1;r.onAbort=()=>{if(a||Lu)return;Lu=!0,s({message:\"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");Ld=e,Av=t}function Spe(e,t=!1){if(Lu)throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");if(typeof e==\"string\")Ou=e;else{Mu=e;let n=cde.filter(s=>Mu[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Av=t}var PN=-1,Zm=-1;function Cpe(e){PN=e}function Npe(){if(Zm===-1)throw new Error(\"WASM backend not initialized.\");return Zm}var Tpe=\"0.0.0\",dde=2;dp(\"wasm\",async()=>{let{wasm:e}=await ude();return new ide(e)},dde);var sr=\"3.14.0-20220307\",$pe={tfjs:sr,\"tfjs-core\":sr,\"tfjs-data\":sr,\"tfjs-layers\":sr,\"tfjs-converter\":sr,\"tfjs-backend-cpu\":sr,\"tfjs-backend-webgl\":sr,\"tfjs-backend-wasm\":sr};export{ao as Abs,nl as Acos,sl as Acosh,pb as AdadeltaOptimizer,hb as AdagradOptimizer,fb as AdamOptimizer,mb as AdamaxOptimizer,kr as Add,xa as AddN,rl as All,al as Any,wa as ArgMax,il as ArgMin,ol as Asin,ul as Asinh,ll as Atan,dl as Atan2,cl as Atanh,ka as AvgPool,Hd as AvgPool3D,ag as AvgPool3DGrad,rg as AvgPoolGrad,ide as BackendWasm,Ia as BatchMatMul,io as BatchToSpaceND,ig as Bincount,og as BroadcastArgs,w$ as BroadcastTo,mW as Callback,TL as CallbackList,Sa as Cast,Ca as Ceil,Ir as ClipByValue,qd as Complex,jd as ComplexAbs,oo as Concat,Na as Conv2D,ug as Conv2DBackpropFilter,Ta as Conv2DBackpropInput,Kd as Conv3D,lg as Conv3DBackpropFilterV2,cg as Conv3DBackpropInputV2,$a as Cos,_a as Cosh,lo as CropAndResize,uo as Cumsum,AL as CustomCallback,Wd as DataStorage,dg as DenseBincount,co as DepthToSpace,Aa as DepthwiseConv2dNative,pg as DepthwiseConv2dNativeBackpropFilter,hg as DepthwiseConv2dNativeBackpropInput,fg as Diag,Xd as Dilation2D,Xf as Dilation2DBackpropFilter,Kf as Dilation2DBackpropInput,jw as ENV,gW as EarlyStopping,Yd as Einsum,Ra as Elu,mg as EluGrad,m$ as Environment,po as Equal,pl as Erf,Da as Exp,ho as ExpandDims,fo as Expm1,gg as FFT,hl as Fill,mo as FlipLeftRight,Fa as Floor,Oa as FloorDiv,hd as FromPixels,Pa as FusedBatchNorm,sa as FusedConv2D,ra as FusedDepthwiseConv2D,qf as GPGPUContext,bo as GatherNd,go as GatherV2,w4 as GraphModel,yo as Greater,za as GreaterEqual,_L as History,bg as IFFT,Ma as Identity,Qd as Imag,Dt as InputSpec,fl as IsFinite,ml as IsInf,gl as IsNan,tl as KernelBackend,Jd as LRN,vg as LRNGrad,xL as LayerVariable,pr as LayersModel,La as LeakyRelu,vo as Less,xo as LessEqual,yg as LinSpace,Ba as Log,bl as Log1p,k$ as LogSoftmax,wo as LogicalAnd,yl as LogicalNot,Zd as LogicalOr,RS as MathBackendCPU,A1 as MathBackendWebGL,Va as Max,Ua as MaxPool,ep as MaxPool3D,wg as MaxPool3DGrad,xg as MaxPoolGrad,kg as MaxPoolWithArgmax,Wa as Maximum,Ga as Mean,Ha as Min,qa as Minimum,ja as MirrorPad,vl as Mod,gb as MomentumOptimizer,Ig as Multinomial,Ka as Multiply,ko as Neg,So as NonMaxSuppressionV3,xl as NonMaxSuppressionV4,Co as NonMaxSuppressionV5,Io as NotEqual,e_ as OP_SCOPE_SUFFIX,To as OneHot,No as OnesLike,$r as Optimizer,Vr as OptimizerConstructors,$o as Pack,Xa as PadV2,pde as Pool,Ya as Pow,Qa as Prelu,_o as Prod,bb as RMSPropOptimizer,_r as RNN,wl as Range,H$ as Rank,tp as Real,Ea as RealDiv,kl as Reciprocal,YF as Reduction,Za as Relu,ei as Relu6,Ao as Reshape,Ja as ResizeBilinear,Cg as ResizeBilinearGrad,Il as ResizeNearestNeighbor,Sg as ResizeNearestNeighborGrad,Eo as Reverse,Ho as RotateWithOffset,Ro as Round,ti as Rsqrt,kp as SGDOptimizer,Do as ScatterNd,Fo as Select,Sl as Selu,Vb as Sequential,si as Sigmoid,Cl as Sign,ni as Sin,Po as Sinh,Oo as Slice,ii as Softmax,Nl as Softplus,zo as SpaceToBatchND,np as SparseFillEmptyRows,Tl as SparseReshape,sp as SparseSegmentMean,rp as SparseSegmentSum,ap as SparseToDense,Mo as SplitV,ri as Sqrt,$l as Square,oi as SquaredDifference,di as Step,Lo as StridedSlice,ip as StringNGrams,Ng as StringSplit,Tg as StringToHashBucketFast,ui as Sub,ai as Sum,$s as SymbolicTensor,Bo as Tan,li as Tanh,et as Tensor,Vt as TensorBuffer,Sr as Tile,Vo as TopK,Wo as Transform,ci as Transpose,$g as Unique,Uo as Unpack,op as UnsortedSegmentSum,md as Variable,Go as ZerosLike,na as _FusedMatMul,Mt as abs,OA as acos,zA as acosh,ie as add,LA as addN,Bk as all,cm as any,Gu as argMax,GA as argMin,qA as asin,KA as asinh,YA as atan,ZA as atan2,eE as atanh,Wg as avgPool,Gk as avgPool3d,$A as backend,N as backend_util,Ade as basicLSTMCell,qu as batchNorm,vE as batchNorm2d,wE as batchNorm3d,IE as batchNorm4d,Ug as batchToSpaceND,Hk as bincount,rpe as booleanMaskAsync,NE as broadcastArgs,td as broadcastTo,qo as broadcast_util,xk as browser,De as buffer,fpe as callbacks,ce as cast,_E as ceil,Bn as clipByValue,lr as clone,aa as complex,Ft as concat,RE as concat1d,FE as concat2d,PE as concat3d,ME as concat4d,$M as constraints,qk as conv1d,ua as conv2d,jk as conv2dTranspose,Kk as conv3d,qE as conv3dTranspose,mde as copyRegisteredKernels,Hg as cos,Yk as cosh,wI as cosineWindow,Qk as cumsum,js as customGrad,k4 as data,QE as denseBincount,Mk as deprecationWarn,JE as depthToSpace,pp as depthwiseConv2d,gpe as deregisterOp,cp as device_util,Ede as diag,sR as dilation2d,vde as disableDeprecationWarnings,Re as dispose,xde as disposeVariables,xe as div,uR as divNoNan,Rde as dot,H3 as dropout,dR as einsum,hp as elu,yde as enableDebugMode,bde as enableProdMode,q3 as enclosingPowerOfTwo,Ss as engine,X as env,qn as equal,fR as erf,jn as exp,On as expandDims,yR as expm1,Zk as eye,ob as fft,Fl as fill,Tde as findBackend,$de as findBackendFactory,fp as floor,Lk as floorDiv,DX as forceHalfFloat,da as fused,ju as gather,W3 as gatherND,kk as gather_util,Cde as getBackend,Jv as getGradient,Yf as getKernel,Qf as getKernelsForBackend,Npe as getThreadsCount,wK as gpgpu_util,Ode as grad,Pde as grads,Wn as greater,jo as greaterEqual,kd as ifft,qg as imag,ds as image,ipe as inTopKAsync,PM as initializers,CB as input,_n as io,fI as irfft,Dde as isFinite,Fde as isInf,_R as isNaN,Ht as keep,xs as kernel_impls,yL as layers,jg as leakyRelu,Jk as less,Ko as lessEqual,AO as linalg,DR as linspace,bpe as loadGraphModel,ppe as loadLayersModel,OR as localResponseNormalization,Kn as log,Kg as log1p,Lde as logSigmoid,eI as logSoftmax,XR as logSumExp,Ds as logicalAnd,Qg as logicalNot,rI as logicalOr,Bde as logicalXor,lpe as losses,We as matMul,Q_ as math,As as max,Zg as maxPool,aI as maxPool3d,sD as maxPoolWithArgmax,Tr as maximum,It as mean,lm as memory,Vde as meshgrid,KV as metrics,pm as min,gp as minimum,lD as mirrorPad,dD as mod,cpe as model,cW as models,Jg as moments,ape as movingAverage,V as mul,Wde as multiRNNCell,gD as multinomial,kt as neg,RO as nextFrame,vI as norm,Ku as notEqual,yd as oneHot,zn as ones,Xn as onesLike,L as op,Ude as outerProduct,pi as pad,Gde as pad1d,Hde as pad2d,qde as pad3d,jde as pad4d,Kde as pool,ca as pow,tb as prelu,F_ as print,iI as prod,wde as profile,Xde as rand,Yde as randomGamma,zD as randomNormal,Pl as randomUniform,Xu as range,Sde as ready,xd as real,VD as reciprocal,dp as registerBackend,hpe as registerCallbackConstructor,S$ as registerGradient,_l as registerKernel,mpe as registerOp,dW as regularizers,Xs as relu,oI as relu6,Nde as removeBackend,G as reshape,Yn as reverse,Qde as reverse1d,Zde as reverse2d,Jde as reverse3d,epe as reverse4d,ub as rfft,uI as round,lI as rsqrt,Ie as scalar,M3 as scatterND,Sk as scatter_util,cI as selu,JD as separableConv2d,dpe as sequential,ae as serialization,Ide as setBackend,_de as setPlatform,Cpe as setThreadsCount,Ipe as setWasmPath,Spe as setWasmPaths,p5 as setWebGLContext,t3 as setdiff1dAsync,Yy as shared,qs as sigmoid,s3 as sign,upe as signal,dI as sin,pI as sinh,He as slice,rb as slice1d,hI as slice2d,ab as slice3d,wd as slice4d,wt as slice_util,ib as softmax,Ol as softplus,eb as spaceToBatchND,Vc as sparse,xI as sparseToDense,ope as spectral,Ln as split,ln as sqrt,ct as square,mI as squaredDifference,mr as squeeze,Qn as stack,bp as step,k3 as stridedSlice,Pf as string,ge as sub,ye as sum,lp as sumOutType,S3 as tan,Hu as tanh,hs as tensor,Qt as tensor1d,ji as tensor2d,sA as tensor3d,tpe as tensor4d,npe as tensor5d,spe as tensor6d,_s as tensor_util,xA as test_util,j as tidy,cs as tile,kde as time,N3 as topk,Fi as train,qe as transpose,lb as truncatedNormal,cx as unique,fde as unregisterGradient,hde as unregisterKernel,A3 as unsortedSegmentSum,Fs as unstack,yn as upcastType,w as util,zde as valueAndGrad,Mde as valueAndGrads,R3 as variable,MR as variableGrads,$pe as version,ype as version_converter,gde as version_core,vpe as version_cpu,t0 as version_layers,Tpe as version_wasm,xpe as version_webgl,wpe as webgl,d5 as webgl_util,Hie as webgpu,vn as where,bI as whereAsync,$t as zeros,je as zerosLike};\n/**\n * @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use backend file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * https://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n * @license\n * Copyright 2021 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the License);\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an AS IS BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n/**\n* @license\n* Copyright 2018 Google LLC. All Rights Reserved.\n* Licensed under the Apache License, Version 2.0 (the \"License\");\n* you may not use this file except in compliance with the License.\n* You may obtain a copy of the License at\n*\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Unless required by applicable law or agreed to in writing, software\n* distributed under the License is distributed on an \"AS IS\" BASIS,\n* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n* See the License for the specific language governing permissions and\n* limitations under the License.\n* =============================================================================\n*/\n/** @license See the LICENSE file. */\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type) as WebGLShader;\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader)}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer() as WebGLFramebuffer;\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture() as WebGLTexture;\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram as GLProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram as GLProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    // eslint-disable-next-line prefer-rest-params\n    const args = Array.prototype.slice.call(arguments, 1);\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 2048;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor)['shape']) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input['shape']}`);\n    if ((tensor as Tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  } else {\n    // check if resizing will be needed\n    if (typeof input['readyState'] !== 'undefined' && input['readyState'] <= 2) {\n      if (config.debug) log('input stream is not ready');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    const originalWidth = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n    const originalHeight = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n    if (!originalWidth || !originalHeight) {\n      if (config.debug) log('cannot determine input dimensions');\n      return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n    }\n    let targetWidth = originalWidth;\n    let targetHeight = originalHeight;\n    if (targetWidth > maxSize) {\n      targetWidth = maxSize;\n      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n    }\n    if (targetHeight > maxSize) {\n      targetHeight = maxSize;\n      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n    }\n\n    // create our canvas and resize it if needed\n    if ((config.filter.width || 0) > 0) targetWidth = config.filter.width;\n    else if ((config.filter.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n    if ((config.filter.height || 0) > 0) targetHeight = config.filter.height;\n    else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n    if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n    if (!inCanvas || (inCanvas?.width !== targetWidth) || (inCanvas?.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n    // draw input to our canvas\n    const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n    if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n      inCtx.putImageData(input, 0, 0);\n    } else {\n      if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n        inCtx.translate(originalWidth, 0);\n        inCtx.scale(-1, 1);\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n        inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n      } else {\n        inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas?.width, inCanvas?.height);\n      }\n    }\n\n    if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas?.height !== outCanvas?.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n    // imagefx transforms using gl from input canvas to output canvas\n    if (config.filter.enabled && env.webgl.supported) {\n      if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n      env.filter = !!fx;\n      if (!fx || !fx.add) {\n        if (config.debug) log('input process error: cannot initialize filters');\n        return { tensor: null, canvas: inCanvas };\n      }\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    } else {\n      copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n      if (fx) fx = null;\n      env.filter = !!fx;\n    }\n\n    if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n    if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n    // create tensor from image unless input was a tensor already\n    let pixels;\n    let depth = 3;\n    if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || (input['data'] && input['width'] && input['height'])) { // if input is imagedata, just use it\n      if (env.browser && tf.browser) {\n        pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n      } else {\n        depth = input['data'].length / input['height'] / input['width'];\n        // const arr = Uint8Array.from(input['data']);\n        const arr = new Uint8Array(input['data']['buffer']);\n        pixels = tf.tensor(arr, [input['height'], input['width'], depth], 'int32');\n      }\n    } else {\n      if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n      if (tf.browser && env.browser) {\n        if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n          pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n        } else {\n          tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n          pixels = tf.browser.fromPixels(tmpCanvas);\n        }\n      } else {\n        const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n        const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n        depth = tempData.data.length / targetWidth / targetHeight;\n        const arr = new Uint8Array(tempData.data.buffer);\n        pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n      }\n    }\n    if (depth === 4) { // rgba to rgb\n      const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n      tf.dispose(pixels);\n      pixels = rgb;\n    }\n    if (!pixels) throw new Error('input error: cannot create tensor');\n    const casted = tf.cast(pixels, 'float32');\n    const tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n    tf.dispose([pixels, casted]);\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw && raw[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch && platformMatch[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      // @ts-ignore getGPGPUContext only exists on WebGL backend\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n    this.webgpu.supported = this.browser && typeof navigator['gpu'] !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      // @ts-ignore navigator.gpu is only defined when webgpu is available in browser\n      if (this.webgpu.supported) this.webgpu.adapter = (await navigator['gpu'].requestAdapter()).name;\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  async updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      // eslint-disable-next-line global-require\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) {\n            cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          }\n          if (line.startsWith('flags')) {\n            cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n          }\n        }\n      } catch { }\n      */\n    }\n    if (!this['cpu']) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this['cpu'] = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\n\nconst options = {\n  cacheModels: false,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nasync function httpHandler(url, init?): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  const modelUrl = join(options.modelBasePath, modelPath || '');\n  const modelPathSegments = modelUrl.split('/');\n  const cachedModelName = 'indexeddb://' + modelPathSegments[modelPathSegments.length - 1].replace('.json', ''); // generate short model name for cache\n  const cachedModels = await tf.io.listModels(); // list all models already in cache\n  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url, init?) => httpHandler(url, init) };\n  const model: GraphModel = new tf.GraphModel(modelCached ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    // @ts-ignore private property\n    if (options.debug) log('model load handler:', model.handler);\n    // @ts-ignore private property\n    const artifacts = await model.handler.load(); // load manifest\n    model.loadSync(artifacts); // load weights\n    if (options.verbose) log('load model:', model['modelUrl']);\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (options.cacheModels && !modelCached) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  return model;\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport type GearType = { age: number, gender: Gender, genderScore: number, race: Array<{ score: number, race: Race }> }\nlet model: GraphModel | null;\nconst last: Array<GearType> = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['gear']);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face['gear']?.skipFrames || 0);\n  const skipTime = (config.face['gear']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face['gear']?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face['gear']?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255,\n  tf1: 1,\n  tf2: 2,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255, 'float32');\n  constants.tf1 = tf.scalar(1, 'float32');\n  constants.tf2 = tf.scalar(2, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: Array<{ age: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet'].enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<{ gender: Gender, genderScore: number }> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function load(config: Config | any) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// eslint-disable-next-line @typescript-eslint/no-explicit-any\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet'].enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],\n  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65],\n  rightEyeIris: [473, 474, 475, 476, 477],\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations['midwayBetweenEyes'][0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const MESH_TO_IRIS_INDICES_MAP: Array<{ key: string, indices: number[] }> = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] },\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] },\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] },\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },\n  // { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] },\n  // { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] },\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: Array<number> = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: Array<number> = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: Array<number> = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: Array<number> = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: Array<number> = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: Array<number> = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: Array<number> = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize) {\n  const spec = { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] };\n  const anchors: Array<[number, number]> = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) { // rotateWithOffset is not defined for tfjs-node\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.2;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBounds(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res)) { // are we using tfhub or pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else {\n    t.batch = tf.squeeze(res); // when using tfhub model\n  }\n  tf.dispose(res);\n  t.boxes = decodeBounds(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: Array<DetectBox> = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: Array<string> = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport async function createAnchors() {\n  const anchors: Array<{ x: number, y: number }> = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0] as number;\n  const scores = await t.scores.data();\n  const detected: Array<{ box: Box, boxRaw: Box, score: number }> = [];\n  const minScore = (config.body['detector'] && config.body['detector']['minConfidence']) ? config.body['detector']['minConfidence'] : 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Array<Point>, outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector']['modelPath'] || '') {\n    models.detector = await loadModel(config.body['detector']['modelPath']);\n    const inputs = Object.values(models.detector.modelSignature['inputs']);\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  await detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = Object.values(models.landmarks.modelSignature['inputs']);\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nasync function prepareImage(input: Tensor, size: number): Promise<Tensor> {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: Array<BodyKeypoint>, outputSize: [number, number]): Array<BodyKeypoint> {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nasync function fixKeypoints(keypoints: Array<BodyKeypoint>) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: Array<BodyKeypoint> = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: Array<BodyKeypoint> = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = await prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: Array<ObjectResult> = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: Array<string> = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore) {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore = (await max.data())[0]; // get highest score\n  tf.dispose([reshaped, max]);\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, tf.scalar(width, 'int32'));\n    const y = (await div.data())[0];\n    tf.dispose([mod, div]);\n    return [x, y, newScore];\n  }\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = resT.squeeze();\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = squeeze.unstack(2);\n      tf.dispose(squeeze);\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body?.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Array<Point[]> = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: Array<Array<{ score: number, emotion: Emotion }>> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<Array<{ score: number, emotion: Emotion }>> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: Array<{ score: number, emotion: Emotion }> = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion?.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: Array<number[]> = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face['embedding']?.skipFrames || 0);\n  const skipTime = (config.face['embedding']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: Array<number> = [];\n    if (config.face['embedding']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model?.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations['leftEyeLower0'];\nconst rightOutline = coords.meshAnnotations['rightEyeLower0'];\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates\n// Update the z coordinate to be an average of the original and the new.\nfunction replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.MESH_TO_IRIS_INDICES_MAP.length; i++) {\n    const { key, indices } = coords.MESH_TO_IRIS_INDICES_MAP[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0], newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\n// eslint-disable-next-line class-methods-use-this\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Array<Point> = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\n// eslint-disable-next-line class-methods-use-this\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, config, meshSize) {\n  if (!model) {\n    if (config.debug) log('face mesh iris detection requested, but model is not loaded');\n    return rawCoords;\n  }\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged\n    // So we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ntype DetectBox = { startPoint: Point, endPoint: Point, landmarks: Array<Point>, confidence: number };\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: Array<FaceResult> = [];\n  const newCache: Array<DetectBox> = [];\n  let id = 0;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config?.filter?.equalization) {\n      const equilized = await histogramEqualization(face.tensor as Tensor);\n      tf.dispose(face.tensor);\n      face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      const [contours, confidence, contourCoords] = model.execute(face.tensor as Tensor) as Array<Tensor>; // first returned tensor represents facial contours which are already included in the coordinates.\n      const faceConfidence = await confidence.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      const coordsReshaped = tf.reshape(contourCoords, [-1, 3]);\n      let rawCoords = await coordsReshaped.array();\n      tf.dispose([contourCoords, coordsReshaped, confidence, contours]);\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n      } else {\n        if (config.face.iris?.enabled) rawCoords = await iris.augmentIris(rawCoords, face.tensor, config, inputSize); // augment results with iris\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / inputSize]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        newCache.push(calculatedBox);\n      }\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.mesh?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport type FaceRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nlet model: GraphModel | null;\nconst last: Array<{\n  age: number,\n  gender: Gender,\n  genderScore: number,\n  descriptor: number[],\n}> = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, descriptor: [] };\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj = {\n      age: <number>0,\n      gender: <Gender>'unknown',\n      genderScore: <number>0,\n      descriptor: <number[]>[],\n    };\n\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description?.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const age = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[age - 1] > all[age + 1] ? 10 * age - 100 * all[age - 1] : 10 * age + 100 * all[age + 1]) / 10;\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : <number[]>[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: Array<number> = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: Array<number[]> = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 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x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = (this.model && this.model.inputs && this.model.inputs[0].shape) ? this.model.inputs[0].shape[2] : 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index]);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res;\n  }\n\n  async predict(input, config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as Array<number>;\n    const hands: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }> = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: Array<{ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null>;\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  // eslint-disable-next-line class-methods-use-this\n  calculateLandmarksBoundingBox(landmarks) {\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: Array<{ landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }> = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { thumb, index, middle, ring, pinky } = Finger;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { none, half, full } = FingerCurl;\n// eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: Array<number[]> = [];\n  const slopesYZ: Array<number[]> = [];\n  const fingerCurls: Array<number> = [];\n  const fingerDirections: Array<number> = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: Array<number> = [];\n    const slopeAtYZ: Array<number> = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: Array<{ name: string, confidence: number }> = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: Array<HandResult> = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Array<Point>;\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = new handdetector.HandDetector(handDetectorModel);\n  handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ntype HandDetectResult = {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: Array<HandDetectResult>,\n  hands: Array<HandResult>;\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = Object.values(models[0].modelSignature['inputs']);\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = Object.values(models[1].modelSignature['inputs']);\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Array<Tensor> = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0] || !models[1] || !models[0]?.inputs[0].shape || !models[1]?.inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: Array<number> = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: Array<string> = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: Array<string[]> = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: Array<string[]> = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: Array<string[][]> = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: Array<BodyKeypoint>,\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: Array<BodyKeypoint>): Array<BodyKeypoint> {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input.shape || !input.shape[1] || !input.shape[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt && kpt.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Array<Box>, // unused\n  bodies: Array<BodyResult>;\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = model.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nasync function parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: Array<BodyKeypoint> = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: Array<BodyResult> = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Array<Point[]> = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nasync function parseMultiPose(res, config, image) {\n  const bodies: Array<BodyResult> = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: Array<BodyKeypoint> = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Array<Point[]> = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model || !model?.inputs[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? await parseSinglePose(res, config, input)\n      : await parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: Array<ObjectResult> = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = Object.values(model.modelSignature['inputs']);\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: Array<ObjectResult> = [];\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors\n      const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n      // find boxes and scores output depending on stride\n      const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n      const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself\n      const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n      for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n        for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n          const score = scores[i][j]; // get score for current position\n          if (score > (config.object.minConfidence || 0) && j !== 61) {\n            const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n            const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n            const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / inputSize)); // just grab indexes of features with highest scores\n            const [x, y] = [\n              cx - (scaleBox / strideSize * boxOffset[0]),\n              cy - (scaleBox / strideSize * boxOffset[1]),\n            ];\n            const [w, h] = [\n              cx + (scaleBox / strideSize * boxOffset[2]) - x,\n              cy + (scaleBox / strideSize * boxOffset[3]) - y,\n            ];\n            let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n            boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n            const box = [ // results normalized to input image pixels\n              boxRaw[0] * outputShape[0],\n              boxRaw[1] * outputShape[1],\n              boxRaw[2] * outputShape[0],\n              boxRaw[3] * outputShape[1],\n            ];\n            const result = {\n              id: id++,\n              // strideSize,\n              score: Math.round(100 * score) / 100,\n              class: j + 1,\n              label: labels[j].label as ObjectType,\n              // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n              // centerRaw: [cx, cy],\n              box: box.map((a) => Math.trunc(a)) as Box,\n              boxRaw,\n            };\n            results.push(result);\n          }\n        }\n      }\n    });\n  }\n  // deallocate tensors\n  res.forEach((t) => tf.dispose(t));\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: Array<number> = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const norm = tf.div(resize, constants.tf255);\n    const transpose = norm.transpose([0, 3, 1, 2]);\n    tf.dispose(norm);\n    tf.dispose(resize);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transpose);\n    lastTime = now();\n    tf.dispose(transpose);\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): Array<BodyResult> {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: Array<unknown>; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a, b) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {\n  const [height, width] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: Array<{ keypoints, box: Box, score: number }> = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Array<Tensor> = model.execute(normalized, poseNetOutputs) as Array<Tensor>;\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) as BodyResult[];\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: Array<number> | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data()) as number[];\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  // @ts-ignore browser is not defined in tfjs-node\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\nexport function reset(instance: Human): void {\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(instance: Human): Promise<void> {\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body?.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector']['modelPath']) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body?.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body?.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body?.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && !instance.models.facemesh) instance.models.facemesh = facemesh.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  // @ts-ignore optional model\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object?.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object?.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n  }\n}\n\nexport async function validate(instance: Human): Promise<void> {\n  interface Op { name: string, category: string, op: string }\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  for (const defined of Object.keys(instance.models)) {\n    const model: GraphModel | null = instance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const ops: string[] = [];\n    // @ts-ignore // executor is a private method\n    const executor = model?.executor;\n    if (executor && executor.graph.nodes) {\n      for (const kernel of Object.values(executor.graph.nodes)) {\n        const op = (kernel as Op).op.toLowerCase();\n        if (!ops.includes(op)) ops.push(op);\n      }\n    } else {\n      if (!executor && instance.config.debug) log('model signature not determined:', defined);\n    }\n    const missing: string[] = [];\n    for (const op of ops) {\n      if (!simpleOps.includes(op) // exclude simple ops\n        && !instance.env.kernels.includes(op) // check actual kernel ops\n        && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n        && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n        && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n        missing.push(op);\n      }\n    }\n    // log('model validation ops:', defined, ops);\n    if (instance.config.debug && missing.length > 0) log('model validation failed:', defined, missing);\n  }\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: <null | AnyCanvas>null,\n  gl: <null | WebGL2RenderingContext>null,\n  extensions: <string[]> [],\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false,\n    desynchronized: true,\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions() as string[];\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport async function register(instance: Human): Promise<void> {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && (!config.gl || !config.gl.getParameter(config.gl.VERSION))) {\n    log('error: humangl backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = await image.canvas(100, 100);\n    } catch (err) {\n      log('error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas?.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', async (e) => {\n          log('error: humangl:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('error: humangl context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('error: humangl context create:', e);\n        });\n      }\n    } catch (err) {\n      log('error: cannot get WebGL context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('error: cannot set WebGL context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('error: cannot register WebGL backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('error: cannot update WebGL backend registration:', err);\n      return;\n    }\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);\n    } else {\n      log('error: no current gl context:', current, config.gl);\n      return;\n    }\n    try {\n      tf.ENV.set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    log('backend registered:', config.name);\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps() {\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.floorDiv(op.inputs.a / op.inputs.b) * op.inputs.b + tf.mod(op.inputs.a, op.inputs.b)),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('floormod');\n  }\n}\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'humangl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator['gpu'] === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'humangl';\n        } else {\n          const adapter = await navigator['gpu'].requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n        }\n      }\n\n      // check available backends\n      if (instance.config.backend === 'humangl') await humangl.register(instance);\n      const available = Object.keys(tf.engine().registryFactory);\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', instance.config.backend);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf?.setWasmPaths !== 'undefined') await tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        const simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n        const mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n        if (instance.config.debug) log(`wasm execution: ${simd ? 'SIMD' : 'no SIMD'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n        if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n        constants.init();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl') {\n      tf.ENV.set('CHECK_COMPUTATION_FOR_ERRORS', false);\n      tf.ENV.set('WEBGL_CPU_FORWARD', true);\n      // tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', false);\n      tf.ENV.set('WEBGL_USE_SHAPES_UNIFORMS', true);\n      tf.ENV.set('CPU_HANDOFF_SIZE_THRESHOLD', 256);\n      // if (!instance.config.object.enabled) tf.ENV.set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (typeof instance.config['deallocate'] !== 'undefined' && instance.config['deallocate']) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.ENV.set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n      if (tf.backend().getGPGPUContext) {\n        const gl = await tf.backend().getGPGPUContext().gl;\n        if (instance.config.debug) log(`gl version:${gl.getParameter(gl.VERSION)} renderer:${gl.getParameter(gl.RENDERER)}`);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // tf.ENV.set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // tf.ENV.set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // tf.ENV.set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    // wait for ready\n    tf.enableProdMode();\n    await tf.ready();\n\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps();\n    // await env.updateBackend(); // update env on backend init\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: Array<string>, config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep, now, log } from './util';\nimport { env } from './env';\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Point } from '../result';\nimport type { AnyCanvas } from '../exports';\n\n/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport type DrawOptions = {\n  /** draw line color */\n  color: string,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: <string>'rgba(173, 216, 230, 0.6)', // 'lightblue' with light alpha channel\n  labelColor: <string>'rgba(173, 216, 230, 1)', // 'lightblue' with dark alpha channel\n  shadowColor: <string>'black',\n  font: <string>'small-caps 16px \"Segoe UI\"',\n  lineHeight: <number>18,\n  lineWidth: <number>4,\n  pointSize: <number>2,\n  roundRect: <number>8,\n  drawPoints: <boolean>false,\n  drawLabels: <boolean>true,\n  drawBoxes: <boolean>true,\n  drawGestures: <boolean>true,\n  drawPolygons: <boolean>true,\n  drawGaze: <boolean>true,\n  fillPolygons: <boolean>false,\n  useDepth: <boolean>true,\n  useCurves: <boolean>false,\n};\n\nlet drawTime = 0;\n\nconst getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nconst rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nfunction point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  z = z || 0;\n  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nfunction rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nfunction lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    const z = pt[2] || 0;\n    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.3)` : localOptions.color;\n    ctx.lineTo(pt[0], Math.round(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nfunction arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n\n/** draw detected gestures */\nexport async function gesture(inCanvas: AnyCanvas, result: Array<GestureResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n\n/** draw detected faces */\nexport async function face(inCanvas: AnyCanvas, result: Array<FaceResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  for (const f of result) {\n    ctx.font = localOptions.font;\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    if (localOptions.drawBoxes) rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n    if (localOptions.drawLabels) {\n      // silly hack since fillText does not suport new line\n      const labels:string[] = [];\n      labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n      if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n      if (f.age) labels.push(`age: ${f.age || ''}`);\n      if (f.iris) labels.push(`distance: ${f.iris}`);\n      if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n      if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n      if (f.emotion && f.emotion.length > 0) {\n        const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n        if (emotion.length > 3) emotion.length = 3;\n        labels.push(emotion.join(' '));\n      }\n      if (f.rotation && f.rotation.angle && f.rotation.gaze) {\n        if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n        if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n      }\n      if (labels.length === 0) labels.push('face');\n      ctx.fillStyle = localOptions.color;\n      for (let i = labels.length - 1; i >= 0; i--) {\n        const x = Math.max(f.box[0], 0);\n        const y = i * localOptions.lineHeight + f.box[1];\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(labels[i], x + 5, y + 16);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(labels[i], x + 4, y + 15);\n      }\n    }\n    // ctx.lineWidth = localOptions.lineWidth;\n    ctx.lineWidth = 2;\n    if (f.mesh && f.mesh.length > 0) {\n      if (localOptions.drawPoints) {\n        for (const pt of f.mesh) point(ctx, pt[0], pt[1], pt[2], localOptions);\n      }\n      if (localOptions.drawPolygons) {\n        if (f.mesh.length > 450) {\n          for (let i = 0; i < triangulation.length / 3; i++) {\n            const points = [\n              triangulation[i * 3 + 0],\n              triangulation[i * 3 + 1],\n              triangulation[i * 3 + 2],\n            ].map((index) => f.mesh[index]);\n            lines(ctx, points, localOptions);\n          }\n        }\n        // iris: array[center, left, top, right, bottom]\n        if (f.annotations && f.annotations['leftEyeIris'] && f.annotations['leftEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['leftEyeIris'][3][0] - f.annotations['leftEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['leftEyeIris'][4][1] - f.annotations['leftEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (f.annotations && f.annotations['rightEyeIris'] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n          ctx.beginPath();\n          const sizeX = Math.abs(f.annotations['rightEyeIris'][3][0] - f.annotations['rightEyeIris'][1][0]) / 2;\n          const sizeY = Math.abs(f.annotations['rightEyeIris'][4][1] - f.annotations['rightEyeIris'][2][1]) / 2;\n          ctx.ellipse(f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n          ctx.stroke();\n          if (localOptions.fillPolygons) {\n            ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n            ctx.fill();\n          }\n        }\n        if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n          ctx.strokeStyle = 'pink';\n          const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n          const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n          const pathV = new Path2D(`\n            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n            C\n              ${valX} ${f.box[1]},\n              ${valX} ${f.box[1] + f.box[3]},\n              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n          `);\n          const pathH = new Path2D(`\n            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n            C \n              ${f.box[0]} ${valY},\n              ${f.box[0] + f.box[2]} ${valY},\n              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n          `);\n          ctx.stroke(pathH);\n          ctx.stroke(pathV);\n        }\n        if (localOptions.drawGaze && f.rotation?.gaze?.strength && f.rotation?.gaze?.bearing && f.annotations['leftEyeIris'] && f.annotations['rightEyeIris'] && f.annotations['leftEyeIris'][0] && f.annotations['rightEyeIris'][0]) {\n          ctx.strokeStyle = 'pink';\n          ctx.fillStyle = 'pink';\n          const leftGaze = [\n            f.annotations['leftEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['leftEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['leftEyeIris'][0][0], f.annotations['leftEyeIris'][0][1]], [leftGaze[0], leftGaze[1]], 4);\n          const rightGaze = [\n            f.annotations['rightEyeIris'][0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n            f.annotations['rightEyeIris'][0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n          ];\n          arrow(ctx, [f.annotations['rightEyeIris'][0][0], f.annotations['rightEyeIris'][0][1]], [rightGaze[0], rightGaze[1]], 4);\n        }\n      }\n    }\n  }\n}\n\n/** draw detected bodies */\nexport async function body(inCanvas: AnyCanvas, result: Array<BodyResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box?.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt].position[2] ? `rgba(${127.5 + (2 * (result[i].keypoints[pt].position[2] || 0))}, ${127.5 - (2 * (result[i].keypoints[pt].position[2] || 0))}, 255, 0.5)` : localOptions.color;\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = localOptions.useDepth && pt.position[2] ? `rgba(${127.5 + (2 * pt.position[2])}, ${127.5 - (2 * pt.position[2])}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n\n/** draw detected hands */\nexport async function hand(inCanvas: AnyCanvas, result: Array<HandResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * (pt[2] || 0))}, ${127.5 - (2 * (pt[2] || 0))}, 255, 0.5)` : localOptions.color;\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Array<Point>, title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || 0;\n        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + (2 * z)}, ${127.5 - (2 * z)}, 255, 0.5)` : localOptions.color;\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations['index'], 'index');\n      addHandLabel(h.annotations['middle'], 'middle');\n      addHandLabel(h.annotations['ring'], 'ring');\n      addHandLabel(h.annotations['pinky'], 'pinky');\n      addHandLabel(h.annotations['thumb'], 'thumb');\n      addHandLabel(h.annotations['palm'], 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Array<Point>) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + (i * z)}, ${127.5 - (i * z)}, 255, 0.5)` : localOptions.color;\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations['index']);\n      addHandLine(h.annotations['middle']);\n      addHandLine(h.annotations['ring']);\n      addHandLine(h.annotations['pinky']);\n      addHandLine(h.annotations['thumb']);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n\n/** draw detected objects */\nexport async function object(inCanvas: AnyCanvas, result: Array<ObjectResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw combined person results instead of individual detection result objects */\nexport async function person(inCanvas: AnyCanvas, result: Array<PersonResult>, drawOptions?: Partial<DrawOptions>) {\n  const localOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport async function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result || !result.performance || !result || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: Array<{ x: number, y: number }>): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: Array<{ x: number, y: number }> = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations['rightEyeIris'] || !face.annotations['leftEyeIris']) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    // eslint-disable-next-line no-unused-vars, @typescript-eslint/no-unused-vars\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (isNaN(thetaX)) thetaX = 0;\n    if (isNaN(thetaY)) thetaY = 0;\n    if (isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const y_axis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let x_axis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const z_axis = normalize(crossVectors(x_axis, y_axis));\n  // adjust x_axis to make sure that all axes are perpendicular to each other\n  x_axis = crossVectors(y_axis, z_axis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    x_axis[0], x_axis[1], x_axis[2],\n    y_axis[0], y_axis[1], y_axis[2],\n    z_axis[0], z_axis[1], z_axis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ntype DescRes = { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] };\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  // eslint-disable-next-line no-async-promise-executor\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: Array<FaceResult> = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      faces[i].tensor = masked as Tensor;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face['gear']?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face['gear']?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = instance.config.face.description?.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = instance.config.face.description?.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face['gear']?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if embedding model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes)?.age) res.age = (descRes as DescRes).age as number;\n    if ((descRes as DescRes)?.gender) res.gender = (descRes as DescRes).gender as Gender;\n    if ((descRes as DescRes)?.genderScore) res.genderScore = (descRes as DescRes)?.genderScore as number;\n    if ((descRes as DescRes)?.descriptor) res.embedding = (descRes as DescRes)?.descriptor as Array<number>;\n    if ((descRes as DescRes)?.race) res.race = (descRes as DescRes)?.race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as Array<{ score: number, emotion: Emotion }>;\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ body: number, gesture: BodyGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ face: number, gesture: FaceGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ iris: number, gesture: IrisGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations || !res[i].annotations.leftEyeIris || !res[i].annotations.leftEyeIris[0] || !res[i].annotations.rightEyeIris || !res[i].annotations.rightEyeIris[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: Array<{ hand: number, gesture: HandGesture }> = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: Array<{ name: string, position: Point }> = [];\n    if (res[i]['annotations']) {\n      for (const [finger, pos] of Object.entries(res[i]['annotations'])) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i]['keypoints']) {\n      const poses = fingerPose.match(res[i]['keypoints']);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, GestureResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n  // curve fitted: buffer = 8 - ln(delay)\n  // interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n  // - at  50ms delay buffer = ~4.1 => 28% towards live data\n  // - at 250ms delay buffer = ~2.5 => 40% towards live data\n  // - at 500ms delay buffer = ~1.8 => 55% towards live data\n  // - at 750ms delay buffer = ~1.4 => 71% towards live data\n  // - at  1sec delay buffer = 1 which means live data is used\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body as BodyResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part as BodyLandmark,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as Array<{ score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }>;\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body?.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body?.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body?.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Array<Point[]> = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations as BodyResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand as HandResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face as FaceResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.bearing || 0) + (newResult.face[i].rotation?.gaze?.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze?.strength || 0) + (newResult.face[i].rotation?.gaze?.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      }\n      bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object as ObjectResult[])); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons as PersonResult[]));\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture as GestureResult[];\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = Array<number>\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see {@link similarity}\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - {@link distance} calculated `distance` of given descriptor to the best match\n * - {@link similarity} calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Array<Descriptor>, options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0 || descriptor.length !== descriptors[0].length) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = distance(descriptor, descriptors[i], options);\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: Array<FaceResult>, bodies: Array<BodyResult>, hands: Array<HandResult>, gestures: Array<GestureResult>, shape: Array<number> | undefined): Array<PersonResult> {\n  let id = 0;\n  const persons: Array<PersonResult> = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures?.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.left?.id) person.gestures?.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands?.right?.id) person.gestures?.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face?.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands?.left?.box);\n    extractXY(person.hands?.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape && shape[1] && shape[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = `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\nWmlQ6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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport type { Human } from './human';\nimport type { Tensor } from './tfjs/types';\nimport { env } from './util/env';\n\nasync function warmupBitmap(instance: Human) {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob;\n  let res;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = null;\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = await instance.detect(tensor.tensor as Tensor, instance.config);\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res;\n  if ('node' in tf) {\n    // @ts-ignore tf.node may be undefined\n    const data = tf['node'].decodeJpeg(img);\n    const expanded = data.expandDims(0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  return res;\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  let res;\n  return new Promise(async (resolve) => {\n    if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n    else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n    else res = await warmupNode(instance);\n    const t1 = now();\n    if (instance.config.debug) log('Warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './util/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API]: {@link https://js.tensorflow.org/api/latest/}\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'humangl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options as DrawOptions,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    // @ts-ignore eslint-typescript cannot correctly infer type in anonymous function\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // include platform info\n    this.emit('create');\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    return validate(defaults, userConfig || this.config);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number {\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  // eslint-disable-next-line class-methods-use-this\n  enhance(input: Tensor): Tensor | null {\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n  }\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (this.config.debug) log(`version: ${this.version}`);\n      if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n      if (!await backend.check(this)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('environment:', this.env);\n        if (this.config.debug) log('tf flags:', this.tf.ENV['flags']);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      await models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels as number || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events && this.events.dispatchEvent) this.events?.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config) as Result;\n  }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<Record<string, number>> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n    }\n    const kernelArr: Array<{ name: string, ms: number }> = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] as unknown as number })); // convert to array\n    kernelArr.sort((a, b) => b.ms - a.ms); // sort\n    kernelArr.length = 20; // crop\n    const res: Record<string, number> = {};\n    for (const kernel of kernelArr) res[kernel.name] = kernel.ms; // create perf objects\n    return res;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // configure backend if needed\n      await backend.check(this);\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames as number)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n        else if (this.performance.gesture) delete this.performance.gesture;\n      }\n\n      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process?.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default };\n"],
+  "mappings": 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 }
diff --git a/dist/human.js b/dist/human.js
index 7a2cedd7..67f9d780 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -4,65 +4,66 @@
   author: <https://github.com/vladmandic>'
 */
 
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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:r},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:r}=this.initializeBackend(e);if(!(r?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new NR(this.backendInstance),!0}setupRegisteredKernels(){Na(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Na(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let r=t.factory();if(r&&!(r instanceof Cu)&&typeof r.then=="function"){let n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,bs(`Initialization of backend ${e} failed`),bs(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return bs(`Initialization of backend ${e} failed`),bs(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}let n;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,r){e();try{let n=r();return t(),n}catch(n){throw t(),n}}nextTensorId(){return q1.nextTensorId++}nextVariableId(){return q1.nextVariableId++}clone(e){let t=B.runKernel(ii,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(Hs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[t],n,a,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,of(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){let n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=M1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(M1(e)){let{kernelName:c,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=of(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:C,dtype:S}=b;return this.makeTensorFromDataId(v,C,S)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=M1(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,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(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=j1(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&vs(e[0])&&(a=e.map(o=>rh(o)));let s=n.write(a,t,r),i=new rt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=Yv(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new rt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Tp(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)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++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*G1(e.dtype)),this.state.numBytes+=r,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:r})),e instanceof Tp||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;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let r=e.size*G1(e.dtype);this.state.numBytes-=r}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let 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,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,r=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,n,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:a},o=j1(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=r[d],p=Pf(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),n(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Yy(e),r=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof rt,()=>"The result y returned by f() must be a tensor.");let s=MR(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");return this.tidy("backward",()=>{let i={};i[a.id]=r==null?UR(a.shape):r,FR(i,s,l=>this.tidy(l),GR);let o=t.map(l=>i[l.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(l=>{for(let u of l.saved)u.dispose()}),this.state.activeTape=null),{value:a,grads:o}})}customGrad(e){return P(Cs(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{P(t.every(i=>i instanceof rt),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");let r,n={};t.forEach((i,o)=>{n[o]=i});let a=(i,o)=>(r=e(...t,o),P(r.value instanceof rt,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),P(Cs(r.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),r.value),s=(i,o)=>{let l=r.gradFunc(i,o),u=Array.isArray(l)?l:[l];P(u.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(...)."),P(u.every(h=>h instanceof rt),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");let d={};return u.forEach((h,p)=>{d[p]=()=>h}),d};return this.runKernelFunc({forwardFunc:a,backwardsFunc:s,inputs:n})}}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){let t=Cp(),r=await this.backend.time(e);return r.wallMs=Cp()-t,r}track(e){return this.state.activeScope!=null&&(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 c3;for(let e in 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+`;return p[p.length-1]=" "+p[p.length-1]+"]"+(s?"":f),p}function lp(e){let t=[];for(let r=0;r<e.length;r+=2)t.push([e[r],e[r+1]]);return t}var rr=class{constructor(e,t,r){if(this.dtype=t,this.shape=e.slice(),this.size=Ct(e),r!=null){let n=r.length;P(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=r||Kv(t,this.size),this.strides=Cu(e)}set(e,...t){t.length===0&&(t=[0]),P(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let r=this.locToIndex(t);this.values[r]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let a=`Requested out of range element at ${e}.   Buffer shape=${this.shape}`;throw new Error(a)}t++}let r=e[e.length-1];for(let n=0;n<e.length-1;++n)r+=this.strides[n]*e[n];return this.values[r]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let r=0;r<e.length-1;++r)t+=this.strides[r]*e[r];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let r=0;r<t.length-1;++r)t[r]=Math.floor(e/this.strides[r]),e-=t[r]*this.strides[r];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return ra().makeTensor(this.values,this.shape,this.dtype)}},ra=null,ru=null,_R=null;function zR(e){ra=e}function OR(e){ru=e}function DR(e){_R=e}var rt=class{constructor(e,t,r,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Ct(e),this.strides=Cu(e),this.dataId=r,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return ru.buffer(this.shape,this.dtype,e)}bufferSync(){return ru.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return iu(this.shape,e,this.dtype==="complex64")}arraySync(){return iu(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=ra().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(r=>of(r))}catch(r){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),ra().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=ra().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>of(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await ra().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(ra().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return ru.print(this,e)}clone(){return this.throwIfDisposed(),ru.clone(this)}toString(e=!1){let t=this.dataSync();return $R(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),ru.cast(this,e)}variable(e=!0,t,r){return this.throwIfDisposed(),ra().makeVariable(this,e,t,r)}};Object.defineProperty(rt,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function LR(){return Ky("Tensor",()=>rt)}LR();var Cp=class extends rt{constructor(e,t,r,n){super(e.shape,e.dtype,e.dataId,n);this.trainable=t,this.name=r}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!Ws(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);ra().disposeTensor(this),this.dataId=e.dataId,ra().incRef(this,null)}dispose(){ra().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Cp,Symbol.hasInstance,{value:e=>e instanceof rt&&e.assign!=null&&e.assign instanceof Function});var ia={};Le(ia,{assertTypesMatch:()=>fw,getTensorsInContainer:()=>Zy,isTensorInList:()=>WR,makeTypesMatch:()=>Ot});var uw=(e=>(e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6",e))(uw||{}),dw=(e=>(e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64",e))(dw||{}),pw=(e=>(e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64",e))(pw||{}),hw=(e=>(e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64",e))(hw||{}),cw=(e=>(e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64",e))(cw||{}),BR={float32:hw,int32:dw,bool:pw,complex64:cw};function Dr(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return BR[e][t]}function rh(e){return Dr(e,"int32")}function Ot(e,t){if(e.dtype===t.dtype)return[e,t];let r=Dr(e.dtype,t.dtype);return[e.cast(r),t.cast(r)]}function fw(e,t){P(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function WR(e,t){return t.some(r=>r.id===e.id)}function Zy(e){let t=[];return mw(e,t,new Set),t}function mw(e,t,r){if(e==null)return;if(e instanceof rt){t.push(e);return}if(!VR(e))return;let n=e;for(let a in n){let s=n[a];r.has(s)||(r.add(s),mw(s,t,r))}}function VR(e){return Array.isArray(e)||typeof e=="object"}function R1(e){return e.kernelName!=null}var h3=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},H1=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new h3}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t];if(await this.initializeBackend(r).success){await this.setBackend(r);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,r=1){return e in this.registryFactory?(bs(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:r},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:r}=this.initializeBackend(e);if(!(r?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new NR(this.backendInstance),!0}setupRegisteredKernels(){Na(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Na(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let r=t.factory();if(r&&!(r instanceof Su)&&typeof r.then=="function"){let n=++this.pendingBackendInitId,a=r.then(s=>n<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,bs(`Initialization of backend ${e} failed`),bs(s.stack||s.message)),!1));return this.pendingBackendInit=a,{success:a,asyncInit:!0}}else return this.registry[e]=r,{success:!0,asyncInit:!1}}catch(r){return bs(`Initialization of backend ${e} failed`),bs(r.stack||r.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let r=e[t],{success:n,asyncInit:a}=this.initializeBackend(r);if(a||n)return{name:r,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let r=this.state.tensorInfo.get(t),n=r.backend,a=this.readSync(t),s=n.refCount(t);n.disposeData(t,!0),r.backend=e,e.move(t,a,r.shape,r.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let r=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}let n;return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,r){e();try{let n=r();return t(),n}catch(n){throw t(),n}}nextTensorId(){return H1.nextTensorId++}nextVariableId(){return H1.nextVariableId++}clone(e){let t=B.runKernel(ii,{x:e}),r={x:e},n=s=>({x:()=>{let i="float32",o={x:s},l={dtype:i};return B.runKernel(Hs,o,l)}}),a=[];return this.addTapeNode(this.state.activeScope.name,r,[t],n,a,{}),t}runKernel(e,t,r){if(this.backendName==null&&this.backend,sf(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:r})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,r){let n=this.backend.numDataIds(),a=0;r.forEach(o=>{a+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-a-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,r=[],n=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,l=R1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(R1(e)){let{kernelName:c,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=sf(c,this.backendName);P(g!=null,()=>`Cannot find registered kernel '${c}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let A=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(c,y,A);let x=A.map(b=>{if(b.rank!=null)return b;let{dataId:v,shape:C,dtype:S}=b;return this.makeTensorFromDataId(v,C,S)});if(n){let b=this.getTensorsForGradient(c,f,x);r=this.saveTensorsForBackwardMode(b)}return x}}else{let{forwardFunc:c}=e,f=m=>{!n||(r=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>c(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:d}=e,h=R1(e)?null:e.backwardsFunc,p;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(p=this.profiler.profileKernel(l,u,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),n&&this.addTapeNode(l,u,t,h,r,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(u).map(c=>u[c]!=null?u[c].shape:null),outputShapes:t.map(c=>c.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,r){let n=G1(e);if(n!=null){let a=n.inputsToSave||[],s=n.outputsToSave||[],i;n.saveAllInputs?(P(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(l=>t[l])):i=a.map(l=>t[l]);let o=r.filter((l,u)=>s[u]);return i.concat(o)}return[]}makeTensor(e,t,r,n){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");r=r||"float32",n=n||this.backend;let a=e;r==="string"&&vs(e[0])&&(a=e.map(o=>th(o)));let s=n.write(a,t,r),i=new rt(t,r,s,this.nextTensorId());if(this.trackTensor(i,n),r==="string"){let o=this.state.tensorInfo.get(s),l=Yv(a);this.state.numBytes+=l-o.bytes,o.bytes=l}return i}makeTensorFromDataId(e,t,r,n){r=r||"float32";let a=new rt(t,r,e,this.nextTensorId());return this.trackTensor(a,n),a}makeVariable(e,t=!0,r,n){r=r||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let a=new Cp(e,t,r,this.nextTensorId());if(this.state.registeredVariables[a.name]!=null)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++,e.dtype==="string"&&this.state.numStringTensors++;let r=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(r=e.size*U1(e.dtype)),this.state.numBytes+=r,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:r})),e instanceof Cp||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;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let r=e.size*U1(e.dtype);this.state.numBytes-=r}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let 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,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,r=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(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-r;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,r,n,a,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:r,saved:a},o=G1(e);o!=null&&(n=o.gradFunc),n!=null&&(i.gradient=l=>(l=l.map((u,d)=>{if(u==null){let h=r[d],p=$f(h.size,h.dtype);return this.makeTensor(p,h.shape,h.dtype)}return u}),n(l.length>1?l:l[0],a,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Zy(e),r=new Set(t.map(a=>a.id));for(let a=0;a<this.state.activeScope.track.length;a++){let s=this.state.activeScope.track[a];!s.kept&&!r.has(s.id)&&s.dispose()}let n=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(a=>{!a.kept&&a.scopeId===n.id&&this.track(a)})}gradients(e,t,r,n=!1){if(P(t.length>0,()=>"gradients() received an empty list of xs."),r!=null&&r.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${r.dtype}'`);let a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));P(a instanceof rt,()=>"The result y returned by f() must be a tensor.");let s=MR(this.state.activeTape,t,a);if(!n&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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+          ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(Xp,i)}var JO=W({sparseSegmentMean_:YO});function QO(e,t,r){let n=F(e,"data","sparseSegmentSum"),a=F(t,"indices","sparseSegmentSum","int32"),s=F(r,"segmentIds","sparseSegmentSum","int32");if(n.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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-         ${s.shape}`);let i={data:n,indices:a,segmentIds:s};return B.runKernel(Yp,i)}var eD=W({sparseSegmentSum_:QO});function tD(e,t,r,n,a,s,i,o){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let u=F(t,"dataSplits","stringNGrams");if(u.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let d={separator:r,nGramWidths:n,leftPad:a,rightPad:s,padWidth:i,preserveShortSequences:o},h={data:l,dataSplits:u},p=B.runKernel(Qp,h,d);return{nGrams:p[0],nGramsSplits:p[1]}}var rD=W({stringNGrams_:tD});function nD(e,t,r=!0){let n=F(e,"input","stringSplit","string"),a=F(t,"delimiter","stringSplit","string");if(n.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${n.shape}`);if(a.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${a.shape}`);let s={skipEmpty:r},i={input:n,delimiter:a},o=B.runKernel(nm,i,s);return{indices:o[0],values:o[1],shape:o[2]}}var aD=W({stringSplit_:nD});function sD(e,t){let r=F(e,"input","stringToHashBucketFast","string"),n={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let a={input:r};return B.runKernel(am,a,n)}var iD=W({stringToHashBucketFast_:sD}),oD={fft:vm,ifft:Mp,rfft:wm,irfft:V2},lD={hammingWindow:zz,hannWindow:t7,frame:r7,stft:Bz},Ie={flipLeftRight:Gz,grayscaleToRGB:Hz,resizeNearestNeighbor:gO,resizeBilinear:fO,rotateWithOffset:Kz,cropAndResize:Vz,nonMaxSuppression:Zz,nonMaxSuppressionAsync:aO,nonMaxSuppressionWithScore:iO,nonMaxSuppressionWithScoreAsync:lO,nonMaxSuppressionPadded:dO,nonMaxSuppressionPaddedAsync:hO,threshold:xO,transform:vO},o7={bandPart:kO,gramSchmidt:SO,qr:TO},uD={absoluteDifference:RO,computeWeightedLoss:Za,cosineDistance:FO,hingeLoss:PO,huberLoss:zO,logLoss:DO,meanSquaredError:BO,sigmoidCrossEntropy:UO,softmaxCrossEntropy:HO},dp={sparseFillEmptyRows:KO,sparseReshape:ZO,sparseSegmentMean:JO,sparseSegmentSum:eD},Hc={stringNGrams:rD,stringSplit:aD,stringToHashBucketFast:iD},Ya=class extends Kw{minimize(e,t=!1,r){let{value:n,grads:a}=this.computeGradients(e,r);if(r!=null){let s=r.map(i=>({name:i.name,tensor:a[i.name]}));this.applyGradients(s)}else this.applyGradients(a);return re(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ek(e,t)}dispose(){this.iterations_!=null&&re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ya,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Nm=class extends Ya{constructor(e,t,r=null){super();this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(yt(s),1-this.rho)),u=L(pe(Cr(le(o,this.epsilon)),Cr(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(yt(u),1-this.rho));i.assign(l),o.assign(d);let h=le(L(u,-this.learningRate),n);n.assign(h)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(re(this.accumulatedGrads.map(e=>e.variable)),re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,r=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Nm.className="Adadelta";_i(Nm);var Em=class extends Ya{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>nd(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,yt(a));s.assign(i);let o=le(L(pe(a,Cr(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(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);let t=!1;this.accumulatedGrads=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Em.className="Adagrad";_i(Em);var Rm=class extends Ya{constructor(e,t,r,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Se(t).variable(),this.accBeta2=Se(r).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=he(1,this.accBeta2);t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:K(()=>at(i).variable(o))}),this.accumulatedSecondMoment[s]==null&&(this.accumulatedSecondMoment[s]={originalName:`${a}/v`,variable:K(()=>at(i).variable(o))});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedSecondMoment[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=le(L(d,this.beta2),L(yt(l),1-this.beta2)),c=pe(h,r),f=pe(p,n);u.assign(h),d.assign(p);let m=le(L(pe(c,le(Cr(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(Ps(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ps(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}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)}};Rm.className="Adam";_i(Rm);var Mm=class extends Ya{constructor(e,t,r,n=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let i=B.registeredVariables[a],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${a}/m`,variable:at(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${a}/v`,variable:at(i).variable(o)});let l=Array.isArray(e)?e[s].tensor:e[a];if(l==null)return;let u=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,h=le(L(u,this.beta1),L(l,1-this.beta1)),p=L(d,this.beta2),c=er(l),f=Xa(p,c);u.assign(h),d.assign(f);let m=le(L(pe(n,r),pe(h,le(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&re(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)}};Mm.className="Adamax";_i(Mm);var ch=class extends Ya{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=hr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(r=>({originalName:r.name,variable:r.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Fm.className="Momentum";_i(Fm);var $m=class extends Ya{constructor(e,t=.9,r=0,n=null,a=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=r,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,n==null&&(this.epsilon=B.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedMeanSquares[r]==null&&(this.accumulatedMeanSquares[r]={originalName:`${t}/rms`,variable:K(()=>at(n).variable(a))}),this.accumulatedMoments[r]==null&&(this.accumulatedMoments[r]={originalName:`${t}/momentum`,variable:K(()=>at(n).variable(a))}),this.accumulatedMeanGrads[r]==null&&this.centered&&(this.accumulatedMeanGrads[r]={originalName:`${t}/mg`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[r].variable,o=this.accumulatedMoments[r].variable;K(()=>{let l=le(L(i,this.decay),L(yt(s),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[r].variable,d=le(L(u,this.decay),L(s,1-this.decay)),h=pe(L(s,this.learningRate),Cr(he(l,le(yt(d),this.epsilon)))),p=le(L(o,this.momentum),h);i.assign(l),u.assign(d),o.assign(p);let c=he(n,p);n.assign(c)}else{let u=le(L(i,this.decay),L(yt(s),1-this.decay)),d=le(L(o,this.momentum),pe(L(s,this.learningRate),Cr(le(u,this.epsilon))));i.assign(u),o.assign(d);let h=he(n,d);n.assign(h)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,r=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})))}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)}};$m.className="RMSProp";_i($m);var As=class{static sgd(e){return new ch(e)}static momentum(e,t,r=!1){return new Fm(e,t,r)}static rmsprop(e,t=.9,r=0,n=null,a=!1){return new $m(e,t,r,n,a)}static adam(e=.001,t=.9,r=.999,n=null){return new Rm(e,t,r,n)}static adadelta(e=.001,t=.95,r=null){return new Nm(e,t,r)}static adamax(e=.002,t=.9,r=.999,n=null,a=0){return new Mm(e,t,r,n,a)}static adagrad(e,t=.1){return new Em(e,t)}},so={sgd:As.sgd,momentum:As.momentum,adadelta:As.adadelta,adagrad:As.adagrad,rmsprop:As.rmsprop,adamax:As.adamax,adam:As.adam},dD=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function l7(){return new Promise(e=>dD(()=>e()))}var 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pD(e,t){let r=e[0].length;e.forEach((a,s)=>{P(a.length===r,()=>`Error in concat${r}D: rank of tensors[${s}] must be the same as the rank of the rest (${r})`)}),P(t>=0&&t<r,()=>`Error in concat${r}D: axis must be between 0 and ${r-1}.`);let n=e[0];e.forEach((a,s)=>{for(let i=0;i<r;i++)P(i===t||a[i]===n[i],()=>`Error in concat${r}D: Shape of tensors[${s}] (${a}) does not match the shape of the rest (${n}) along the non-concatenated axis ${s}.`)})}function hD(e,t){let r=e[0].slice();for(let n=1;n<e.length;n++)r[t]+=e[n][t];return r}var Z2=30;function cD(e){return e<=Z2?e:nf(e,Math.floor(Math.sqrt(e)))}function fD(e,t,r){let n=r*(typeof e=="number"?e:e[0]),a=t*(typeof e=="number"?e:e[1]);return[n,a]}function mD(e,t,r,n=!0){let a=[];if(n)a=a.concat(t.slice(0)),a.push(e[0]/r),a=a.concat(e.slice(1));else{a=a.concat(e[0]);let s=t.length;for(let i=0;i<s;++i)a=a.concat([e[i+1]/t[i],t[i]]);a=a.concat(e.slice(s+1))}return a}function gD(e,t,r=!0){let n=[];if(r){n.push(t);for(let 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this.applyGradients(a);return re(a),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Ek(e,t)}dispose(){this.iterations_!=null&&re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Se(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(Ya,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Tm=class extends Ya{constructor(e,t,r=null){super();this.learningRate=e,this.rho=t,this.epsilon=r,this.accumulatedGrads=[],this.accumulatedUpdates=[],r==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t],a=!1;this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accum_grad`,variable:K(()=>at(n).variable(a))}),this.accumulatedUpdates[r]==null&&(this.accumulatedUpdates[r]={originalName:`${t}/accum_var`,variable:K(()=>at(n).variable(a))});let s=Array.isArray(e)?e[r].tensor:e[t];if(s==null)return;let i=this.accumulatedGrads[r].variable,o=this.accumulatedUpdates[r].variable;K(()=>{let l=le(L(i,this.rho),L(yt(s),1-this.rho)),u=L(pe(Cr(le(o,this.epsilon)),Cr(le(i,this.epsilon))),s),d=le(L(o,this.rho),L(yt(u),1-this.rho));i.assign(l),o.assign(d);let 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Ya{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=B.registeredVariables[t];this.accumulatedGrads[r]==null&&(this.accumulatedGrads[r]={originalName:`${t}/accumulator`,variable:K(()=>rd(n.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[r].tensor:e[t];if(a==null)return;let s=this.accumulatedGrads[r].variable;K(()=>{let i=le(s,yt(a));s.assign(i);let o=le(L(pe(a,Cr(le(i,B.backend.epsilon()))),-this.learningRate),n);n.assign(o)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&re(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);let 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m=le(L(pe(c,le(Cr(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign(Ps(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ps(this.beta2,this.iterations_+1))});let t=e.length/2,r=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(r)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(r)}))}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)}};Em.className="Adam";_i(Em);var Rm=class extends Ya{constructor(e,t,r,n=null,a=0){super();this.learningRate=e,this.beta1=t,this.beta2=r,this.epsilon=n,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Se(0).variable(),this.accBeta1=Se(t).variable()}),n==null&&(this.epsilon=B.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(r=>r.name):Object.keys(e);K(()=>{let r=he(1,this.accBeta1),n=pe(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((a,s)=>{let 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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)}};Rm.className="Adamax";_i(Rm);var hh=class extends Ya{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,r)=>{let n=Array.isArray(e)?e[r].tensor:e[t];if(n==null)return;let a=B.registeredVariables[t];K(()=>{let s=le(L(this.c,n),a);a.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=hr(Se(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},YW=0,st=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=YW++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let r=this.getClassName();t=Ba(r)+"_"+Um(r)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let r;if(e.batchInputShape!=null)r=e.batchInputShape;else if(e.inputShape!=null){let a=null;e.batchSize!=null&&(a=e.batchSize),r=[a].concat(e.inputShape)}this.batchInputShape=r;let n=e.dtype;n==null&&(n=e.inputDType),n==null&&(n="float32"),this.dtype=n}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new aa(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return Jr(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return Jr(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new La(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=It(e),n=!0;for(let s of r)if(!(s instanceof sa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof sa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return mo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of It(e))s.push(i.shape);this.build(Jr(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=It(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Jr(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=JW(e),i=this.computeOutputShape(s),o,l=QW(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new sa(l,u,this,It(e),t,this.name,d)):o=new sa(l,i,this,It(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new La(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new La(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new aa(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return mf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return uy(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let r=[],n=uy(t);for(let a=0;a<n.length;++a){let s=n[a],i=t[a],o=e[a];if(!w.arraysEqual(s.shape,o.shape))throw new q(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);r.push([i,o])}hA(r)})}addWeight(e,t,r,n,a,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(n=o!=null?o():Et("zeros"));let l=n.apply(t,r),u=new T7(l,r,e,s,i);return l.dispose(),a!=null&&this.addLoss(()=>a.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,r,n,a,s,i=null){let o=It(e);t=It(t),r=It(r),n=It(n),a=ff(a),s=ff(s);let l=[],u=[],d=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),d.push(h.tensorIndex);new Gm({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:r,outputMasks:n,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function JW(e){e=It(e);let t=[];for(let r of e)t.push(r.shape);return Jr(t)}function QW(e){return"float32"}function N7(e,t,r){if((t==null||r!=null&&r>0)&&(t=e.sourceLayer,r=e.nodeIndex),t.inboundNodes.length===0)return[e];{let n=t.inboundNodes[r];if(n.inboundLayers.length===0)return n.inputTensors;{let a=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],o=n.inboundLayers[s],l=n.nodeIndices[s],u=N7(i,o,l);for(let d of u)a.indexOf(d)===-1&&a.push(d)}return a}}}var ud=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Um("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=e.dtype||"float32";this.batchInputShape=t,this.dtype=r,this.inputSpec=[{shape:t}];let n=new sa(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new Gm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. 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R=0;R<E.length;++R){c.hasKey(E[R])||c.add(E[R],v[R],Array.isArray(C)?C[0]:C);let _=o.indexOf(E[R].name);_!==-1&&(l[_]=v[R])}a||re(x)}return c.disposeMasks(),s?l:l[0]}function NV(e,t){w.assert(e!=null&&e.length>0,()=>"Expected at least one fetch, got none");let r=[],n={};if(e.length===1){let a=W3(e[0],t);r=a.sorted,n=a.recipientMap}else{let a=new Set;for(let s of e){let{sorted:i,recipientMap:o}=W3(s,t);for(let l of i)a.has(l.name)||(r.push(l),a.add(l.name));for(let l in o)n[l]==null&&(n[l]=new Set),o[l].forEach(u=>n[l].add(u))}}return{sorted:r,recipientCounts:EV(n)}}function EV(e){let t={};for(let r in e)t[r]=e[r].size;return t}function W3(e,t){let r=new Set,n=[],a={};for(let o of t.names())r.add(o);let s=[],i=[];for(s.push(e);s.length>0;){let o=s[s.length-1];if(r.has(o.name)){s.pop();continue}let l=i[i.length-1]===s.length-1;if(o.inputs.length===0||l)s.pop(),n.push(o),r.add(o.name),l&&i.pop();else{i.push(s.length-1);for(let u of o.inputs)a[u.name]==null&&(a[u.name]=new Set),a[u.name].add(o.name),!r.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:a}}function RV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(let a of e.sourceLayer.inboundNodes[n].outputTensors)if(a.id===e.id){r=n;break}t=e.sourceLayer.getOutputAt(r)}return t}var ka=class extends st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Um(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Is(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Is(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ia(x===0,"input layer has >1 nodes"),Ia(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof ud))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},r={},n={},a={},s={},i=[],o=(y,A,x,b,v,C)=>{(b==null||v==null||C==null)&&(b=y.sourceLayer,v=y.nodeIndex,C=y.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new aa(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(ka.nodeKey(b,v)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let E=S.inboundLayers.length;for(let R=0;R<E;R++){let _=S.inputTensors[R],M=S.inboundLayers[R],I=S.nodeIndices[R],O=S.tensorIndices[R];o(_,A,x,M,I,O)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=n[y.outboundLayer.id]==null?0:n[y.outboundLayer.id];A=Math.max(A,x),n[y.outboundLayer.id]=A,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],C=y.nodeIndices[b],S=v.inboundNodes[C],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,E),r[S.id]=S}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(r[y])}let p={};for(let y in n){let A=n[y];A in p||(p[A]=[]),p[A].push(a[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort(Pc);this.layers=[];for(let y of c){let A=p[y];A.sort((x,b)=>{let v=s[x.id],C=s[b.id];return v<C?-1:v>C?1:0});for(let x of A)x instanceof ka&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort(Pc);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new aa(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new aa(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Gm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}hA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${AA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=py(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new ho;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return pp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=ko(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=ff(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Pc);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Jr(d)),p=ff(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ia(o in r),a.push(r[o])}return Jr(a)}runInternalGraph(e,t){t==null&&(t=ko(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Pc);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=y[x],C=A[x];r[b.id]=[v,C]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ia(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof ka?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=ka.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=ka.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=ka.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=ka.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],C=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let S=a[b];if(S.inboundNodes.length<=v){i(m,g);return}let E=S.inboundNodes[v];y.push(E.outputTensors[C])}y.length>0&&m.apply(Jr(y),A)}function l(m){let g=m.name,y=la(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!oW(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function MV(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function V7(e,t){return MV(e,t,"classWeight")}async function U7(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Or(e);if(e.shape.length===2){if(e.shape[1]>1)return Cn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function FV(e,t){return L(e,t)}var $V=32;function G7(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=V3("input",e.inputNames,r),i=V3("output",e.outputNames,n),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function V3(e,t,r){if(r instanceof rt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function PV(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function _V(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=r.validationData!=null,s,i;if(a)if(U3(r.validationData))w.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=PV(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=P7(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=_7(d,h,r.epochs,null,null,zV(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let f=r.initialEpoch==null?0:r.initialEpoch,m=await t.iterator();for(;f<r.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(n||(m=await t.iterator());n?y<r.batchesPerEpoch:!0;){let x=await m.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],r=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let c=0;c<this.inputs.length;++c)u.push({key:this.inputs[c],value:r[c]});let d=new ho(u),h=pp(this.outputs,d,{training:!0}),p;for(let c=0;c<this.lossFunctions.length;++c){let f=this.lossFunctions[c](n[c],h[c]);a[c]!=null&&(f=FV(f,a[c]));let m=Bt(f);t.push(m),c===0?p=f:p=le(p,f)}for(let c=0;c<this.metricsTensors.length;++c){let f;if(this.outputs.length>1&&c<this.outputs.length)f=t[c];else{let m=this.metricsTensors[c][0],g=this.metricsTensors[c][1];f=Bt(m(n[g],h[g]))}hr(f),s.push(f)}return p=Bt(p),this.calculateLosses().forEach(c=>{p=le(p,c)}),p},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>K(()=>{let t=[],r,n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:n[l]});let i=new ho(s),o=pp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Bt(u(a[l],o[l]));l===0?r=d:r=le(r,d),t.push(r)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],h=Bt(u(a[d],o[d]));t.push(h)}return t})}async fit(e,t,r={}){return BV(this,e,t,r)}async fitDataset(e,t){return _V(this,e,t)}async trainOnBatch(e,t){let r=await this.standardizeUserData(e,t),n=r[0],a=r[1],s=this.makeTrainFunction()(n.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return re(s),na(r[0],e),na(r[1],t),Jr(i)}getNamedWeights(e){let t=[],r=e!=null&&e.trainableOnly,n=r?this.trainableWeights:this.weights,a=this.getWeights(r);for(let s=0;s<n.length;++s)r&&!n[s].trainable||t.push({name:n[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=pf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-pf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ba(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ba(t))}else{let t=Object.keys(this.loss);e={};let r=this.loss;for(let n of t)if(typeof r[n]=="string")e[n]=Ba(r[n]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof 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if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=lo(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Ir.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ir.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:jV,generatedBy:`TensorFlow.js tfjs-layers v${AA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Ir.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Ir.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(L3(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){L3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Ga.className="Model";ue.registerClass(Ga);var H7=class extends Ga{};H7.className="Functional";ue.registerClass(H7);async function HV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=$p(r),a=la(n,t);if(e.weightsManifest!=null){let s=await Ir.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function qV(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Ir.getLoadHandlers(e,t);if(r.length===0)r.push(Ir.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) load handlers for URL '${e}'`);e=r[0]}return KV(e,void 0,t)}async function KV(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=la($p(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=XV(n.weightData,n.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),re(u),re(d.map(h=>h.tensor))}return o}function XV(e,t){let r=Ir.decodeWeights(e,t),n={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:r[s.name]}):n[s.name]=r[s.name]}),{modelWeights:n,optimizerWeights:a}}var my=class extends Ga{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Um("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof my||e instanceof Ga,r;if(t){if(r=e,r.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(r.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=E7({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=r.outputs,this.inputs=r.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=N7(this.outputs[0])}this.inboundNodes=[],new Gm({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ko(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{let n=e.apply(this.outputs[0]);if(Array.isArray(n))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[n],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ft(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Ga({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,r=console.log){this.built||this.build(),super.summary(e,t,r)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,r={}){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof my))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=la(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},qm=my;qm.className="Sequential";ue.registerClass(qm);function ZV(e){return new Ga(e)}function YV(e){return new qm(e)}function JV(e,t){return t==null&&(t={}),qV(e,t)}function q7(e){return E7(e)}function QV(e,t){cA.registerCallbackConstructor(e,t)}var rn=class extends ue.Serializable{getConfig(){return{}}},K7=class extends rn{apply(e,t=1){return SW(e,t)}};K7.className="elu";ue.registerClass(K7);var X7=class extends rn{apply(e){return D2(e)}};X7.className="selu";ue.registerClass(X7);var Z7=class extends rn{apply(e){return $a(e)}};Z7.className="relu";ue.registerClass(Z7);var Y7=class extends rn{apply(e){return K(()=>ph(6,$a(e)))}};Y7.className="relu6";ue.registerClass(Y7);var J7=class extends rn{apply(e){return e}};J7.className="linear";ue.registerClass(J7);var Q7=class extends rn{apply(e){return Sr(e)}};Q7.className="sigmoid";ue.registerClass(Q7);var e4=class extends rn{apply(e){return TW(e)}};e4.className="hardSigmoid";ue.registerClass(e4);var t4=class extends rn{apply(e){return ad(e)}};t4.className="softplus";ue.registerClass(t4);var r4=class extends rn{apply(e){return CW(e)}};r4.className="softsign";ue.registerClass(r4);var n4=class extends rn{apply(e){return fu(e)}};n4.className="tanh";ue.registerClass(n4);var bA=class extends rn{apply(e,t=-1){return od(e,t)}};bA.className="softmax";ue.registerClass(bA);var a4=class extends rn{apply(e,t=-1){return T2(e,t)}};a4.className="logSoftmax";ue.registerClass(a4);var s4=class extends rn{apply(e,t=1){return K(()=>L(Sr(L(e,t)),e))}};s4.className="swish";ue.registerClass(s4);var i4=class extends rn{apply(e){return K(()=>L(e,fu(ad(e))))}};i4.className="mish";ue.registerClass(i4);function Os(e){return e.getClassName()}function L1(e,t={}){return fh(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Ds(e){if(e==null){let t={};return t.className="linear",t.config={},L1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},L1(t)}else return e instanceof rn?e:L1(e)}function vA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var o4=class extends ue.Serializable{},xh=class extends o4{constructor(e){super();vA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,er(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,yh(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};xh.className="L1L2";ue.registerClass(xh);function eU(e){return vA(e),new xh({l1:e!=null?e.l1:null,l2:0})}function tU(e){return vA(e),new xh({l2:e!=null?e.l2:null,l1:0})}var q3={l1l2:"L1L2"};function At(e){return Y2(e)}function K3(e,t={}){return fh(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in q3?q3[e]:e,config:{}};return K3(t)}else return e instanceof o4?e:K3(e)}var wA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let r=$a(e);return this.maxValue!=null&&(r=un(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};wA.className="ReLU";ue.registerClass(wA);var kA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return hm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};kA.className="LeakyReLU";ue.registerClass(kA);var IA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ar(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new qt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=Ge(e),xm(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:nr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};IA.className="PReLU";ue.registerClass(IA);var SA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return uh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};SA.className="ELU";ue.registerClass(SA);var CA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=Ge(e);return L(r,me(hn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};CA.className="ThresholdedReLU";ue.registerClass(CA);var TA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new bA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=Ge(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};TA.className="Softmax";ue.registerClass(TA);function du(e,t,r){if(typeof e=="number")return ko(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!vW(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ua(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Sa(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+zs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function NA(e,t){return K(()=>(Vt(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function l4(e,t){return K(()=>(Vt(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function rU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=x2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=ya(o,r)),o})}function X3(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=NA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=_s.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function nU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=l4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=w2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=ya(o,r)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var EA=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",EA.verifyArgs(t),this.rank=e,cr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=du(t.kernelSize,e,"kernelSize"),this.strides=du(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Pn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Vt(this.dataFormat),this.activation=Ds(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ar(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=du(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ia("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!J2(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Os(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},bh=class extends EA{constructor(e,t){super(e,t);this.kernel=null,bh.verifyArgs(t),this.filters=t.filters,cr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ar(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r,n=this.bias==null?null:this.bias.read(),a=y7(this.activation.getClassName());if(a!=null&&this.rank===2)r=X3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=rU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=X3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=nU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ua(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:nr(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},u4=class extends bh{constructor(e){super(2,e);u4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!J2(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Km=u4;Km.className="Conv2D";ue.registerClass(Km);var d4=class extends bh{constructor(e){super(3,e);d4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Xm=d4;Xm.className="Conv3D";ue.registerClass(Xm);var RA=class extends Km{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Sa(o,h,u,this.padding),f=Sa(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,1]));let g=v2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Sa(t[n],o,s,this.padding),t[a]=Sa(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};RA.className="Conv2DTranspose";ue.registerClass(RA);var MA=class extends Xm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Sa(l,f,h,this.padding),A=Sa(u,m,p,this.padding),x=Sa(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,4,1]));let v=Ak(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=nt(v,[0,4,1,2,3])),this.bias!==null&&(v=ya(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Sa(t[n],u,i,this.padding),t[a]=Sa(t[a],d,o,this.padding),t[s]=Sa(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};MA.className="Conv3DTranspose";ue.registerClass(MA);var p4=class extends bh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ar(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ar(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),r=Lk(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=nt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseConstraint),e.pointwiseConstraint=nr(this.pointwiseConstraint),e}};p4.className="SeparableConv";var FA=class extends p4{constructor(e){super(2,e)}};FA.className="SeparableConv2D";ue.registerClass(FA);var h4=class extends bh{constructor(e){super(1,e);h4.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!J2(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},$A=h4;$A.className="Conv1D";ue.registerClass($A);var PA=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let r=_c(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return _c(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=_c(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return _c(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};PA.className="Cropping2D";ue.registerClass(PA);var _A=class extends st{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,AW(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=Ge(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=nt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};_A.className="UpSampling2D";ue.registerClass(_A);function aU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ha()),Vt(a);let i=NA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=lh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var zA=class extends EA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ar(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r=aU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ua(t,this.kernelSize[0],this.padding,this.strides[0]),s=ua(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseRegularizer),e}};zA.className="DepthwiseConv2D";ue.registerClass(zA);function c4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function f4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ca(2,l));if(t=nt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Ht(a,-1)),a=nt(a,u)),n&&(t=Mn(t,0),a!=null&&(a=Mn(a,0)));let d=[],h,p=r,c=t.shape[0],f=en(t),m;a!=null&&(m=en(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=m[y],C=he(Rn(v),v),S=le(L(x[0],v),L(p[0],C)),E=p.map((R,_)=>le(L(x[1][_],v),L(R,C)));return{output:S,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=sr(d,1)),[h,g,p]})}var m4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Jm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ca(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){ly(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");ly(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new qt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new La("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>hr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=c4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof sa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=Ge(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=f4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=gh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?iy(t,[1,r]):t):this.cell.stateSize>1?[iy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===m4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=la(n,r);return new e(Object.assign(t,{cell:a}))}},Ja=m4;Ja.className="RNN";ue.registerClass(Ja);var vh=class extends st{},Zm=class extends vh{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=xu([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xu([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ta(L(e,s),this.kernel.read()):a=Ta(e,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ta(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Zm.className="SimpleRNNCell";ue.registerClass(Zm);var OA=class extends Ja{constructor(e){e.cell=new Zm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};OA.className="SimpleRNN";ue.registerClass(OA);var Ym=class extends vh{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=xu([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xu([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ta(e,this.kernel.read());this.useBias&&(u=ya(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Kt(d,[2*this.units,this.units],d.rank-1),c=Ta(n,h),[f,m,g]=Kt(u,3,u.rank-1),[y,A]=Kt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Ta(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Ym.className="GRUCell";ue.registerClass(Ym);var DA=class extends Ja{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Ym(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};DA.className="GRU";ue.registerClass(DA);var wh=class extends vh{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=xu([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=xu([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Gn{apply(i,o){let l=a.apply([s]),u=new zm().apply([s]),d=a.apply([s*2]);return P3(P3(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ta(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ta(n,this.recurrentKernel.read())),this.useBias&&(h=ya(h,this.bias.read()));let[p,c,f,m]=Kt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};wh.className="LSTMCell";ue.registerClass(wh);var LA=class extends Ja{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new wh(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};LA.className="LSTM";ue.registerClass(LA);var Jm=class extends vh{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){ly(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{mo(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(la(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return uy(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}hA(t)}};Jm.className="StackedRNNCells";ue.registerClass(Jm);function Ls(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):I7(t(),r),o=()=>Ah(i,t,n);return!a||a<=1?hr(o().clone()):Array(a).fill(void 0).map(o).map(l=>hr(l.clone()))}var g4=class extends Ja{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new La("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>hr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ua(l,n[0],a,s[0],i[0]),h=ua(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};g4.className="ConvRNN2D";var Qm=class extends wh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t});this.filters=t,cr(this.filters,"filters"),this.kernelSize=du(r,2,"kernelSize"),this.kernelSize.forEach(o=>cr(o,"kernelSize")),this.strides=du(n||1,2,"strides"),this.strides.forEach(o=>cr(o,"strides")),this.padding=a||"valid",Pn(this.padding),this.dataFormat=s||"channelsLast",Vt(this.dataFormat),this.dilationRate=du(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>cr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Gn{apply(d,h){let p=l.apply([u]),c=ln([u]),f=l.apply([u*2]);return aA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,Y)=>!ee||!ee[Y]?V:L(ee[Y],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,C]=Kt(this.kernel.read(),i,A),[S,E,R,_]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,C,_,this.padding);let[M,I,O,z]=Kt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,O),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Fs(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?ya(a,r,this.dataFormat):a}recurrentConv(e,t){return Fs(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Qm.className="ConvLSTM2DCell";ue.registerClass(Qm);var BA=class extends g4{constructor(e){let t=new Qm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};BA.className="ConvLSTM2D";ue.registerClass(BA);var e0=class extends st{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return Ah(()=>I7(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};e0.className="Dropout";ue.registerClass(e0);var WA=class extends e0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};WA.className="SpatialDropout1D";ue.registerClass(WA);var VA=class extends st{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ar(e.kernelConstraint),this.biasConstraint=ar(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=y7(this.activation.getClassName()),a;return n!=null?a=Ta(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ta(r,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};VA.className="Dense";ue.registerClass(VA);var UA=class extends st{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ss(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=nt(r,n)}return IW(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};UA.className="Flatten";ue.registerClass(UA);var GA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ds(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.activation.apply(r)})}getConfig(){let e={activation:Os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};GA.className="Activation";ue.registerClass(GA);var jA=class extends st{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=Ge(e),wW(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};jA.className="RepeatVector";ue.registerClass(jA);var HA=class extends st{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Ss(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};HA.className="Reshape";ue.registerClass(HA);var qA=class extends st{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ca(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return nt(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Permute";ue.registerClass(qA);var KA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=Ge(e),n=-1;return hf(yu(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=-1,a=!0,s=hf(yu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};KA.className="Masking";ue.registerClass(KA);var XA=class extends st{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,cr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ar(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=Ge(e),yu(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);r.dtype!=="int32"&&(r=Pm(r,"int32"));let n=k7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:nr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};XA.className="Embedding";ue.registerClass(XA);var Il=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Is(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Is(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=zs(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=gh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Ss(u.slice(1))));p=nt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ca(1,l).concat([0]);r.push(nt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(nt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ca(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Is(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Ht(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=pa(r,t[n]);return r})}},ZA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};ZA.className="Add";ue.registerClass(ZA);var YA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};YA.className="Multiply";ue.registerClass(YA);var JA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};JA.className="Average";ue.registerClass(JA);var QA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=Xa(t,e[r]);return t})}};QA.className="Maximum";ue.registerClass(QA);var ex=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=ph(t,e[r]);return t})}};ex.className="Minimum";ue.registerClass(ex);var tx=class extends Il{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>aA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Rn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Ht(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return m2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};tx.className="Concatenate";ue.registerClass(tx);function sp(e,t){for(;e<0;)e+=t;return e}function sU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(nt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Ht(o,1)),o})}var rx=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>sp(a,e[s].shape.length)):n=[sp(this.axes,t.shape.length),sp(this.axes,r.shape.length)],this.normalize&&(t=gf(t,n[0]),r=gf(r,n[1])),sU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[sp(this.axes,e.length),sp(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};rx.className="Dot";ue.registerClass(rx);var nx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return Ah(()=>le(_m(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};nx.className="GaussianNoise";ue.registerClass(nx);var ax=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.rate>0&&this.rate<1?Ah(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,_m(r.shape,1,n))},()=>r,t.training||!1):r})}};ax.className="GaussianDropout";ue.registerClass(ax);var sx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return Ah(()=>{let n=Ge(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=xl(id(r),this.rate);o=Pm(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>Ge(e),t.training||!1)}return e})}};sx.className="AlphaDropout";ue.registerClass(sx);function Pp(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=uk(e,t,r,n,a,s);else if(e.rank===3)i=dk(e,t,r,n,a,s);else if(e.rank===4)i=pk(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function iU(e,t,r,n,a=.001){return K(()=>{let s=ym(e,n),i=s.mean,o=s.variance;return[Pp(e,i,o,r,t,a),i,o]})}function oU(e,t,r,n,a=.001){return K(()=>{let s=ym(e,n),i=s.mean,o=s.variance,l=[];for(let c of ca(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[Pp(e,u,d,p,h,a),i,o]})}function lU(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ca(0,e.rank-1))?iU(e,t,r,n,a):oU(e,t,r,n,a)}var ix=class extends st{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ar(e.betaConstraint),this.gammaConstraint=ar(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=Ge(e),a=n.shape,s=a.length,i=ca(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ko(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ca(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return Pp(n,g,y,A,x,this.epsilon)}else return Pp(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=lU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(he(b,y),x);g.write(he(b,v))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:nr(this.betaConstraint),gammaConstraint:nr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};ix.className="BatchNormalization";ue.registerClass(ix);var ox=class extends st{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Is(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=Ge(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=ym(r,this.axis,!0),o=ko(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Dn(s,h),i=Dn(i,h),u=Dn(u,p),d=Dn(d,p),Pp(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};ox.className="LayerNormalization";ue.registerClass(ox);function uU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ha()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Vn(e,n)})}var lx=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?ha():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>uU(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};lx.className="ZeroPadding2D";ue.registerClass(lx);function t0(e,t,r,n,a,s){return K(()=>{Vt(a),x7(s),Pn(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ha()),s==null&&(s="max"),e=NA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=gm(e,t,r,o):i=lm(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function y4(e,t,r,n,a,s){return K(()=>{Vt(a),x7(s),Pn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ha()),s==null&&(s="max"),e=l4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=M2(e,t,r,o):i=y2(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var A4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Pn(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=ua(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=gh(Ge(e),2);let r=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ux=class extends A4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),t0(e,t,r,n,a,"max")}};ux.className="MaxPooling1D";ue.registerClass(ux);var dx=class extends A4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),t0(e,t,r,n,a,"avg")}};dx.className="AveragePooling1D";ue.registerClass(dx);var x4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ua(t,this.poolSize[0],this.padding,this.strides[0]),r=ua(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},px=class extends x4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),t0(e,t,r,n,a,"max")}};px.className="MaxPooling2D";ue.registerClass(px);var hx=class extends x4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),t0(e,t,r,n,a,"avg")}};hx.className="AveragePooling2D";ue.registerClass(hx);var b4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ua(t,this.poolSize[0],this.padding,this.strides[0]),r=ua(r,this.poolSize[1],this.padding,this.strides[1]),n=ua(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},cx=class extends b4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),y4(e,t,r,n,a,"max")}};cx.className="MaxPooling3D";ue.registerClass(cx);var fx=class extends b4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),y4(e,t,r,n,a,"avg")}};fx.className="AveragePooling3D";ue.registerClass(fx);var v4=class extends st{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},mx=class extends v4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return Bt(r,1)})}};mx.className="GlobalAveragePooling1D";ue.registerClass(mx);var gx=class extends v4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return fr(r,1)})}};gx.className="GlobalMaxPooling1D";ue.registerClass(gx);var w4=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},yx=class extends w4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};yx.className="GlobalAveragePooling2D";ue.registerClass(yx);var Ax=class extends w4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?fr(r,[1,2]):fr(r,[2,3])})}};Ax.className="GlobalMaxPooling2D";ue.registerClass(Ax);var k4=class extends st{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let n=t.layer,a=la(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},xx=class extends k4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=Ge(e),f4((r,n)=>[Ge(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};xx.className="TimeDistributed";ue.registerClass(xx);function dU(e){wl(yW,"BidirectionalMergeMode",e)}var pU="concat",bx=class extends k4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=la(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=la(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?pU:e.mergeMode,dU(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Jr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=c4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new qt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof sa;for(let l of s)if(l instanceof sa!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=Mn(a,1));let i;return this.mergeMode==="concat"?i=aA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){mo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),mo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new La(`Layer ${this.name} is not connected, no input to return.`);return Jr(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new La(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new La(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. 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Input received: ${e}`);for(let r=0;r<e.length;r++){let n=e[r],a=t[r];if(a==null)continue;let s=n.rank;if(a.ndim!=null&&s!==a.ndim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected ndim=${a.ndim}, found ndim=${s}`);if(a.maxNDim!=null&&s>a.maxNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected max_ndim=${a.maxNDim}, found ndim=${s}`);if(a.minNDim!=null&&s<a.minNDim)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected min_ndim=${a.minNDim}, found ndim=${s}.`);if(a.dtype!=null&&n.dtype!==a.dtype)throw new q(`Input ${r} is incompatible with layer ${this.name} : expected dtype=${a.dtype}, found dtype=${n.dtype}.`);if(a.axes){let i=n.shape;for(let o in a.axes){let l=Number(o),u=a.axes[o],d=l>=0?i[l]:i[i.length+l];if(u!=null&&[u,null].indexOf(d)===-1)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${u} but got shape ${i}.`)}}if(a.shape!=null)for(let i=0;i<a.shape.length;++i){let o=a.shape[i],l=n.shape[i];if(o!=null&&l!=null&&o!==l)throw new q(`Input ${r} is incompatible with layer ${this.name}: expected shape=${a.shape}, found shape=${n.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let r=It(e),n=!0;for(let s of r)if(!(s instanceof sa)){n=!1;break}let a=!0;for(let s of r)if(s instanceof sa){a=!1;break}if(n===a)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return mo(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let s=[];for(let i of It(e))s.push(i.shape);this.build(Jr(s)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&a&&(this._refCount=1)}if(this.assertInputCompatibility(e),a){let s=this.call(e,t),i=It(s),o=[];for(let l of i)r.indexOf(l)!==-1&&(l=l.clone()),o.push(l);if(s=Jr(o),this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return s}else{let s=JW(e),i=this.computeOutputShape(s),o,l=QW(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?s[0]:s),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((u,d)=>new sa(l,u,this,It(e),t,this.name,d)):o=new sa(l,i,this,It(e),t,this.name),this.addInboundNode(e,o,null,null,s,i,t),this._refCount++,this.activityRegularizer!=null)throw new Ve("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((r,n)=>{r!=null&&e[n]!=null&&e[n]!==r&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new La(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let r=JSON.stringify(t.outputShapes);e.indexOf(r)===-1&&e.push(r)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new La(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new aa(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return ff(this.weights)}build(e){this.built=!0}getWeights(e=!1){return ly(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let r=[],n=ly(t);for(let a=0;a<n.length;++a){let s=n[a],i=t[a],o=e[a];if(!w.arraysEqual(s.shape,o.shape))throw new q(`Layer weight shape ${s.shape} not compatible with provided weight shape ${o.shape}`);r.push([i,o])}pA(r)})}addWeight(e,t,r,n,a,s,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),r==null&&(r="float32"),this.fastWeightInitDuringBuild&&(n=o!=null?o():Et("zeros"));let l=n.apply(t,r),u=new T7(l,r,e,s,i);return l.dispose(),a!=null&&this.addLoss(()=>a.apply(u.read())),s==null&&(s=!0),s?this._trainableWeights.push(u):this._nonTrainableWeights.push(u),u}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=It(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(r=>{if(r!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,r,n,a,s,i=null){let o=It(e);t=It(t),r=It(r),n=It(n),a=cf(a),s=cf(s);let l=[],u=[],d=[];for(let h of o)l.push(h.sourceLayer),u.push(h.nodeIndex),d.push(h.tensorIndex);new Um({outboundLayer:this,inboundLayers:l,nodeIndices:u,tensorIndices:d,inputTensors:o,outputTensors:t,inputMasks:r,outputMasks:n,inputShapes:a,outputShapes:s},i);for(let h=0;h<t.length;h++)t[h].sourceLayer=this,t[h].nodeIndex=this.inboundNodes.length-1,t[h].tensorIndex=h}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function JW(e){e=It(e);let t=[];for(let r of e)t.push(r.shape);return Jr(t)}function QW(e){return"float32"}function N7(e,t,r){if((t==null||r!=null&&r>0)&&(t=e.sourceLayer,r=e.nodeIndex),t.inboundNodes.length===0)return[e];{let n=t.inboundNodes[r];if(n.inboundLayers.length===0)return n.inputTensors;{let a=[];for(let s=0;s<n.inboundLayers.length;s++){let i=n.inputTensors[s],o=n.inboundLayers[s],l=n.nodeIndices[s],u=N7(i,o,l);for(let d of u)a.indexOf(d)===-1&&a.push(d)}return a}}}var ld=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Vm("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let r=e.dtype||"float32";this.batchInputShape=t,this.dtype=r,this.inputSpec=[{shape:t}];let n=new sa(this.dtype,this.batchInputShape,this,[],{},this.name);n.nodeIndex=0,n.tensorIndex=0,new Um({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[n],outputTensors:[n],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};ld.className="InputLayer";ue.registerClass(ld);function E7(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. 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Set),a[u.name].add(o.name),!r.has(u.name)&&s.push(u)}}return{sorted:n,recipientMap:a}}function RV(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let r=null;for(let n=0;n<e.sourceLayer.inboundNodes.length;++n)for(let a of e.sourceLayer.inboundNodes[n].outputTensors)if(a.id===e.id){r=n;break}t=e.sourceLayer.getOutputAt(r)}return t}var ka=class extends st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=Vm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Is(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(A),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let A=y.sourceLayer,x=y.nodeIndex,b=y.tensorIndex;Ia(x===0,"input layer has >1 nodes"),Ia(b===0,"input layer has >1 tensors"),this.inputLayers.push(A),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let A=this.inputLayers[y];if(!(A instanceof ld))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${A.getClassName()}.`);this.inputNames.push(A.name),this.feedInputShapes.push(A.batchInputShape),this.feedInputNames.push(A.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},r={},n={},a={},s={},i=[],o=(y,A,x,b,v,C)=>{(b==null||v==null||C==null)&&(b=y.sourceLayer,v=y.nodeIndex,C=y.tensorIndex);let S=b.inboundNodes[v];if(x.indexOf(S)!==-1)throw new aa(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(A.indexOf(S)!==-1)return;this.containerNodes.add(ka.nodeKey(b,v)),b.id in s||(s[b.id]=Object.keys(s).length),x.indexOf(S)===-1&&x.push(S);let E=S.inboundLayers.length;for(let R=0;R<E;R++){let _=S.inputTensors[R],M=S.inboundLayers[R],I=S.nodeIndices[R],O=S.tensorIndices[R];o(_,A,x,M,I,O)}for(A.push(S);x.indexOf(S)>=0;)x.splice(x.indexOf(S),1);i.push(S)},l=[],u=[];for(let y of this.outputs)o(y,l,u);let d=i.slice().reverse();for(let y of d){r[y.id]=y,y.id in t||(t[y.id]=0);let A=t[y.id],x=n[y.outboundLayer.id]==null?0:n[y.outboundLayer.id];A=Math.max(A,x),n[y.outboundLayer.id]=A,a[y.outboundLayer.id]=y.outboundLayer,t[y.id]=A;for(let b=0;b<y.inboundLayers.length;b++){let v=y.inboundLayers[b],C=y.nodeIndices[b],S=v.inboundNodes[C],E=t[S.id]==null?0:t[S.id];t[S.id]=Math.max(A+1,E),r[S.id]=S}}let h={};for(let y in t){let A=t[y];A in h||(h[A]=[]),h[A].push(r[y])}let p={};for(let y in n){let A=n[y];A in p||(p[A]=[]),p[A].push(a[y])}let c=Object.keys(p).map(y=>parseInt(y,10)).sort($c);this.layers=[];for(let y of c){let A=p[y];A.sort((x,b)=>{let v=s[x.id],C=s[b.id];return v<C?-1:v>C?1:0});for(let x of A)x instanceof ka&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=p,c=Object.keys(h).map(y=>parseInt(y,10)).sort($c);let f=this.inputs.slice(),m=[];for(let y of c)for(let A of h[y]){let x=A.outboundLayer;if(x!=null){for(let b of A.inputTensors)if(f.indexOf(b)===-1)throw new aa(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${x.name}". The following previous layers were accessed without issue: ${m}`);for(let b of A.outputTensors)f.push(b);m.push(x.name)}}this.nodesByDepth=h;let g=this.layers.map(y=>y.name);for(let y of g){let A=g.filter(x=>x===y).length;if(A!==1)throw new aa(`The name "${y}" is used ${A} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Um({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(r=>r.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.layers)t.push(...r.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let r={},n=0;for(let s of this.layers)for(let i of s.weights){if(r[i.originalName]!=null)throw new q(`Duplicate weight name: ${i.originalName}`);r[i.originalName]=i,n++}let a=[];for(let s in e){let i=s;if(r[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(r[i]!=null)a.push([r[i],e[s]]);else if(t)throw new q(`Provided weight data has no target variable: ${s}`);delete r[i]}if(t){let s=[];for(let i in r)s.push(i);if(s.length>0)throw new q(`${s.length} of ${n} weights are not set: ${s}`)}pA(a)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${yA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let r=dy(this.updatedConfig());return t?JSON.stringify(r):r}call(e,t){return K(()=>{e=It(e);let r=new ho;for(let n=0;n<this.inputs.length;++n)r.add(this.inputs[n],e[n]);return dp(this.outputs,r,t)})}computeMask(e,t){return K(()=>{e=It(e);let r;return t==null?r=ko(null,e.length):r=It(t),this.runInternalGraph(e,r)[1]})}computeOutputShape(e){let t=cf(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let r={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],l=t[i],u=o.name+"_0_0";r[u]=l}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort($c);if(n.length>1)for(let i of n){let o=this.nodesByDepth[i];for(let l of o){let u=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(u.id)!==-1)continue;let d=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],A=`${m.name}_${g}_${y}`,x=r[A];d.push(x)}let h=u.computeOutputShape(Jr(d)),p=cf(h),c=u.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${u.name}_${c}_${f}`;r[m]=p[f]}}}let a=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],l=this.outputLayersNodeIndices[i],u=this.outputLayersTensorIndices[i],d=`${o.name}_${l}_${u}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ia(o in r),a.push(r[o])}return Jr(a)}runInternalGraph(e,t){t==null&&(t=ko(null,e.length));let r={};for(let o=0;o<this.inputs.length;++o){let l=this.inputs[o],u=e[o],d=t[o];r[l.id]=[u,d]}let n=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort($c);for(let o of n){let l=this.nodesByDepth[o];for(let u of l){let d=u.outboundLayer,h=u.inputTensors,p=u.outputTensors,c=new Array;for(let f of h)f.id in r&&c.push(r[f.id]);if(c.length===h.length){let f={},m,g,y,A;if(u.callArgs!=null&&(f=u.callArgs),c.length===1){let[x,b]=c[0];f.mask==null&&(f.mask=b),y=It(d.call(x,f)),A=It(d.computeMask(x,b)),m=[x],g=[b]}else m=c.map(x=>x[0]),g=c.map(x=>x[1]),f.mask==null&&(f.mask=g),y=It(d.call(m,f)),A=It(d.computeMask(m,g));if(d.activityRegularizer)throw new Ve("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<p.length;++x){let b=p[x],v=y[x],C=A[x];r[b.id]=[v,C]}}}}let a=[],s=[],i=[];for(let o of this.outputs){Ia(o.id in r,`Could not compute output ${o.name} : ${o.id}`);let[l,u]=r[o.id];i.push(l.shape),a.push(l),s.push(u)}return[a,s,i]}buildNodeConversionMap(e){let t={},r;for(let n of this.layers){r=n instanceof ka?1:0;for(let a=0;a<n.inboundNodes.length;a++){let s=ka.nodeKey(n,a);this.containerNodes.has(s)&&(t[s]=r,r+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let r of this.layers)if(r.name===e)return r;throw new q(`No such layer: ${e}`)}calculateLosses(){return K(()=>{let e=[];for(let t of this.layers)for(let r=0;r<t.inboundNodes.length;++r){let n=ka.nodeKey(t,r);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),r=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),l=[];for(let d=0;d<s.inboundNodes.length;d++){let h=s.inboundNodes[d],p=ka.nodeKey(s,d),c={};if(this.containerNodes.has(p)){if(h.callArgs)try{JSON.stringify(h.callArgs),c=h.callArgs}catch(f){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${h.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),c={}}if(h.inboundLayers.length>0){let f=[];for(let m=0;m<h.inboundLayers.length;m++){let g=h.inboundLayers[m],y=h.nodeIndices[m],A=h.tensorIndices[m],x=ka.nodeKey(g,y),b=t[x];b==null&&(b=0),f.push([g.name,b,A,c])}l.push(f)}}}let u={};u.name=s.name,u.className=i,u.config=o,u.inboundNodes=l,r.push(u)}e.layers=r;let n=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.inputLayersTensorIndices[s];n.push([i.name,u,d])}e.inputLayers=n;let a=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],l=ka.nodeKey(i,o);if(!this.containerNodes.has(l))continue;let u=t[l];u==null&&(u=0);let d=this.outputLayersTensorIndices[s];a.push([i.name,u,d])}return e.outputLayers=a,e}static fromConfig(e,t,r={},n=!1){let a={},s={};function i(m,g){m.name in s?s[m.name].push(g):s[m.name]=[g]}function o(m,g){let y=[],A;for(let x of g){let b=x[0],v=x[1],C=x[2];if(A=x[3]==null?{}:x[3],!(b in a)){i(m,g);return}let S=a[b];if(S.inboundNodes.length<=v){i(m,g);return}let E=S.inboundNodes[v];y.push(E.outputTensors[C])}y.length>0&&m.apply(Jr(y),A)}function l(m){let g=m.name,y=la(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(n),a[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);i(y,A)})}let u=t.name,d=t.layers;for(let m of d)l(m);for(;!oW(s);)for(let m of d){let g=a[m.name];if(g.name in s){let y=s[g.name];delete s[g.name];for(let A of y)o(g,A)}}let h=[],p=[],c=t.inputLayers;for(let m of c){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;h.push(x[A])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],A=m[2];Ia(g in a);let x=a[g].inboundNodes[y].outputTensors;p.push(x[A])}return new e({inputs:h,outputs:p,name:u})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function MV(e,t,r){let n=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(a=>null);if(n===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==n)throw new Error(`Provided ${r} is an array of ${e.length} element(s), but the model has ${n} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let a=[];return t.forEach(s=>{s in e?a.push(e[s]):a.push(null)}),a}else throw new Error(`The model has multiple (${n}) outputs, so ${r} must be either an array with ${n} elements or an object with ${t} keys. Provided ${r} not understood: ${JSON.stringify(e)}`)}function V7(e,t){return MV(e,t,"classWeight")}async function U7(e,t,r,n){if(t!=null||n!=null)throw new Error("Support sampleWeight is not implemented yet");if(r!=null){let a=K(()=>{if(e.shape.length===1)return Or(e);if(e.shape.length===2){if(e.shape[1]>1)return Cn(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await a.data());re(a);let i=[];return s.forEach(o=>{if(r[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(r[o])}),St(i,"float32")}else return null}function FV(e,t){return L(e,t)}var $V=32;function G7(e,t){let r,n,a=t;r=a.xs,n=a.ys,w.assert(r!=null&&n!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let s=W3("input",e.inputNames,r),i=W3("output",e.outputNames,n),o=s[0].shape[0];w.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<s.length;l++)w.assert(s[l].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${s[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let l=0;l<i.length;l++)w.assert(i[l].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${i[l].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function W3(e,t,r){if(r instanceof rt)return[r];if(Array.isArray(r))return w.assert(r.length===t.length,()=>`Received an array of ${r.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),r;{let n=[];for(let a of t){if(r[a]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${a}'.`);n.push(r[a])}return n}}function PV(e){if(e.length===3)throw new Ve("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function _V(e,t,r){let n=r.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),w.assert(r!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),w.assert(r.epochs!=null&&r.epochs>0&&Number.isInteger(r.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${r.epochs}`),w.assert(!n||r.batchesPerEpoch>0&&Number.isInteger(r.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${r.batchesPerEpoch}`),w.assert(r.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let a=r.validationData!=null,s,i;if(a)if(V3(r.validationData))w.assert(r.validationBatches==null||r.validationBatches>0&&Number.isInteger(r.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${r.validationBatches}`);else{let g=PV(r.validationData);s=g.xs,i=g.ys}let o=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),u;a?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let d=P7(r.callbacks,r.yieldEvery),h=r.verbose==null?1:r.verbose,{callbackList:p,history:c}=_7(d,h,r.epochs,null,null,zV(t,r),null,a,u);p.setModel(e),e.history=c,await p.onTrainBegin(),e.stopTraining_=!1;let f=r.initialEpoch==null?0:r.initialEpoch,m=await t.iterator();for(;f<r.epochs;){let g={};await p.onEpochBegin(f);let y=0,A=0;for(n||(m=await t.iterator());!n||y<r.batchesPerEpoch;){let x=await m.next();if(n&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${r.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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Use LayersModel.compile(modelCompileArgs).");let a=[];for(let s=0;s<this.feedOutputShapes.length;++s){let i=this.feedOutputShapes[s];this.feedLossFns[s]===gf?a.push(i.slice(0,i.length-1).concat([1])):a.push(i)}if(e=G3(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=G3(t,this.feedOutputNames,a,!1,"target"),VV(e,t,null),UV(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&n!=null&&n>0&&e[0].shape[0]%n!==0)throw new q(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${n}. Found: ${e[0].shape[0]} sample(s).`);return[e,t]}async standardizeUserData(e,t,r,n,a=!0,s){let[i,o]=this.standardizeUserDataXY(e,t,a,s);if(r!=null)throw new Error("sample weight is not supported yet.");let l=null;if(n!=null){let u=V7(n,this.outputNames);l=[];for(let d=0;d<u.length;++d)l.push(await U7(o[d],null,u[d]))}return[i,o,l]}testLoop(e,t,r,n=0,a){return K(()=>{let s=this.checkNumSamples(t,r,a,"steps"),i=[];if(n>0)throw new Ve("Verbose mode is not implemented yet.");if(a!=null)throw new Ve("steps mode in testLoop() is not implemented yet");{let o=hy(s,r),l=St(ca(0,s));for(let u=0;u<o.length;++u){let d=o[u][0],h=o[u][1],p=go(l,d,h-d),c=AA(t,p),f=e(c);if(u===0)for(let m=0;m<f.length;++m)i.push(Se(0));for(let m=0;m<f.length;++m){let g=f[m];i[m]=le(i[m],L(h-d,g))}}for(let u=0;u<i.length;++u)i[u]=pe(i[u],s)}return i})}getDedupedMetricsNames(){let e=this.metricsNames,t=[];for(let r=0;r<e.length;++r){let n=e[r],a=n;E3(e,n)>1&&(a+=`_${E3(e.slice(0,r),n)}`),t.push(a)}return t}makeTrainFunction(){return e=>{let t=[],r=e.slice(0,this.inputs.length),n=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let u=[];for(let c=0;c<this.inputs.length;++c)u.push({key:this.inputs[c],value:r[c]});let d=new ho(u),h=dp(this.outputs,d,{training:!0}),p;for(let c=0;c<this.lossFunctions.length;++c){let f=this.lossFunctions[c](n[c],h[c]);a[c]!=null&&(f=FV(f,a[c]));let m=Bt(f);t.push(m),c===0?p=f:p=le(p,f)}for(let c=0;c<this.metricsTensors.length;++c){let f;if(this.outputs.length>1&&c<this.outputs.length)f=t[c];else{let m=this.metricsTensors[c][0],g=this.metricsTensors[c][1];f=Bt(m(n[g],h[g]))}hr(f),s.push(f)}return p=Bt(p),this.calculateLosses().forEach(c=>{p=le(p,c)}),p},o=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(i,l,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>K(()=>{let t=[],r,n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let l=0;l<this.inputs.length;++l)s.push({key:this.inputs[l],value:n[l]});let i=new ho(s),o=dp(this.outputs,i);for(let l=0;l<this.lossFunctions.length;++l){let u=this.lossFunctions[l],d=Bt(u(a[l],o[l]));l===0?r=d:r=le(r,d),t.push(r)}for(let l=0;l<this.metricsTensors.length;++l){let u=this.metricsTensors[l][0],d=this.metricsTensors[l][1],h=Bt(u(a[d],o[d]));t.push(h)}return t})}async fit(e,t,r={}){return BV(this,e,t,r)}async fitDataset(e,t){return _V(this,e,t)}async trainOnBatch(e,t){let r=await this.standardizeUserData(e,t),n=r[0],a=r[1],s=this.makeTrainFunction()(n.concat(a)),i=[];for(let o of s){let l=await o.data();i.push(l[0])}return re(s),na(r[0],e),na(r[1],t),Jr(i)}getNamedWeights(e){let t=[],r=e!=null&&e.trainableOnly,n=r?this.trainableWeights:this.weights,a=this.getWeights(r);for(let s=0;s<n.length;++s)r&&!n[s].trainable||t.push({name:n[s].originalName,tensor:a[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=df().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-df().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ba(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ba(t))}else{let t=Object.keys(this.loss);e={};let r=this.loss;for(let n of t)if(typeof r[n]=="string")e[n]=Ba(r[n]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Ba(zc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ba(zc(e)));{let e={};for(let t in this.metrics)e[t]=Ba(zc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Fp(e.optimizer_config),r=la(t),n;if(typeof e.loss=="string")n=lo(e.loss);else if(Array.isArray(e.loss))n=e.loss.map(s=>lo(s));else if(e.loss!=null){n={};for(let s in e.loss)n[s]=lo(e.loss[s])}let a;if(Array.isArray(e.metrics))a=e.metrics.map(s=>lo(s));else if(e.metrics!=null){a={};for(let s in e.metrics)a[s]=lo(e.metrics[s])}this.compile({loss:n,metrics:a,optimizer:r})}async save(e,t){if(typeof e=="string"){let i=Ir.getSaveHandlers(e);if(i.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new q(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let r=await Ir.encodeWeights(this.getNamedWeights(t)),n=!1,a=null,s={modelTopology:this.toJSON(a,n),format:jV,generatedBy:`TensorFlow.js tfjs-layers v${yA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:l}=await Ir.encodeWeights(await this.optimizer.getWeights(),i);r.specs.push(...l),r.data=Ir.concatenateArrayBuffers([r.data,o])}return this.userDefinedMetadata!=null&&(D3(this.userDefinedMetadata,this.name,!0),s.userDefinedMetadata=this.userDefinedMetadata),s.weightData=r.data,s.weightSpecs=r.specs,e.save(s)}setUserDefinedMetadata(e){D3(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};Ga.className="Model";ue.registerClass(Ga);var H7=class extends Ga{};H7.className="Functional";ue.registerClass(H7);async function HV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let r=e.modelTopology;r.model_config!=null&&(r=r.model_config);let n=Fp(r),a=la(n,t);if(e.weightsManifest!=null){let s=await Ir.loadWeights(e.weightsManifest,e.pathPrefix,a.weights.map(o=>o.originalName)),i={};for(let o of a.weights)i[o.originalName]=s[o.originalName];a.loadWeights(i),re(s)}return a}async function qV(e,t){if(t==null&&(t={}),typeof e=="string"){let r=Ir.getLoadHandlers(e,t);if(r.length===0)r.push(Ir.browserHTTPRequest(e,t));else if(r.length>1)throw new q(`Found more than one (${r.length}) load handlers for URL '${e}'`);e=r[0]}return KV(e,void 0,t)}async function KV(e,t,r){if(r==null&&(r={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let n=await e.load(),a=n.modelTopology;a.model_config!=null&&(a=a.model_config);let s=r.strict==null?!0:r.strict,i=n.weightData!=null&&n.weightSpecs!=null&&s,o=la(Fp(a),t,i),l=n.trainingConfig;if(l!=null&&o.loadTrainingConfig(l),n.userDefinedMetadata!=null&&o.setUserDefinedMetadata(n.userDefinedMetadata),n.weightData!=null){if(n.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:u,optimizerWeights:d}=XV(n.weightData,n.weightSpecs);o.loadWeights(u,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),re(u),re(d.map(h=>h.tensor))}return o}function XV(e,t){let r=Ir.decodeWeights(e,t),n={},a=[];return t.forEach(s=>{s.group==="optimizer"?a.push({name:s.name,tensor:r[s.name]}):n[s.name]=r[s.name]}),{modelWeights:n,optimizerWeights:a}}var fy=class extends Ga{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Vm("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof fy||e instanceof Ga,r;if(t){if(r=e,r.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(r.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let n=E7({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(n)}if(t)this.outputs=r.outputs,this.inputs=r.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=N7(this.outputs[0])}this.inboundNodes=[],new Um({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ko(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(n=>n.shape),outputShapes:this.outputs[0].shape})}else{let n=e.apply(this.outputs[0]);if(Array.isArray(n))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[n],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ft(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new Ga({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,r=console.log){this.built||this.build(),super.summary(e,t,r)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,r={}){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.evaluate(e,t,r)}async evaluateDataset(e,t){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,r={}){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.fit(e,t,r)}async fitDataset(e,t){if(!this.built)throw new aa("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,r={},n=!1){let a,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");a=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),a=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof fy))throw new Ve(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of a){let l=la(o,void 0,n);n&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let r={};r.className=t.getClassName(),r.config=t.getConfig(),e.push(r)}return{name:this.name,layers:e}}},Hm=fy;Hm.className="Sequential";ue.registerClass(Hm);function ZV(e){return new Ga(e)}function YV(e){return new Hm(e)}function JV(e,t){return t==null&&(t={}),qV(e,t)}function q7(e){return E7(e)}function QV(e,t){hA.registerCallbackConstructor(e,t)}var rn=class extends ue.Serializable{getConfig(){return{}}},K7=class extends rn{apply(e,t=1){return SW(e,t)}};K7.className="elu";ue.registerClass(K7);var X7=class extends rn{apply(e){return O2(e)}};X7.className="selu";ue.registerClass(X7);var Z7=class extends rn{apply(e){return $a(e)}};Z7.className="relu";ue.registerClass(Z7);var Y7=class extends rn{apply(e){return K(()=>dh(6,$a(e)))}};Y7.className="relu6";ue.registerClass(Y7);var J7=class extends rn{apply(e){return e}};J7.className="linear";ue.registerClass(J7);var Q7=class extends rn{apply(e){return Sr(e)}};Q7.className="sigmoid";ue.registerClass(Q7);var e4=class extends rn{apply(e){return TW(e)}};e4.className="hardSigmoid";ue.registerClass(e4);var t4=class extends rn{apply(e){return nd(e)}};t4.className="softplus";ue.registerClass(t4);var r4=class extends rn{apply(e){return CW(e)}};r4.className="softsign";ue.registerClass(r4);var n4=class extends rn{apply(e){return cu(e)}};n4.className="tanh";ue.registerClass(n4);var xA=class extends rn{apply(e,t=-1){return id(e,t)}};xA.className="softmax";ue.registerClass(xA);var a4=class extends rn{apply(e,t=-1){return C2(e,t)}};a4.className="logSoftmax";ue.registerClass(a4);var s4=class extends rn{apply(e,t=1){return K(()=>L(Sr(L(e,t)),e))}};s4.className="swish";ue.registerClass(s4);var i4=class extends rn{apply(e){return K(()=>L(e,cu(nd(e))))}};i4.className="mish";ue.registerClass(i4);function Os(e){return e.getClassName()}function D1(e,t={}){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Ds(e){if(e==null){let t={};return t.className="linear",t.config={},D1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},D1(t)}else return e instanceof rn?e:D1(e)}function bA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var o4=class extends ue.Serializable{},Ah=class extends o4{constructor(e){super();bA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return K(()=>{let t=Wt([1]);return this.hasL1&&(t=le(t,ke(L(this.l1,er(e))))),this.hasL2&&(t=le(t,ke(L(this.l2,gh(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ah.className="L1L2";ue.registerClass(Ah);function eU(e){return bA(e),new Ah({l1:e!=null?e.l1:null,l2:0})}function tU(e){return bA(e),new Ah({l2:e!=null?e.l2:null,l1:0})}var H3={l1l2:"L1L2"};function At(e){return Z2(e)}function q3(e,t={}){return ch(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Rt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in H3?H3[e]:e,config:{}};return q3(t)}else return e instanceof o4?e:q3(e)}var vA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ge(e);let r=$a(e);return this.maxValue!=null&&(r=un(r,0,this.maxValue)),r}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};vA.className="ReLU";ue.registerClass(vA);var wA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return pm(r,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};wA.className="LeakyReLU";ue.registerClass(wA);var kA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Et(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Rt(e.alphaRegularizer),this.alphaConstraint=ar(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let n of this.sharedAxes)t[n-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let r={};if(this.sharedAxes!=null)for(let n=1;n<e.length;++n)r[n]=e[n];this.inputSpec=[new qt({ndim:e.length,axes:r})],this.built=!0}call(e,t){return e=Ge(e),Am(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Pt(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:nr(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};kA.className="PReLU";ue.registerClass(kA);var IA=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Ve(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let r=Ge(e);return lh(r)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};IA.className="ELU";ue.registerClass(IA);var SA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let r=Ge(e);return L(r,me(hn(r,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};SA.className="ThresholdedReLU";ue.registerClass(SA);var CA=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new xA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let r=Ge(e);return this.softmax(r,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};CA.className="Softmax";ue.registerClass(CA);function uu(e,t,r){if(typeof e=="number")return ko(e,t);if(e.length!==t)throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let n=0;n<t;++n){let a=e[n];if(!vW(a))throw new q(`The ${r} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${a}`)}return e}function ua(e,t,r,n,a=1){if(e==null)return e;let s=t+(t-1)*(a-1),i;return r==="same"?i=e:i=e-s+1,Math.floor((i+n-1)/n)}function Sa(e,t,r,n){if(e==null)return null;if(n==="valid")e=e*t+zs([r-t,0]);else if(n==="same")e=e*t;else throw new q(`Unsupport padding mode: ${n}.`);return e}function TA(e,t){return K(()=>(Vt(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function l4(e,t){return K(()=>(Vt(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function rU(e,t,r,n=1,a="valid",s,i=1){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(r!=null&&r.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=nt(e,[0,2,1])),a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=A2(e,t,n,a==="same"?"same":"valid","NWC",i);return r!=null&&(o=ya(o,r)),o})}function K3(e,t,r,n=[1,1],a="valid",s,i,o=null){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=TA(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=_s.conv2d({x:l,filter:t,strides:n,pad:a==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:r,activation:o}),s==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function nU(e,t,r,n=[1,1,1],a="valid",s,i){return K(()=>{if(s==null&&(s=ha()),Vt(s),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=l4(e,s);if(a==="causal")throw new Ve("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=v2(o,t,n,a==="same"?"same":"valid","NDHWC",i),r!=null&&(o=ya(o,r)),s==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var NA=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",NA.verifyArgs(t),this.rank=e,cr(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ve(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=uu(t.kernelSize,e,"kernelSize"),this.strides=uu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Pn(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Vt(this.dataFormat),this.activation=Ds(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Et(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=ar(t.biasConstraint),this.biasRegularizer=Rt(t.biasRegularizer),this.activityRegularizer=Rt(t.activityRegularizer),this.dilationRate=uu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ia("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Y2(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Os(this.activation),useBias:this.useBias,biasInitializer:Pt(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},xh=class extends NA{constructor(e,t){super(e,t);this.kernel=null,xh.verifyArgs(t),this.filters=t.filters,cr(this.filters,"filters"),this.kernelInitializer=Et(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=ar(t.kernelConstraint),this.kernelRegularizer=Rt(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let r=e[t],n=this.kernelSize.concat([r,this.filters]);this.kernel=this.addWeight("kernel",n,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:r}}],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r,n=this.bias==null?null:this.bias.read(),a=y7(this.activation.getClassName());if(a!=null&&this.rank===2)r=K3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,a);else{if(this.rank===1)r=rU(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)r=K3(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)r=nU(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ve("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(r=this.activation.apply(r))}return r})}computeOutputShape(e){e=ft(e);let t=[],r=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let a=0;a<r.length;++a){let s=ua(r[a],this.kernelSize[a],this.padding,this.strides[a],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[a]);t.push(s)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:Pt(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:nr(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},u4=class extends xh{constructor(e){super(2,e);u4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Y2(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},qm=u4;qm.className="Conv2D";ue.registerClass(qm);var d4=class extends xh{constructor(e){super(3,e);d4.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Km=d4;Km.className="Conv3D";ue.registerClass(Km);var EA=class extends qm{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:4,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=n[s],l=n[i],u=this.kernelSize[0],d=this.kernelSize[1],h=this.strides[0],p=this.strides[1],c=Sa(o,h,u,this.padding),f=Sa(l,p,d,this.padding),m=[a,c,f,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,1]));let g=b2(r,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=ya(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3):(r=3,n=1,a=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],l=this.strides[1];return t[r]=this.filters,t[n]=Sa(t[n],o,s,this.padding),t[a]=Sa(t[a],l,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};EA.className="Conv2DTranspose";ue.registerClass(EA);var RA=class extends Km{constructor(e){super(e);if(this.inputSpec=[new qt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let r=e[t],n=this.kernelSize.concat([this.filters,r]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new qt({ndim:5,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{let r=Ge(e);if(r.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${r.shape.length}`);let n=r.shape,a=n[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let l=n[o],u=n[s],d=n[i],h=this.kernelSize[0],p=this.kernelSize[1],c=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Sa(l,f,h,this.padding),A=Sa(u,m,p,this.padding),x=Sa(d,g,c,this.padding),b=[a,y,A,x,this.filters];this.dataFormat!=="channelsLast"&&(r=nt(r,[0,2,3,4,1]));let v=Ak(r,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(v=nt(v,[0,4,1,2,3])),this.bias!==null&&(v=ya(v,this.bias.read(),this.dataFormat)),this.activation!==null&&(v=this.activation.apply(v)),v})}computeOutputShape(e){e=ft(e);let t=e.slice(),r,n,a,s;this.dataFormat==="channelsFirst"?(r=1,n=2,a=3,s=4):(r=4,n=1,a=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],d=this.strides[1],h=this.strides[2];return t[r]=this.filters,t[n]=Sa(t[n],u,i,this.padding),t[a]=Sa(t[a],d,o,this.padding),t[s]=Sa(t[s],h,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};RA.className="Conv3DTranspose";ue.registerClass(RA);var p4=class extends xh{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Rt(t.depthwiseRegularizer),this.depthwiseConstraint=ar(t.depthwiseConstraint),this.pointwiseInitializer=Et(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Rt(t.pointwiseRegularizer),this.pointwiseConstraint=ar(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let r=e[t],n=this.kernelSize.concat([r,this.depthMultiplier]),a=[];for(let i=0;i<this.rank;++i)a.push(1);a.push(r*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",a,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new qt({ndim:this.rank+2,axes:{[t]:r}})],this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r;if(this.rank===1)throw new Ve("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),r=Lk(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),this.dataFormat==="channelsFirst"&&(r=nt(r,[0,3,1,2])),r})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.pointwiseInitializer=Pt(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseConstraint),e.pointwiseConstraint=nr(this.pointwiseConstraint),e}};p4.className="SeparableConv";var MA=class extends p4{constructor(e){super(2,e)}};MA.className="SeparableConv2D";ue.registerClass(MA);var h4=class extends xh{constructor(e){super(1,e);h4.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Y2(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},FA=h4;FA.className="Conv1D";ue.registerClass(FA);var $A=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=Ge(e),this.dataFormat==="channelsLast"){let r=Pc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Pc(r,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let r=Pc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Pc(r,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};$A.className="Cropping2D";ue.registerClass($A);var PA=class extends st{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,AW(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],r=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,r]}else{let t=e[1]==null?null:this.size[0]*e[1],r=e[2]==null?null:this.size[1]*e[2];return[e[0],t,r,e[3]]}}call(e,t){return K(()=>{let r=Ge(e),n=r.shape;if(this.dataFormat==="channelsFirst"){r=nt(r,[0,2,3,1]);let a=this.size[0]*n[2],s=this.size[1]*n[3],i=this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s]);return nt(i,[0,3,1,2])}else{let a=this.size[0]*n[1],s=this.size[1]*n[2];return this.interpolation==="nearest"?Ie.resizeNearestNeighbor(r,[a,s]):Ie.resizeBilinear(r,[a,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};PA.className="UpSampling2D";ue.registerClass(PA);function aU(e,t,r=[1,1],n="valid",a,s){return K(()=>{a==null&&(a=ha()),Vt(a);let i=TA(e,a);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=oh(i,t,r,n==="same"?"same":"valid","NHWC",s),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}var _A=class extends NA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Et(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=ar(e.depthwiseConstraint),this.depthwiseRegularizer=Rt(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let r=e[t],n=[this.kernelSize[0],this.kernelSize[1],r,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[r*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{e=Ge(e);let r=aU(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(r=ya(r,this.bias.read(),this.dataFormat)),this.activation!=null&&(r=this.activation.apply(r)),r})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,a=ua(t,this.kernelSize[0],this.padding,this.strides[0]),s=ua(r,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,a,s]:[e[0],a,s,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Pt(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=nr(this.depthwiseRegularizer),e}};_A.className="DepthwiseConv2D";ue.registerClass(_A);function c4(e,t,r,n){if(Array.isArray(e)){if(t!=null||r!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");n!=null&&(r=e.slice(e.length-n,e.length),e=e.slice(0,e.length-n)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function a(s){return s==null||Array.isArray(s)?s:[s]}return t=a(t),r=a(r),{inputs:e,initialState:t,constants:r}}function f4(e,t,r,n=!1,a,s,i=!1,o=!1){return K(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(ca(2,l));if(t=nt(t,u),s!=null)throw new Ve("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),a!=null&&(a=me(me(a,"bool"),"float32"),a.rank===l-1&&(a=Ht(a,-1)),a=nt(a,u)),n&&(t=Mn(t,0),a!=null&&(a=Mn(a,0)));let d=[],h,p=r,c=t.shape[0],f=en(t),m;a!=null&&(m=en(a));for(let y=0;y<c;++y){let A=f[y],x=K(()=>e(A,p));if(a==null)h=x[0],p=x[1];else{let b=K(()=>{let v=m[y],C=he(Rn(v),v),S=le(L(x[0],v),L(p[0],C)),E=p.map((R,_)=>le(L(x[1][_],v),L(R,C)));return{output:S,newStates:E}});h=b.output,p=b.newStates}o&&d.push(h)}let g;return o&&(g=sr(d,1)),[h,g,p]})}var m4=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Ym({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new qt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ca(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){oy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let r=t[0],n;if(this.returnSequences?n=[e[0],e[1],r]:n=[e[0],r],this.returnState){let a=[];for(let s of t)a.push([e[0],s]);return[n].concat(a)}else return n}computeMask(e,t){return K(()=>{Array.isArray(t)&&(t=t[0]);let r=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(a=>null);return[r].concat(n)}else return r})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let r=0;r<e;++r)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ve("Constants support is not implemented in RNN yet.");oy(e)&&(e=e[0]),e=e;let t=this.stateful?e[0]:null,r=e.slice(2);this.inputSpec[0]=new qt({shape:[t,null,...r]});let n=[e[0]].concat(e.slice(2));this.cell.build(n);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(s=>s.shape[s.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(s=>new qt({shape:[null,s]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new La("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape[0];if(r==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_=[Wt([r,this.cell.stateSize])];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>Wt([r,n])):this.states_[0]=Wt([r,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):re(this.states_);for(let n=0;n<this.states_.length;++n){let a=e[n],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[r,s];if(!w.arraysEqual(a.shape,i))throw new q(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${a.shape}`);this.states_[n]=a}}this.states_=this.states_.map(n=>hr(n.clone()))})}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=c4(e,r,n,this.numConstants);e=a.inputs,r=a.initialState,n=a.constants;let s=[],i=[];if(r!=null){t.initialState=r,s=s.concat(r),this.stateSpec=[];for(let o of r)this.stateSpec.push(new qt({shape:o.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,s=s.concat(n),this.numConstants=n.length),s[0]instanceof sa){let o=[e].concat(s),l=this.inputSpec.concat(i),u=this.inputSpec;this.inputSpec=l;let d=super.apply(o,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;e=Ge(e),a==null&&(this.stateful?a=this.states_:a=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(a.length!==s)throw new q(`RNN Layer has ${s} state(s) but was passed ${a.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},o=f4((p,c)=>{let f=this.cell.call([p].concat(c),i);return[f[0],f.slice(1)]},e,a,this.goBackwards,r,null,this.unroll,this.returnSequences),l=o[0],u=o[1],d=o[2];this.stateful&&this.resetStates(d,n);let h=this.returnSequences?u:l;return this.returnState?[h].concat(d):h})}getInitialState(e){return K(()=>{let t=Wt(e.shape);return t=ke(t,[1,2]),t=mh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(r=>r>1?sy(t,[1,r]):t):this.cell.stateSize>1?[sy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let r=this.cell.getConfig();return this.getClassName()===m4.className&&(t.cell={className:this.cell.getClassName(),config:r}),{...r,...e,...t}}static fromConfig(e,t,r={}){let n=t.cell,a=la(n,r);return new e(Object.assign(t,{cell:a}))}},Ja=m4;Ja.className="RNN";ue.registerClass(Ja);var bh=class extends st{},Xm=class extends bh{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=Au([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Au([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let r=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:n,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(r),rate:this.recurrentDropout,training:n,dropoutFunc:this.dropoutFunc}));let a,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?a=Ta(L(e,s),this.kernel.read()):a=Ta(e,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),i!=null&&(r=L(r,i));let o=le(a,Ta(r,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Xm.className="SimpleRNNCell";ue.registerClass(Xm);var zA=class extends Ja{constructor(e){e.cell=new Xm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return new e(t)}};zA.className="SimpleRNN";ue.registerClass(zA);var Zm=class extends bh{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=Au([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Au([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return K(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:r,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(n),rate:this.recurrentDropout,training:r,count:3,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,s=this.recurrentDropoutMask,i,o,l;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let u=Ta(e,this.kernel.read());this.useBias&&(u=ya(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,s[0]));let d=this.recurrentKernel.read(),[h,p]=Kt(d,[2*this.units,this.units],d.rank-1),c=Ta(n,h),[f,m,g]=Kt(u,3,u.rank-1),[y,A]=Kt(c,2,c.rank-1);i=this.recurrentActivation.apply(le(f,y)),o=this.recurrentActivation.apply(le(m,A));let x=Ta(L(o,n),p);l=this.activation.apply(le(g,x));let b=le(L(i,n),L(le(1,zt(i)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Zm.className="GRUCell";ue.registerClass(Zm);var OA=class extends Ja{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Zm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};OA.className="GRU";ue.registerClass(OA);var vh=class extends bh{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ds(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Et(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Rt(e.kernelRegularizer),this.recurrentRegularizer=Rt(e.recurrentRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.kernelConstraint=ar(e.kernelConstraint),this.recurrentConstraint=ar(e.recurrentConstraint),this.biasConstraint=ar(e.biasConstraint),this.dropout=Au([1,zs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Au([1,zs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let r=e[e.length-1];this.kernel=this.addWeight("kernel",[r,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let n;if(this.useBias){if(this.unitForgetBias){let a=this.biasInitializer,s=this.units;n=new(t=class extends Gn{apply(i,o){let l=a.apply([s]),u=new _m().apply([s]),d=a.apply([s*2]);return $3($3(l,u),d)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],a=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(e),rate:this.dropout,training:r,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(n),rate:this.recurrentDropout,training:r,count:4,dropoutFunc:this.dropoutFunc}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,l,u,d;0<this.dropout&&this.dropout<1&&(e=L(e,s[0]));let h=Ta(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=L(n,i[0])),h=le(h,Ta(n,this.recurrentKernel.read())),this.useBias&&(h=ya(h,this.bias.read()));let[p,c,f,m]=Kt(h,4,h.rank-1);o=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(c),u=le(L(l,a),L(o,this.activation.apply(f))),d=this.recurrentActivation.apply(m);let g=L(d,this.activation.apply(u));return[g,g,u]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),recurrentInitializer:Pt(this.recurrentInitializer),biasInitializer:Pt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),recurrentConstraint:nr(this.recurrentConstraint),biasConstraint:nr(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};vh.className="LSTMCell";ue.registerClass(vh);var DA=class extends Ja{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new vh(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};DA.className="LSTM";ue.registerClass(DA);var Ym=class extends bh{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return K(()=>{e=e;let r=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(r.splice(0,i.stateSize.length)):n.push(r.splice(0,1));n.reverse();let a=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];r=n[i],i===0?s=[e[0]].concat(r):s=[s[0]].concat(r),s=o.call(s,t),a.push(s.slice(1))}r=[];for(let i of a.slice().reverse())r.push(...i);return[s[0]].concat(r)})}build(e){oy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((r,n)=>{mo(`RNNCell_${n}`,()=>{r.build(e),Array.isArray(r.stateSize)?t=r.stateSize[0]:t=r.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=n=>({className:n.getClassName(),config:n.getConfig()}),r={cells:this.cells.map(t)};return{...e,...r}}static fromConfig(e,t,r={}){let n=[];for(let a of t.cells)n.push(la(a,r));return new e({cells:n})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let r of this.cells)t.push(...r.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ly(e)}setWeights(e){let t=[];for(let r of this.cells){let n=r.weights.length,a=e.splice(n);for(let s=0;s<r.weights.length;++s)t.push([r.weights[s],a[s]])}pA(t)}};Ym.className="StackedRNNCells";ue.registerClass(Ym);function Ls(e){let{ones:t,rate:r,training:n=!1,count:a=1,dropoutFunc:s}=e,i=()=>s!=null?s(t(),r):I7(t(),r),o=()=>yh(i,t,n);return!a||a<=1?hr(o().clone()):Array(a).fill(void 0).map(o).map(l=>hr(l.clone()))}var g4=class extends Ja{constructor(e){if(e.unroll)throw new Ve("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ve("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new qt({ndim:5})]}call(e,t){return K(()=>{if(this.cell.dropoutMask!=null&&(re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let r=t==null?null:t.mask,n=t==null?null:t.training,a=t==null?null:t.initialState;return super.call(e,{mask:r,training:n,initialState:a})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return K(()=>{let{stateSize:t}=this.cell,r=e.shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)],s=Wt(a);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new La("Cannot call resetStates() on an RNN Layer that is not stateful.");let r=this.inputSpec[0].shape,n=this.computeSingleOutputShape(r),a=[n[0],...n.slice(2)];if(r[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_=[Wt(a)];else if(e==null)re(this.states_),this.keptStates!=null&&(re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Wt(a)):this.states_[0]=Wt(a);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):re(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=a;if(!w.arraysEqual(i.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>hr(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:r,kernelSize:n,padding:a,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",l=e[o?3:2],u=e[o?4:3],d=ua(l,n[0],a,s[0],i[0]),h=ua(u,n[1],a,s[1],i[1]);return[...e.slice(0,2),...o?[r,d,h]:[d,h,r]]}};g4.className="ConvRNN2D";var Jm=class extends vh{constructor(e){let{filters:t,kernelSize:r,strides:n,padding:a,dataFormat:s,dilationRate:i}=e;super({...e,units:t});this.filters=t,cr(this.filters,"filters"),this.kernelSize=uu(r,2,"kernelSize"),this.kernelSize.forEach(o=>cr(o,"kernelSize")),this.strides=uu(n||1,2,"strides"),this.strides.forEach(o=>cr(o,"strides")),this.padding=a||"valid",Pn(this.padding),this.dataFormat=s||"channelsLast",Vt(this.dataFormat),this.dilationRate=uu(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>cr(o,"dilationRate"))}build(e){var t;e=ft(e);let r=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[r]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[r]}`);let n=e[r],a=4,s=this.kernelSize.concat([n,this.filters*a]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*a]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;o=new(t=class extends Gn{apply(d,h){let p=l.apply([u]),c=ln([u]),f=l.apply([u*2]);return nA([p,c,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*a],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return K(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let r=t.training||!1,n=e[0],a=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ls({ones:()=>Rn(n),rate:this.dropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,l=(V,ee,Y)=>!ee||!ee[Y]?V:L(ee[Y],V),u=l(n,o,0),d=l(n,o,1),h=l(n,o,2),p=l(n,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ls({ones:()=>Rn(a),rate:this.recurrentDropout,training:r,count:i,dropoutFunc:this.dropoutFunc}));let c=this.recurrentDropoutMask,f=l(a,c,0),m=l(a,c,1),g=l(a,c,2),y=l(a,c,3),A=3,[x,b,v,C]=Kt(this.kernel.read(),i,A),[S,E,R,_]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];u=this.inputConv(u,x,S,this.padding),d=this.inputConv(d,b,E,this.padding),h=this.inputConv(h,v,R,this.padding),p=this.inputConv(p,C,_,this.padding);let[M,I,O,z]=Kt(this.recurrentKernel.read(),i,A);f=this.recurrentConv(f,M),m=this.recurrentConv(m,I),g=this.recurrentConv(g,O),y=this.recurrentConv(y,z);let j=this.recurrentActivation.apply(le(u,f)),X=this.recurrentActivation.apply(le(d,m)),D=le(L(X,s),L(j,this.activation.apply(le(h,g)))),Q=L(this.recurrentActivation.apply(le(p,y)),this.activation.apply(D));return[Q,Q,D]})}getConfig(){let{units:e,...t}=super.getConfig(),r={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...r}}inputConv(e,t,r,n){let a=Fs(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return r?ya(a,r,this.dataFormat):a}recurrentConv(e,t){return Fs(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Jm.className="ConvLSTM2DCell";ue.registerClass(Jm);var LA=class extends g4{constructor(e){let t=new Jm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};LA.className="ConvLSTM2D";ue.registerClass(LA);var Qm=class extends st{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,r=[];for(let n=0;n<this.noiseShape.length;++n)r.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return r}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,a=this.getNoiseShape(r);return yh(()=>I7(r,this.rate,a,this.seed),()=>r,n)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Qm.className="Dropout";ue.registerClass(Qm);var BA=class extends Qm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};BA.className="SpatialDropout1D";ue.registerClass(BA);var WA=class extends st{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,cr(this.units,"units"),this.activation=Ds(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Et(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Et(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=ar(e.kernelConstraint),this.biasConstraint=ar(e.biasConstraint),this.kernelRegularizer=Rt(e.kernelRegularizer),this.biasRegularizer=Rt(e.biasRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=y7(this.activation.getClassName()),a;return n!=null?a=Ta(r,this.kernel.read(),n,this.bias?this.bias.read():null):(a=Ta(r,this.kernel.read()),this.bias!=null&&(a=ya(a,this.bias.read())),this.activation!=null&&(a=this.activation.apply(a))),a})}getConfig(){let e={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:Pt(this.kernelInitializer),biasInitializer:Pt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:nr(this.kernelConstraint),biasConstraint:nr(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Dense";ue.registerClass(WA);var VA=class extends st{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Ss(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);if(this.dataFormat==="channelsFirst"&&r.rank>1){let n=[0];for(let a=2;a<r.rank;++a)n.push(a);n.push(1),r=nt(r,n)}return IW(r)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};VA.className="Flatten";ue.registerClass(VA);var UA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ds(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.activation.apply(r)})}getConfig(){let e={activation:Os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};UA.className="Activation";ue.registerClass(UA);var GA=class extends st{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=Ge(e),wW(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};GA.className="RepeatVector";ue.registerClass(GA);var jA=class extends st{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let r="Total size of new array must be unchanged.",n=t.slice(),a=1,s=null;for(let o=0;o<n.length;++o){let l=n[o];if(this.isUnknown(l))if(s===null)s=o;else throw new q("Can only specifiy one unknown dimension.");else a*=l}let i=Ss(e);if(s!==null){if(a===0||i%a!==0)throw new q(r);n[s]=i/a}else if(i!==a)throw new q(r);return n}computeOutputShape(e){let t=!1;for(let r=0;r<e.length;++r)if(this.isUnknown(e[r])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=r.shape,a=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return G(r,a)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};jA.className="Reshape";ue.registerClass(jA);var HA=class extends st{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ca(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new qt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((r,n)=>{t[n+1]=e[r]}),t}call(e,t){return nt(Ge(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};HA.className="Permute";ue.registerClass(HA);var qA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let r=Ge(e),n=-1;return pf(gu(r,this.maskValue),n)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e),n=-1,a=!0,s=pf(gu(r,this.maskValue),n,a);return L(r,me(s,r.dtype))})}};qA.className="Masking";ue.registerClass(qA);var KA=class extends st{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(It(e.inputLength))}this.inputDim=e.inputDim,cr(this.inputDim,"inputDim"),this.outputDim=e.outputDim,cr(this.outputDim,"outputDim"),this.embeddingsInitializer=Et(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Rt(e.embeddingsRegularizer),this.activityRegularizer=Rt(e.activityRegularizer),this.embeddingsConstraint=ar(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=Ge(e),gu(e,at(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=It(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let r=0;for(let n=0;n<t.length;++n){let a=t[n],s=e[n+1];if(a!=null&&s!=null&&a!==s)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);a==null&&(t[r]=s),r++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);r.dtype!=="int32"&&(r=$m(r,"int32"));let n=k7(this.embeddings.read(),G(r,[r.size]));return G(n,ft(this.computeOutputShape(r.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Pt(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:nr(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};KA.className="Embedding";ue.registerClass(KA);var Il=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ve}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let r=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let a=e[e.length-t.length+n],s=t[n];if(a==null||s==null||a<0||s<0)r.push(null);else if(a===1)r.push(s);else if(s===1)r.push(a);else{if(a!==s)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));r.push(a)}}return r}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let a of e)a!=null&&a[0]!==null&&t.push(a[0]);if(t=Is(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let r=e[0]==null?null:e[0].slice(1);for(let a=1;a<e.length;++a){let s=e[a]==null?null:e[a].slice(1);r=this.computeElementwiseOpOutputShape(r,s)}let n=e.map(a=>a.length);e.indexOf(null)===-1&&Is(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let r=[],n=e.map(a=>a.rank);if(n.indexOf(null)===-1){let a=zs(n);for(let s of e){let i=s.rank;for(let o=0;o<a-i;++o)s=mh(s,1);r.push(s)}return this.mergeFunction(r)}else{let a=!1;for(let o of e){let l=o.rank;if(l==null){let u=o.shape,d=u[0],h=u.slice(1).concat([d]),p=G(o,[d].concat(Ss(u.slice(1))));p=nt(p,[1,0]),p=G(p,h),r.push(p),a=!0}else if(l>1){let u=ca(1,l).concat([0]);r.push(nt(o,u)),a=!0}else r.push(o)}let s=this.mergeFunction(r),i=s.rank;if(a){if(i==null){let o=s.shape,l=o.length,u=o[l-1],d=[u].concat(o.slice(0,o.length-1));s=G(nt(G(s,[-1,u]),[1,0]),d)}else if(i>1){let o=[i-1].concat(ca(0,i-1));s=nt(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let a=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,a)}let r=[];for(let n of e)n!=null&&n[0]!==null&&r.push(n[0]);return r=Is(r),r.length===1?t=r.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:Ht(n,0));let r=t[0];for(let n=1;n<t.length-1;++n)r=pa(r,t[n]);return r})}},XA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return t})}};XA.className="Add";ue.registerClass(XA);var ZA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=L(t,e[r]);return t})}};ZA.className="Multiply";ue.registerClass(ZA);var YA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let r=1;r<e.length;++r)t=le(t,e[r]);return L(1/e.length,t)})}};YA.className="Average";ue.registerClass(YA);var JA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=Xa(t,e[r]);return t})}};JA.className="Maximum";ue.registerClass(JA);var QA=class extends Il{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let r=1;r<e.length;++r)t=dh(t,e[r]);return t})}};QA.className="Minimum";ue.registerClass(QA);var ex=class extends Il{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let r=[];for(let n=0;n<e.length;++n){let a=e[n].slice();a.splice(this.axis,1);let s=!1;for(let i of r)if(w.arraysEqual(i,a)){s=!0;break}s||r.push(a)}if(r.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>nA(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,r=t[0].slice(),n=this.axis<0?r.length+this.axis:this.axis;for(let a of t.slice(1)){if(r[n]==null||a[n]==null){r[n]=null;break}r[n]+=a[n]}return r}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let r=!0;if(t.forEach(s=>{if(s!=null){r=!1;return}}),r)return null;let n=[];for(let s=0;s<e.length;++s)t[s]==null?n.push(me(Rn(e[s]),"bool")):t[s].rank<e[s].rank?n.push(Ht(t[s],-1)):n.push(t[s]);let a=kt(n,this.axis);return f2(a,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};ex.className="Concatenate";ue.registerClass(ex);function ap(e,t){for(;e<0;)e+=t;return e}function sU(e,t,r){if(e.shape.length>3||t.shape.length>3)throw new Ve("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof r=="number"&&(r=[r,r]),e.dtype==="complex64"||t.dtype==="complex64")throw new Ve("batchDot is not implemented for complex64-type Tensors yet.");let n=e.shape.length,a=t.shape.length;r==null&&(r=[n-1,a-2]);let s=r;return K(()=>{let i;if(n>a){i=n-a;let l=[];for(let u=0;u<i;++u)l.push(1);t=G(t,t.shape.concat(l))}else if(a>n){i=a-n;let l=[];for(let u=0;u<i;++u)l.push(1);e=G(e,e.shape.concat(l))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=ke(L(e,t),s[0]):o=ke(L(nt(e,[1,0]),t),s[1]);else{let l=s[0]!==e.shape.length-1,u=s[1]===t.shape.length-1;o=Je(e,t,l,u)}if(i>0){let l;n>a?l=n+a-3:l=n-1;let u=[];for(let d=l;d<l+i;++d)u.push(d);o=et(o,u)}return o.shape.length===1&&(o=Ht(o,1)),o})}var tx=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],r=e[1];if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);if(t[n[0]]!==r[n[1]])throw new q(`Dimension incompatibility: ${t[n[0]]} !== ${r[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],r=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((a,s)=>ap(a,e[s].shape.length)):n=[ap(this.axes,t.shape.length),ap(this.axes,r.shape.length)],this.normalize&&(t=mf(t,n[0]),r=mf(r,n[1])),sU(t,r,n)}interpretAxes(e,t){let r;return Array.isArray(this.axes)?r=this.axes:r=[ap(this.axes,e.length),ap(this.axes,t.length)],r}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),r=e[1].slice();if(t.length>3||r.length>3)throw new Ve("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,r);t.splice(n[0],1),r.splice(n[1],1),r.splice(0,1);let a=t.concat(r);return a.length===1&&a.push(1),a}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};tx.className="Dot";ue.registerClass(tx);var rx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return yh(()=>le(Pm(r.shape,0,this.stddev),r),()=>r,t.training||!1)})}};rx.className="GaussianNoise";ue.registerClass(rx);var nx=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{this.invokeCallHook(e,t);let r=Ge(e);return this.rate>0&&this.rate<1?yh(()=>{let n=Math.sqrt(this.rate/(1-this.rate));return L(r,Pm(r.shape,1,n))},()=>r,t.training||!1):r})}};nx.className="GaussianDropout";ue.registerClass(nx);var ax=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ge(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let r=this._getNoiseShape(e);return yh(()=>{let n=Ge(e),a=1.6732632423543772,s=1.0507009873554805,i=-a*s,o=xl(sd(r),this.rate);o=$m(o,"float32");let l=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-l*i*this.rate,d=le(L(n,o),L(le(o,-1),i));return le(L(d,l),u)},()=>Ge(e),t.training||!1)}return e})}};ax.className="AlphaDropout";ue.registerClass(ax);function $p(e,t,r,n,a,s=.001){let i;if(e.rank===2)i=uk(e,t,r,n,a,s);else if(e.rank===3)i=dk(e,t,r,n,a,s);else if(e.rank===4)i=pk(e,t,r,n,a,s);else throw new Ve(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function iU(e,t,r,n,a=.001){return K(()=>{let s=gm(e,n),i=s.mean,o=s.variance;return[$p(e,i,o,r,t,a),i,o]})}function oU(e,t,r,n,a=.001){return K(()=>{let s=gm(e,n),i=s.mean,o=s.variance,l=[];for(let c of ca(0,e.rank))n.indexOf(c)!==-1?l.push(1):l.push(e.shape[c]);let u=G(i,l),d=G(o,l),h=t==null?null:G(t,l),p=r==null?null:G(r,l);return[$p(e,u,d,p,h,a),i,o]})}function lU(e,t,r,n,a=.001){return w.arraysEqual(n.slice().sort(),ca(0,e.rank-1))?iU(e,t,r,n,a):oU(e,t,r,n,a)}var sx=class extends st{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.movingMeanInitializer=Et(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Et(e.movingVarianceInitializer||"ones"),this.betaConstraint=ar(e.betaConstraint),this.gammaConstraint=ar(e.gammaConstraint),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,r=e[t];if(r==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new qt({ndim:e.length,axes:{[t]:r}})];let n=[r];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let r=t.training==null?!1:t.training,n=Ge(e),a=n.shape,s=a.length,i=ca(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let l=ko(1,s);l[o]=a[o];let u=i.slice();u.sort();let d=!w.arraysEqual(u,ca(0,s).slice(0,s-1)),h=()=>{if(d){let g=G(this.movingMean.read(),l),y=G(this.movingVariance.read(),l),A=this.center?G(this.beta.read(),l):null,x=this.scale?G(this.gamma.read(),l):null;return $p(n,g,y,A,x,this.epsilon)}else return $p(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!r)return h();let[p,c,f]=lU(n,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(g,y,A)=>{K(()=>{let x=1-A,b=g.read(),v=L(he(b,y),x);g.write(he(b,v))})};return m(this.movingMean,c,this.momentum),m(this.movingVariance,f,this.momentum),p})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),movingMeanInitializer:Pt(this.movingMeanInitializer),movingVarianceInitializer:Pt(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:nr(this.betaConstraint),gammaConstraint:nr(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};sx.className="BatchNormalization";ue.registerClass(sx);var ix=class extends st{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=Et(e.betaInitializer||"zeros"),this.gammaInitializer=Et(e.gammaInitializer||"ones"),this.betaRegularizer=Rt(e.betaRegularizer),this.gammaRegularizer=Rt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let a=0;a<this.axis.length;++a)this.axis[a]<0&&(this.axis[a]+=t);for(let a of this.axis)if(a<0||a>=t)throw new Error(`Invalid axis: ${a}`);if(this.axis.length!==Is(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let r=this.axis.map(a=>e[a]),n=!0;this.scale?this.gamma=this.addWeight("gamma",r,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",r,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let r=Ge(e),n=r.shape,a=n.length;return K(()=>{let{mean:s,variance:i}=gm(r,this.axis,!0),o=ko(1,a);for(let c of this.axis)o[c]=n[c];let l=c=>c!=null&&c.shape.length!==a?G(c,o):c,u=l(this.gamma.read()),d=l(this.beta.read()),h=[],p=[];for(let c=0;c<a;++c)this.axis.indexOf(c)!==-1?(h.push(n[c]),p.push(1)):(h.push(1),p.push(n[c]));return s=Dn(s,h),i=Dn(i,h),u=Dn(u,p),d=Dn(d,p),$p(r,s,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Pt(this.betaInitializer),gammaInitializer:Pt(this.gammaInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};ix.className="LayerNormalization";ue.registerClass(ix);function uU(e,t,r){return K(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(r==null&&(r=ha()),r!=="channelsLast"&&r!=="channelsFirst")throw new q(`Unknown data format: ${r}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let n;return r==="channelsFirst"?n=[[0,0],[0,0],t[0],t[1]]:n=[[0,0],t[0],t[1],[0,0]],Vn(e,n)})}var ox=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?ha():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,r;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],r=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);r=e.padding[1]}this.padding=[t,r]}this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,r;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?r=e[3]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],e[1],t,r]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?r=e[2]+this.padding[1][0]+this.padding[1][1]:r=null,[e[0],t,r,e[3]])}call(e,t){return K(()=>uU(Ge(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ox.className="ZeroPadding2D";ue.registerClass(ox);function e0(e,t,r,n,a,s){return K(()=>{Vt(a),x7(s),Pn(n),r==null&&(r=[1,1]),n==null&&(n="valid"),a==null&&(a=ha()),s==null&&(s="max"),e=TA(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=mm(e,t,r,o):i=om(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,3,1,2])),i})}function y4(e,t,r,n,a,s){return K(()=>{Vt(a),x7(s),Pn(n),r==null&&(r=[1,1,1]),n==null&&(n="valid"),a==null&&(a=ha()),s==null&&(s="max"),e=l4(e,a);let i,o=n==="same"?"same":"valid";return s==="max"?i=R2(e,t,r,o):i=g2(e,t,r,o),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var A4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(cr(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Pn(this.padding),this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=ua(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=mh(Ge(e),2);let r=this.poolingFunction(Ge(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return et(r,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},lx=class extends A4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"max")}};lx.className="MaxPooling1D";ue.registerClass(lx);var ux=class extends A4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"avg")}};ux.className="AveragePooling1D";ue.registerClass(ux);var x4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ua(t,this.poolSize[0],this.padding,this.strides[0]),r=ua(r,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r]:[e[0],t,r,e[3]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},dx=class extends x4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"max")}};dx.className="MaxPooling2D";ue.registerClass(dx);var px=class extends x4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),e0(e,t,r,n,a,"avg")}};px.className="AveragePooling2D";ue.registerClass(px);var b4=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];cr(this.poolSize,"poolSize"),cr(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),Pn(this.padding),this.inputSpec=[new qt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],r=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ua(t,this.poolSize[0],this.padding,this.strides[0]),r=ua(r,this.poolSize[1],this.padding,this.strides[1]),n=ua(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,r,n]:[e[0],t,r,n,e[4]]}call(e,t){return K(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ge(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},hx=class extends b4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),y4(e,t,r,n,a,"max")}};hx.className="MaxPooling3D";ue.registerClass(hx);var cx=class extends b4{constructor(e){super(e)}poolingFunction(e,t,r,n,a){return Vt(a),Pn(n),y4(e,t,r,n,a,"avg")}};cx.className="AveragePooling3D";ue.registerClass(cx);var v4=class extends st{constructor(e){super(e);this.inputSpec=[new qt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ve}},fx=class extends v4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return Bt(r,1)})}};fx.className="GlobalAveragePooling1D";ue.registerClass(fx);var mx=class extends v4{constructor(e){super(e||{})}call(e,t){return K(()=>{let r=Ge(e);return fr(r,1)})}};mx.className="GlobalMaxPooling1D";ue.registerClass(mx);var w4=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Vt(this.dataFormat),this.inputSpec=[new qt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ve}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},gx=class extends w4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?Bt(r,[1,2]):Bt(r,[2,3])})}};gx.className="GlobalAveragePooling2D";ue.registerClass(gx);var yx=class extends w4{call(e,t){return K(()=>{let r=Ge(e);return this.dataFormat==="channelsLast"?fr(r,[1,2]):fr(r,[2,3])})}};yx.className="GlobalMaxPooling2D";ue.registerClass(yx);var k4=class extends st{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,r={}){let n=t.layer,a=la(n,r);delete t.layer;let s={layer:a};return Object.assign(s,t),new e(s)}},Ax=class extends k4{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),r=this.layer.computeOutputShape(t),n=e[1];return[r[0],n].concat(r.slice(1))}call(e,t){return K(()=>(e=Ge(e),f4((r,n)=>[Ge(this.layer.call(r,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ax.className="TimeDistributed";ue.registerClass(Ax);function dU(e){wl(yW,"BidirectionalMergeMode",e)}var pU="concat",xx=class extends k4{constructor(e){super(e);let t=e.layer.getConfig(),r={};r.className=e.layer.getClassName(),r.config=t,this.forwardLayer=la(r),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=la(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?pU:e.mergeMode,dU(this.mergeMode),e.weights)throw new Ve("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,r=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,r)),this.backwardLayer.setWeights(e.slice(r))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let r,n,a;return this.returnState&&(a=t.slice(1)),r=t[0],r=r,this.mergeMode==="concat"?(r[r.length-1]*=2,n=[r]):this.mergeMode==null?n=[r,r.slice()]:n=[r],this.returnState?this.mergeMode==null?n.concat(a).concat(a.slice()):[r].concat(a).concat(a.slice()):Jr(n)}apply(e,t){let r=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let a=c4(e,r,n,this.numConstants);if(e=a.inputs,r=a.initialState,n=a.constants,Array.isArray(e)&&(r=e.slice(1),e=e[0]),(r==null||r.length===0)&&n==null)return super.apply(e,t);let s=[],i=[];if(r!=null){let l=r.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=r,s.push(...r);let u=r.map(d=>new qt({shape:d.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),i.push(...u)}if(n!=null)throw new Ve("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof sa;for(let l of s)if(l instanceof sa!==o)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let l=[e].concat(s),u=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=u;let h=super.apply(l,t);return this.inputSpec=d,h}else return super.apply(e,t)}call(e,t){return K(()=>{let r=t.initialState,n,a;if(r==null)n=this.forwardLayer.call(e,t),a=this.backwardLayer.call(e,t);else{let o=r.slice(0,r.length/2),l=r.slice(r.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),a=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let s;this.returnState&&(Array.isArray(n)&&(s=n.slice(1).concat(a.slice(1))),n=n[0],a=a[0]),this.returnSequences&&(a=Mn(a,1));let i;return this.mergeMode==="concat"?i=nA([n,a]):this.mergeMode==="sum"?i=le(n,a):this.mergeMode==="ave"?i=L(.5,le(n,a)):this.mergeMode==="mul"?i=L(n,a):this.mergeMode==null&&(i=[n,a]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){mo(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),mo(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let r;if(this.returnSequences?this.mergeMode==null?r=[t,t]:r=t:this.mergeMode==null?r=[null,null]:r=null,this.returnState){let n=this.forwardLayer.states.map(a=>null);return Array.isArray(r)?r.concat(n).concat(n):[r].concat(n).concat(n)}else return r}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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implemented`)}},Rj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[kk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[nt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=k("args",e,t,r);return[_s.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[mu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[mu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[Nk(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[od(k("x",e,t,r))];case"LogSoftmax":return[T2(k("x",e,t,r))];case"SparseToDense":return[H2(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Fj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[fr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[$s(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[m2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[hf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Cn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[ek(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[F2(k("x",e,t,r),i,o)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[I2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[A2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[xk(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$j=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[gu(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[gu(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[Mn(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[Mn(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[Vk(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=et(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(et(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[sr(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return en(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Dn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Kt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[Xk(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Zk(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[H2(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Pj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=dp.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=dp.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[dp.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[dp.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_j=(e,t,r)=>{switch(e.op){case"FFT":return[vm(k("x",e,t,r))];case"IFFT":return[Mp(k("x",e,t,r))];case"RFFT":return[wm(k("x",e,t,r))];case"IRFFT":return[V2(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zj=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=Hc.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=Hc.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[Hc.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Oj=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[Ht(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[et(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[_k(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Vn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[Am(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[um(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[bk(k("x",e,t,r),n,a)]}case"BroadcastTo":return[xp(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[hk(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function rv(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>cj(s,i,o));case"basic_math":return K(()=>fj(s,i,o));case"control":return bj(s,i,o);case"convolution":return K(()=>vj(s,i,o));case"creation":return K(()=>wj(s,i,o));case"dynamic":return kj(s,i,o);case"evaluation":return K(()=>Ij(s,i,o));case"image":return K(()=>Nj(s,i,o));case"graph":return K(()=>Sj(s,i,o));case"logical":return K(()=>Ej(s,i,o));case"matrices":return K(()=>Rj(s,i,o));case"normalization":return K(()=>Mj(s,i,o));case"reduction":return K(()=>Fj(s,i,o));case"slice_join":return K(()=>$j(s,i,o));case"sparse":return K(()=>Pj(s,i,o));case"spectral":return K(()=>_j(s,i,o));case"string":return K(()=>zj(s,i,o));case"transformation":return K(()=>Oj(s,i,o));case"hash_table":return Tj(s,i,o,n);case"custom":let l=P4(s.op);if(l&&l.customExecutor)return l.customExecutor(new hj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.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,r);return w.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var nv=class{constructor(e={},t={},r={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=r,this.functionMap=n,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(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let r=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(r))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.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)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let 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))}else throw new Error("Cannot increase frame iteration, the context is empty")}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(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function av(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>sn(p)[0]),d=[];n!=null&&(d=n.map(p=>sn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((r6(p)||Vj(p)||Uj(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Dj(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>sn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var Lj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Bj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Wj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function r6(e){return Lj.indexOf(e.op)>=0}function Vj(e){return Bj.indexOf(e.op)>=0}function Uj(e){return Wj.indexOf(e.op)>=0}var Ty=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new Ty(e.functions[r],this)})}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){let t=Object.keys(e).map(r=>e[r].map(n=>n.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=>{let 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),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=av(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)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 [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return Dj(this.graph,this.weightMap,r)}execute(e,t){e=this.mapInputs(e);let r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=r.map(d=>this.graph.nodes[sn(d)[0]]),a=t.map(d=>sn(d)[0]),s=a.map(d=>this.graph.nodes[d]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return K(()=>{let d=new nv(this.weightMap,l,u,this.functionExecutorMap),h={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=sn(f),y=[];y[g]=e[f],h[m]=y});let p=this.getFrozenTensorIds(h),c={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let g=rv(m,h,d,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=g,this.checkTensorForDisposal(m.name,m,h,d,p,a,c)}}return this.parent==null&&d.dispose(p),t.map(f=>_r(f,h,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(r=>e[r]).map(r=>r.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,r,n,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(r[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=jG(o.name,r,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!a.has(u.id)){let d=i[u.id];if(d===1){if(!this.keepTensorForDebug)u.dispose();else{let[h,p]=Ca(t.name,n);this.intermediateTensors[h]?this.intermediateTensors[h][p]=u:(this.intermediateTensors[h]=[],this.intermediateTensors[h][p]=u)}delete i[u.id]}else d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,r=!1,n={},a={}){r||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=J().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new nv(this.weightMap,n,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,r);let i=t.map(u=>_r(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,r){let n=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(n,this.outputNodes,!0,t,r)}async executeWithControlFlow(e,t,r,n){let a=Object.keys(e),s=a.map(A=>this.graph.nodes[sn(A)[0]]),i=r.map(A=>sn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:h}=av(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),c={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=sn(A),v=[];v[b]=e[A],c[x]=v});let f={},m=this.getFrozenTensorIds(c),g={};for(;p.length>0;){let A=this.processStack(s,p,t,c,g,m,i,f,l);await Promise.all(A)}d==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!r6(A)&&!_r(A.name,c,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${A}`)}return c}processStack(e,t,r,n,a,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();r.currentContext=d.contexts;let h="";if(d.node.op==="Enter"&&k("isConstant",d.node,n,r)&&([h]=Ca(d.node.name,r)),n[d.node.name]==null){let p=rv(d.node,n,r,this._resourceManager);h||([h]=Ca(d.node.name,r));let c=r.currentContext;w.isPromise(p)?u.push(p.then(f=>(n[h]=f,r.currentContext=c,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l),f))):(n[h]=p,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l))}else this.processChildNodes(d.node,t,r,n,a,l)}return u}processChildNodes(e,t,r,n,a,s){e.children.forEach(i=>{let[o]=Ca(i.name,r);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!_r(l,n,r))&&(a[o]=!0,t.push({contexts:r.currentContext,node:i})):i.inputNames.every(l=>!!_r(l,n,r))&&(a[o]=!0,t.push({contexts:r.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let r=e[t],[n]=sn(t),a=this.graph.nodes[n];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===r.shape.length&&r.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${r.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&w.assert(r.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(e){let t={};for(let r in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let n=this._signature.inputs[r];t[n.name]=e[r]}else t[r]=e[r];return t}checkInputs(e){let t=Object.keys(e).filter(r=>{let[n]=sn(r);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[r]=sn(t);if(!this.graph.nodes[r])throw new Error(`The output '${t}' is not found in the graph`)})}},Gj=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},jj="?tfjs-format=file",Hj="model.json",r0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Gj}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ir.browserHTTPRequest(e,this.loadOptions);else{let t=Ir.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ir.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Ir.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ty(J3.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=J3.Instance.transformGraph(e.modelInitializer);this.initializer=new Ty(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let r=Ir.getSaveHandlers(e);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${e}'`);e=r[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof rt)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,r,n)=>(t[r]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=this.executor.execute(e,t);return r.length>1?r:r[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=await this.executor.executeAsync(e,t);return r.length>1?r:r[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,r)=>(t[r]=[e[r]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function qj(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Hj}${jj}`);let r=new r0(e,t);return await r.load(),r}var Kj="0.0.0",n6={};Le(n6,{CSVDataset:()=>m6,Dataset:()=>pd,FileDataSource:()=>w6,TextLineDataset:()=>f6,URLDataSource:()=>k6,array:()=>yH,csv:()=>NH,func:()=>EH,generator:()=>RH,microphone:()=>FH,version_data:()=>$H,webcam:()=>MH,zip:()=>AH});var Xj=Eo(Ff()),Zj=Eo(Ff());function Yj(e,t){return wf(e,t)}function wf(e,t,r=new Map,n=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(n.has(e))throw new Error("Circular references are not supported.");if(r.has(e))return r.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(vu(e)){let s=Array.isArray(e)?[]:{};n.add(e);for(let i in e){let o=e[i],l=wf(o,t,r,n);s[i]=l}return n.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return r.set(e,a.value),a.value}function Jj(e,t=s6){return a6(e,t)}function a6(e,t,r=new Set){let n=e[0];if(r.has(n))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(vu(n)){let s=Array.isArray(n)?[]:{};r.add(n);for(let i in n){let o=e.map(u=>u[i]),l=a6(o,t,r);s[i]=l}return r.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return a.value}function s6(e){return e===null?null:vu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function i6(e,t){let r=new Map;wf(e,t,r);for(let n of Array.from(r.keys())){let a=r.get(n);if(w.isPromise(a)){let s=await a;r.set(n,s)}}return wf(e,t,r)}function vu(e){let t=!1;if(J().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:r}=Gv();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof rt)&&!(e instanceof Promise)&&!t)}function Qj(e){return e==null||eH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof rt||w.isTypedArray(e)}function eH(e){return e===null||typeof e!="object"&&typeof e!="function"}function tH(e){return Yj(e,rH)}function rH(e){return e instanceof rt?{value:e.clone(),recurse:!1}:vu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var o6=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},l6=class extends o6{constructor(){super(l6.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},u6=l6;u6.INITIAL_CAPACITY=32;function d6(e){return new sH(e)}function Cx(e){return new iH(e)}function nH(e,t){return new p6(e,t)}function aH(e,t=h6.FAIL){return new mH(e,t)}var gr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new cH(this,e)}filter(e){return new pH(this,e)}map(e){return new hH(this,e)}mapAsync(e){return new sv(this,e)}serialMapAsync(e){return new sv(this,e).serial()}flatmap(e){return new fH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new dH(this,e,t)}columnMajorBatch(e,t=!0,r=s6){return this.rowMajorBatch(e,t).map(n=>Jj(n,r))}concatenate(e,t){return new p6(d6([this,e]),t)}take(e){return e<0||e==null?this:new uH(this,e)}skip(e){return e<0||e==null?this:new lH(this,e)}prefetch(e){return new c6(this,e)}shuffle(e,t){return new gH(this,e,t)}serial(){return new oH(this)}},sH=class extends gr{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:tH(e),done:!1}}},iH=class extends gr{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},oH=class extends gr{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},lH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;re(e.value)}return this.upstream.next()}},uH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},dH=class extends gr{constructor(e,t,r=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},pH=class extends gr{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},hH=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ia.getTensorsInContainer(e.value),r=this.transform(e.value),n=ia.getTensorsInContainer(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},cH=class extends gr{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},sv=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ia.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ia.getTensorsInContainer(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Tx=class extends gr{constructor(){super();this.outputQueue=new u6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},fH=class extends Tx{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ia.getTensorsInContainer(e.value),r=this.transform(e.value),n=ia.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return!0}},p6=class extends gr{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},h6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(h6||{}),mH=class extends gr{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof gr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await i6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},c6=class extends gr{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new o6(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},gH=class extends c6{constructor(e,t,r){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Zj.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},pd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
-      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),an(async()=>(await r.iterator()).columnMajorBatch(e,t,xH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,an(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,an(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return an(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return an(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return an(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,an(async()=>{let n=Cx(async()=>({value:await t.iterator(),done:!1}));return nH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,an(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=Xj.alea(t||w.now().toString());return an(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,an(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};pd.MAX_BUFFER_SIZE=1e4;function an(e,t=null){return new class extends pd{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function yH(e){return an(async()=>d6(e),e.length)}function AH(e){if(!vu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return an(async()=>{let r=await i6(e,n=>{if(n instanceof pd)return{value:n.iterator(),recurse:!1};if(vu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return aH(r,1)},t)}function xH(e){if(e===null)return null;let t=e[0];return Qj(t)?{value:bH(e),recurse:!1}:{value:null,recurse:!0}}function bH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?sr(e):ct(e)}var f6=class extends pd{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
-`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Lc='"',op=Symbol("out"),iv=Symbol("field"),Bc=Symbol("quote"),W1=Symbol("quoteafterquote"),ov=Symbol("quoteinquote"),m6=class extends pd{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new f6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=op;for(let i=0;i<a;i++)switch(s){case op:switch(e.charAt(i)){case Lc:n=i+1,s=Bc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=op;break;default:s=iv,n=i;break}break;case iv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=op,n=i+1;break;default:}break;case Bc:switch(e.charAt(i)){case Lc:s=W1;break;default:}break;case W1:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=op,n=i+1;break;case Lc:s=Bc;break;default:s=ov;break}break;case ov:switch(e.charAt(i)){case Lc:s=Bc;break;default:}break;default:}if(s===W1?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},g6=class extends gr{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new g6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},y6=class extends gr{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=oa([s,a,o,i],[1,4])}else this.cropBox=oa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new y6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$n.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Ht(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},A6=class{},x6=class extends gr{split(e){return new vH(this,e)}},vH=class extends x6{constructor(e,t){super();this.upstream=e,this.impl=new wH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wH=class extends Tx{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},kH=class extends gr{decodeUTF8(){return new IH(this)}},IH=class extends x6{constructor(e){super();this.upstream=e,this.impl=new SH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SH=class extends Tx{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Gv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return J().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},b6=class extends kH{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},n.onabort=s=>t(new Error("Aborted")),n.onerror=s=>t(new Error(s.type));let a=this.file.slice(this.offset,r);n.readAsArrayBuffer(a)}this.offset=r}),done:!1}}};async function CH(e,t={},r){let n,a;typeof e=="string"?n=e:(n=e.url,a=TH(e));let s=await(r||w.fetch)(n,a);if(s.ok){let i=new Uint8Array(await s.arrayBuffer());return new b6(i,t)}else throw new Error(s.statusText)}var TH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function v6(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var w6=class extends A6{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(v6(this.input)&&J().get("IS_NODE")){let e=jy();this.input=e.readFileSync(this.input.substr(7))}return new b6(this.input,this.options)}},k6=class extends A6{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return v6(this.url)?new w6(this.url,this.fileOptions).iterator():CH(this.url,this.fileOptions)}};function NH(e,t={}){return new m6(new k6(e),t)}function EH(e){let t=Cx(e);return an(async()=>t)}function RH(e){return an(async()=>{let t=await e();return Cx(()=>t.next())})}async function MH(e,t){return y6.create(e,t)}async function FH(e){return g6.create(e)}var $H="0.0.0";function Te(e,t){Array.isArray(e)||(e=[e]),e.forEach(r=>{r!=null&&w.assert(r.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var PH=Un.whereImpl,I6=class extends Cu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Op(this,kr())}nextDataId(){return I6.nextDataId++}write(e,t,r){this.firstUse&&(this.firstUse=!1,J().get("IS_NODE")&&N.warn(`
+        ${n}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=Sy(s,r),o=n===0?0:e.size/n,l=K(()=>{let d=[];e=G(e,[1,n,o]);for(let h=0;h<t.length;++h){let p=h===0?0:a[h-1],c=[0,p,0],f=[1,t[h],o];d[h]=G(Pe(e,c,f),i)}return e.dispose(),d}),u=new wh([],r,e.dtype,t.length);for(let d=0;d<l.length;d++)u.setItem(d,l[d]);return u}var bj=async(e,t,r)=>{switch(e.op){case"If":case"StatelessIf":{let n=k("thenBranch",e,t,r),a=k("elseBranch",e,t,r),s=k("cond",e,t,r),i=k("args",e,t,r);return(await s.data())[0]?r.functionMap[n].executeFunctionAsync(i,r.tensorArrayMap,r.tensorListMap):r.functionMap[a].executeFunctionAsync(i,r.tensorArrayMap,r.tensorListMap)}case"While":case"StatelessWhile":{let n=k("body",e,t,r),a=k("cond",e,t,r),s=k("args",e,t,r),i=await r.functionMap[a].executeFunctionAsync(s,r.tensorArrayMap,r.tensorListMap),o=s.map(d=>d.id),l=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let u=s;for(;l[0];){let d=u;u=await 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n=k("elementShape",e,t,r),a=k("elementDType",e,t,r),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=k(s,e,t,r),o=yj(n,a,i);return r.addTensorList(o),[o.idTensor]}case"TensorListGather":{let n=k("tensorListId",e,t,r),a=k("indices",e,t,r),s=k("elementShape",e,t,r),i=k("elementDType",e,t,r);return[r.getTensorList(n.id).gather(a,i,s)]}case"TensorListStack":{let n=k("tensorListId",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=k("numElements",e,t,r);return[r.getTensorList(n.id).stack(a,s,i)]}case"TensorListFromTensor":{let n=k("tensor",e,t,r),a=k("elementShape",e,t,r),s=k("elementDType",e,t,r),i=gj(n,a,s);return r.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let n=k("tensorListId",e,t,r),a=r.getTensorList(n.id),s=k("dtype",e,t,r),i=k("elementShape",e,t,r);return[a.concat(s,i)]}case"TensorListPushBack":{let n=k("tensorListId",e,t,r),a=k("tensor",e,t,r),s=r.getTensorList(n.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{let 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g=k("leakyreluAlpha",e,t,r);return{stride:d,pad:h,dataFormat:p,dilations:c,biasArg:f,preluArg:m,activationFunc:a,leakyreluAlpha:g}}var vj=(e,t,r)=>{switch(e.op){case"Conv1D":{let n=k("stride",e,t,r),a=k("pad",e,t,r),s=k("dataFormat",e,t,r).toUpperCase(),i=k("dilation",e,t,r);return[A2(k("x",e,t,r),k("filter",e,t,r),n,a,s,i)]}case"Conv2D":{let 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n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r),i=k("includeBatchInIndex",e,t,r),{result:o,indexes:l}=Pk(k("x",e,t,r),[s[1],s[2]],[n[1],n[2]],a,i);return[o,l]}case"AvgPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[g2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"MaxPool3D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("kernelSize",e,t,r);return[R2(k("x",e,t,r),[s[1],s[2],s[3]],[n[1],n[2],n[3]],a)]}case"Dilation2D":{let n=k("strides",e,t,r),a=k("pad",e,t,r),s=k("dilations",e,t,r),i=n[1],o=n[2],l=s[1],u=s[2];return[vk(k("x",e,t,r),k("filter",e,t,r),[i,o],a,[l,u],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},wj=(e,t,r)=>{switch(e.op){case"Fill":{let n=k("shape",e,t,r),a=k("dtype",e,t,r),s=k("value",e,t,r);return[rd(n,s,a)]}case"LinSpace":{let n=k("start",e,t,r),a=k("stop",e,t,r),s=k("num",e,t,r);return[Tk(n,a,s)]}case"Multinomial":{let 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k("x",e,t,r).map(u=>St(u.shape));case"Size":return[Se(k("x",e,t,r).size,"int32")];case"Rank":return[Se(k("x",e,t,r).rank,"int32")];case"NoOp":return[Se(1)];case"Print":let s=k("x",e,t,r),i=k("data",e,t,r),o=k("message",e,t,r),l=k("summarize",e,t,r);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(o);for(let u=0;u<i.length;u++)console.log(Array.prototype.slice.call(i[u].dataSync()).slice(0,l));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Cj=class{constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=Se(0),this.tensorMap=new Map,hr(this.handle)}get id(){return this.handle.id}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Se(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),K(()=>{let n=en(t),a=r.length,s=n.length;w.assert(a===s,()=>`The number of elements doesn't match, keys has ${a} elements, the values has ${s} elements.`);for(let i=0;i<a;i++){let o=r[i],l=n[i];hr(l),this.tensorMap.set(o,l)}return this.handle})}async find(e,t){this.checkKeyAndValueTensor(e,t);let r=await e.data();return K(()=>{let n=[];for(let a=0;a<r.length;a++){let s=r[a],i=this.findWithDefault(s,t);n.push(i)}return sr(n)})}findWithDefault(e,t){let r=this.tensorMap.get(e);return r!=null?r:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}},Tj=async(e,t,r,n)=>{switch(e.op){case"HashTable":case"HashTableV2":{let a=k("keyDType",e,t,r),s=k("valueDType",e,t,r),i=new Cj(a,s);return 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n=k("image",e,t,r),a=k("boxes",e,t,r),s=k("boxInd",e,t,r),i=k("cropSize",e,t,r),o=k("method",e,t,r),l=k("extrapolationValue",e,t,r);return[Ie.cropAndResize(n,a,s,i,o,l)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ej=(e,t,r)=>{switch(e.op){case"Equal":return[Tn(k("a",e,t,r),k("b",e,t,r))];case"NotEqual":return[gu(k("a",e,t,r),k("b",e,t,r))];case"Greater":return[hn(k("a",e,t,r),k("b",e,t,r))];case"GreaterEqual":return[xl(k("a",e,t,r),k("b",e,t,r))];case"Less":return[S2(k("a",e,t,r),k("b",e,t,r))];case"LessEqual":return[bl(k("a",e,t,r),k("b",e,t,r))];case"LogicalAnd":return[pa(k("a",e,t,r),k("b",e,t,r))];case"LogicalNot":return[fm(k("a",e,t,r))];case"LogicalOr":return[E2(k("a",e,t,r),k("b",e,t,r))];case"Select":case"SelectV2":return[Lr(k("condition",e,t,r),k("a",e,t,r),k("b",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Rj=(e,t,r)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[Je(k("a",e,t,r),k("b",e,t,r),k("transposeA",e,t,r),k("transposeB",e,t,r))];case"Einsum":return[kk(k("equation",e,t,r),...k("tensors",e,t,r))];case"Transpose":return[nt(k("x",e,t,r),k("perm",e,t,r))];case"_FusedMatMul":let[n,a]=k("fusedOps",e,t,r),s=n==="biasadd",i=a==="prelu",o=k("numArgs",e,t,r),l=k("leakyreluAlpha",e,t,r);if(s){if(i&&o!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&o!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,d]=k("args",e,t,r);return[_s.matMul({a:k("a",e,t,r),b:k("b",e,t,r),transposeA:k("transposeA",e,t,r),transposeB:k("transposeB",e,t,r),bias:u,activation:a,preluActivationWeights:d,leakyreluAlpha:l})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Mj=(e,t,r)=>{switch(e.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[fu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"FusedBatchNormV3":return[fu(k("x",e,t,r),k("mean",e,t,r),k("variance",e,t,r),k("offset",e,t,r),k("scale",e,t,r),k("epsilon",e,t,r))];case"LRN":return[Nk(k("x",e,t,r),k("radius",e,t,r),k("bias",e,t,r),k("alpha",e,t,r),k("beta",e,t,r))];case"Softmax":return[id(k("x",e,t,r))];case"LogSoftmax":return[C2(k("x",e,t,r))];case"SparseToDense":return[j2(k("sparseIndices",e,t,r),k("outputShape",e,t,r),k("sparseValues",e,t,r),k("defaultValue",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Fj=(e,t,r)=>{switch(e.op){case"Max":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[fr(k("x",e,t,r),i,o)]}case"Mean":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[Bt(k("x",e,t,r),i,o)]}case"Min":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[$s(k("x",e,t,r),i,o)]}case"Sum":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[ke(k("x",e,t,r),i,o)]}case"All":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[f2(k("x",e,t,r),i,o)]}case"Any":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[pf(k("x",e,t,r),i,o)]}case"ArgMax":{let i=k("axis",e,t,r);return[Cn(k("x",e,t,r),i)]}case"ArgMin":{let i=k("axis",e,t,r);return[ek(k("x",e,t,r),i)]}case"Prod":{let i=k("axis",e,t,r),o=k("keepDims",e,t,r);return[M2(k("x",e,t,r),i,o)]}case"Cumsum":{let i=k("axis",e,t,r),o=k("exclusive",e,t,r),l=k("reverse",e,t,r);return[k2(k("x",e,t,r),i,o,l)]}case"Bincount":let n=k("x",e,t,r),a=k("weights",e,t,r),s=k("size",e,t,r);return[y2(n,a,s)];case"DenseBincount":{let i=k("x",e,t,r),o=k("weights",e,t,r),l=k("size",e,t,r),u=k("binaryOutput",e,t,r);return[xk(i,o,l,u)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},$j=(e,t,r)=>{switch(e.op){case"ConcatV2":case"Concat":{let n=k("n",e,t,r),a=k("axis",e,t,r),s=k("tensors",e,t,r);return s=s.slice(0,n),[kt(s,a)]}case"Gather":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[mu(n,me(a,"int32"),0)]}case"GatherV2":{let n=k("axis",e,t,r),a=k("batchDims",e,t,r),s=k("x",e,t,r),i=k("indices",e,t,r);return[mu(s,me(i,"int32"),n,a)]}case"Reverse":{let n=k("dims",e,t,r),a=[];for(let i=0;i<n.length;i++)n[i]&&a.push(i);let s=k("x",e,t,r);return[Mn(s,a)]}case"ReverseV2":{let n=k("axis",e,t,r),a=k("x",e,t,r);return[Mn(a,n)]}case"Slice":{let n=k("begin",e,t,r),a=k("size",e,t,r);return[Pe(k("x",e,t,r),n,a)]}case"StridedSlice":{let n=k("begin",e,t,r),a=k("end",e,t,r),s=k("strides",e,t,r),i=k("beginMask",e,t,r),o=k("endMask",e,t,r),l=k("ellipsisMask",e,t,r),u=k("newAxisMask",e,t,r),d=k("shrinkAxisMask",e,t,r),h=k("x",e,t,r);return[Vk(h,n,a,s,i,o,l,u,d)]}case"Pack":return K(()=>{let n=k("axis",e,t,r),a=k("tensors",e,t,r),s=a[0].shape,i=et(a[0]).shape,o=a.map(l=>{let u=w.arraysEqual(l.shape,s);if(!u&&!w.arraysEqual(et(l).shape,i))throw new Error("the input tensors shape does not match");return u?l:G(l,s)});return[sr(o,n)]});case"Unpack":{let n=k("axis",e,t,r),a=k("tensor",e,t,r);return en(a,n)}case"Tile":{let n=k("reps",e,t,r);return[Dn(k("x",e,t,r),n)]}case"Split":case"SplitV":{let n=k("axis",e,t,r),a=k("numOrSizeSplits",e,t,r),s=k("x",e,t,r);return Kt(s,a,n)}case"ScatterNd":{let n=k("indices",e,t,r),a=k("values",e,t,r),s=k("shape",e,t,r);return[Xk(n,a,s)]}case"GatherNd":{let n=k("x",e,t,r),a=k("indices",e,t,r);return[Zk(n,a)]}case"SparseToDense":{let n=k("sparseIndices",e,t,r),a=k("outputShape",e,t,r),s=k("sparseValues",e,t,r),i=k("defaultValue",e,t,r);return[j2(n,s,a,s.dtype===i.dtype?i:me(i,s.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Pj=(e,t,r)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:n,outputValues:a,emptyRowIndicator:s,reverseIndexMap:i}=up.sparseFillEmptyRows(k("indices",e,t,r),k("values",e,t,r),k("denseShape",e,t,r),k("defaultValue",e,t,r));return[n,a,s,i]}case"SparseReshape":{let{outputIndices:n,outputShape:a}=up.sparseReshape(k("inputIndices",e,t,r),k("inputShape",e,t,r),k("newShape",e,t,r));return[n,a]}case"SparseSegmentMean":return[up.sparseSegmentMean(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];case"SparseSegmentSum":return[up.sparseSegmentSum(k("data",e,t,r),k("indices",e,t,r),k("segmentIds",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},_j=(e,t,r)=>{switch(e.op){case"FFT":return[bm(k("x",e,t,r))];case"IFFT":return[Rp(k("x",e,t,r))];case"RFFT":return[vm(k("x",e,t,r))];case"IRFFT":return[W2(k("x",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},zj=(e,t,r)=>{switch(e.op){case"StringNGrams":{let{nGrams:n,nGramsSplits:a}=jc.stringNGrams(k("data",e,t,r),k("dataSplits",e,t,r),k("separator",e,t,r),k("nGramWidths",e,t,r),k("leftPad",e,t,r),k("rightPad",e,t,r),k("padWidth",e,t,r),k("preserveShortSequences",e,t,r));return[n,a]}case"StringSplit":{let{indices:n,values:a,shape:s}=jc.stringSplit(k("input",e,t,r),k("delimiter",e,t,r),k("skipEmpty",e,t,r));return[n,a,s]}case"StringToHashBucketFast":return[jc.stringToHashBucketFast(k("input",e,t,r),k("numBuckets",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Oj=(e,t,r)=>{switch(e.op){case"Cast":return[me(k("x",e,t,r),k("dtype",e,t,r))];case"ExpandDims":{let n=k("axis",e,t,r);return[Ht(k("x",e,t,r),n)]}case"Squeeze":{let n=k("axis",e,t,r);return[et(k("x",e,t,r),n)]}case"Reshape":return[G(k("x",e,t,r),k("shape",e,t,r))];case"MirrorPad":return[_k(k("x",e,t,r),k("padding",e,t,r),k("mode",e,t,r))];case"PadV2":case"Pad":return[Vn(k("x",e,t,r),k("padding",e,t,r),k("constantValue",e,t,r))];case"SpaceToBatchND":{let n=k("blockShape",e,t,r),a=k("paddings",e,t,r);return[ym(k("x",e,t,r),n,a)]}case"BatchToSpaceND":{let n=k("blockShape",e,t,r),a=k("crops",e,t,r);return[lm(k("x",e,t,r),n,a)]}case"DepthToSpace":{let n=k("blockSize",e,t,r),a=k("dataFormat",e,t,r).toUpperCase();return[bk(k("x",e,t,r),n,a)]}case"BroadcastTo":return[Ap(k("x",e,t,r),k("shape",e,t,r))];case"BroadcastArgs":return[hk(k("s0",e,t,r),k("s1",e,t,r))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function tv(e,t,r,n){let a=((s,i,o)=>{switch(s.category){case"arithmetic":return K(()=>cj(s,i,o));case"basic_math":return K(()=>fj(s,i,o));case"control":return bj(s,i,o);case"convolution":return K(()=>vj(s,i,o));case"creation":return K(()=>wj(s,i,o));case"dynamic":return kj(s,i,o);case"evaluation":return K(()=>Ij(s,i,o));case"image":return K(()=>Nj(s,i,o));case"graph":return K(()=>Sj(s,i,o));case"logical":return K(()=>Ej(s,i,o));case"matrices":return K(()=>Rj(s,i,o));case"normalization":return K(()=>Mj(s,i,o));case"reduction":return K(()=>Fj(s,i,o));case"slice_join":return K(()=>$j(s,i,o));case"sparse":return K(()=>Pj(s,i,o));case"spectral":return K(()=>_j(s,i,o));case"string":return K(()=>zj(s,i,o));case"transformation":return K(()=>Oj(s,i,o));case"hash_table":return Tj(s,i,o,n);case"custom":let l=P4(s.op);if(l&&l.customExecutor)return l.customExecutor(new hj(s,i,o));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.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,r);return w.isPromise(a)?a.then(s=>[].concat(s)):[].concat(a)}var rv=class{constructor(e={},t={},r={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=r,this.functionMap=n,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(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let r=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(r))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.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)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let 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))}else throw new Error("Cannot increase frame iteration, the context is empty")}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(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function nv(e,t,r,n){let a=new Set,s=[],i=null,o=null,l=new Set,u=Object.keys(e).map(p=>sn(p)[0]),d=[];n!=null&&(d=n.map(p=>sn(p.name)[0]));let h=[...t];for(;h.length>0;){let p=h.pop();if((r6(p)||Vj(p)||Uj(p))&&i==null&&(i=p,o=i.children.map(c=>c.name).filter(c=>a.has(c))),a.add(p.name),r[p.name]==null&&u.indexOf(p.name)===-1&&d.indexOf(p.name)===-1){if(p.inputs.length===0){s.push(p.name);continue}p.inputs.forEach(c=>{l.has(c.name)||(l.add(c.name),h.push(c))})}}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:i,syncInputs:o}}function Dj(e,t,r){let{usedNodes:n,inputs:a}=r,s=[],i=Object.keys(a).map(d=>sn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{n.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{n.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{n.has(d.name)&&s.push(d)});let l=new Set,u=[];for(;s.length>0;){let d=s.pop();l.add(d.name),t[d.name]||u.push(d),d.children.forEach(h=>{!l.has(h.name)&&n.has(h.name)&&h.inputs.every(p=>l.has(p.name))&&s.push(h)})}return u}var Lj=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Bj=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Wj=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function r6(e){return Lj.indexOf(e.op)>=0}function Vj(e){return Bj.indexOf(e.op)>=0}function Uj(e){return Wj.indexOf(e.op)>=0}var Cy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(r=>{this._functionExecutorMap[r]=new Cy(e.functions[r],this)})}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){let t=Object.keys(e).map(r=>e[r].map(n=>n.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=>{let 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),{})}getCompilationKey(e,t){let r=e.map(a=>a.name).sort(),n=t.map(a=>a.name).sort();return r.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let r=nv(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:a,syncInputs:s}=r;if(a!=null)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 [${s}]`);if(n.length>0){let i=t.map(l=>l.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${n}]`)}return Dj(this.graph,this.weightMap,r)}execute(e,t){e=this.mapInputs(e);let r=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=r.map(d=>this.graph.nodes[sn(d)[0]]),a=t.map(d=>sn(d)[0]),s=a.map(d=>this.graph.nodes[d]);this.resetIntermediateTensors(),s.length===0&&(s=this._outputs);let i=this.getCompilationKey(n,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let l={},u={};return K(()=>{let d=new rv(this.weightMap,l,u,this.functionExecutorMap),h={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=sn(f),y=[];y[g]=e[f],h[m]=y});let p=this.getFrozenTensorIds(h),c={};for(let f=0;f<o.length;f++){let m=o[f];if(!h[m.name]){let g=tv(m,h,d,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);h[m.name]=g,this.checkTensorForDisposal(m.name,m,h,d,p,a,c)}}return this.parent==null&&d.dispose(p),t.map(f=>_r(f,h,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(r=>e[r]).map(r=>r.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,r,n,a,s,i){t.category==="control"||s.indexOf(e)!==-1||(r[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let l=jG(o.name,r,n);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!a.has(u.id)){let d=i[u.id];if(d===1){if(!this.keepTensorForDebug)u.dispose();else{let[h,p]=Ca(t.name,n);this.intermediateTensors[h]?this.intermediateTensors[h][p]=u:(this.intermediateTensors[h]=[],this.intermediateTensors[h][p]=u)}delete i[u.id]}else d!=null&&i[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(t=>{t&&!t.kept&&!t.isDisposed&&!this.keepIds.has(t.id)&&t.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,r=!1,n={},a={}){r||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=J().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let s=new rv(this.weightMap,n,a,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,s,t,r);let i=t.map(u=>_r(u,this.tensorsMap,s)),o=i.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...o,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&s.dispose(this.keepIds),i}async executeFunctionAsync(e,t,r){let n=e.reduce((a,s,i)=>(a[this.inputs[i].name]=s,a),{});return this._executeAsync(n,this.outputNodes,!0,t,r)}async executeWithControlFlow(e,t,r,n){let a=Object.keys(e),s=a.map(A=>this.graph.nodes[sn(A)[0]]),i=r.map(A=>sn(A)[0]),o=i.map(A=>this.graph.nodes[A]);o.length===0&&(o=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:d,syncInputs:h}=nv(e,o,this.weightMap,this._initNodes),p=[...s,...this.graph.weights,...this._initNodes||[]].map(A=>({node:A,contexts:t.currentContext})),c={...this.weightMap};Object.keys(e).forEach(A=>{let[x,b]=sn(A),v=[];v[b]=e[A],c[x]=v});let f={},m=this.getFrozenTensorIds(c),g={};for(;p.length>0;){let A=this.processStack(s,p,t,c,g,m,i,f,l);await Promise.all(A)}d==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(A=>!r6(A)&&!_r(A.name,c,t)).map(A=>A.name);if(y.length>0){let A="";throw d!=null&&(A=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${h}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${a}]. Consider providing the following inputs: [${u}]. ${A}`)}return c}processStack(e,t,r,n,a,s,i,o,l){let u=[];for(;t.length>0;){let d=t.pop();r.currentContext=d.contexts;let h="";if(d.node.op==="Enter"&&k("isConstant",d.node,n,r)&&([h]=Ca(d.node.name,r)),n[d.node.name]==null){let p=tv(d.node,n,r,this._resourceManager);h||([h]=Ca(d.node.name,r));let c=r.currentContext;w.isPromise(p)?u.push(p.then(f=>(n[h]=f,r.currentContext=c,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l),f))):(n[h]=p,this.checkTensorForDisposal(h,d.node,n,r,s,i,o),this.processChildNodes(d.node,t,r,n,a,l))}else this.processChildNodes(d.node,t,r,n,a,l)}return u}processChildNodes(e,t,r,n,a,s){e.children.forEach(i=>{let[o]=Ca(i.name,r);a[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(l=>!!_r(l,n,r))&&(a[o]=!0,t.push({contexts:r.currentContext,node:i})):i.inputNames.every(l=>!!_r(l,n,r))&&(a[o]=!0,t.push({contexts:r.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let r=e[t],[n]=sn(t),a=this.graph.nodes[n];if(a.attrParams.shape&&a.attrParams.shape.value){let s=a.attrParams.shape.value,i=s.length===r.shape.length&&r.shape.every((o,l)=>s[l]===-1||s[l]===o);w.assert(i,()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${s}], but was [${r.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&w.assert(r.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${r.dtype}`)})}mapInputs(e){let t={};for(let r in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[r]!=null){let n=this._signature.inputs[r];t[n.name]=e[r]}else t[r]=e[r];return t}checkInputs(e){let t=Object.keys(e).filter(r=>{let[n]=sn(r);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[r]=sn(t);if(!this.graph.nodes[r])throw new Error(`The output '${t}' is not found in the graph`)})}},Gj=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},jj="?tfjs-format=file",Hj="model.json",t0=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new Gj}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Ir.browserHTTPRequest(e,this.loadOptions);else{let t=Ir.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Ir.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,r;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?r=this.artifacts.userDefinedMetadata.signature:r=this.artifacts.signature,this.signature=r,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Ir.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Cy(Y3.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let a=Y3.Instance.transformGraph(e.modelInitializer);this.initializer=new Cy(a),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let r=Ir.getSaveHandlers(e);if(r.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(r.length>1)throw new Error(`Found more than one (${r.length}) save handlers for URL '${e}'`);e=r[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof rt)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,r,n)=>(t[r]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=this.executor.execute(e,t);return r.length>1?r:r[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let r=await this.executor.executeAsync(e,t);return r.length>1?r:r[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,r)=>(t[r]=[e[r]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function qj(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${Hj}${jj}`);let r=new t0(e,t);return await r.load(),r}var Kj="0.0.0",n6={};Le(n6,{CSVDataset:()=>m6,Dataset:()=>dd,FileDataSource:()=>w6,TextLineDataset:()=>f6,URLDataSource:()=>k6,array:()=>yH,csv:()=>NH,func:()=>EH,generator:()=>RH,microphone:()=>FH,version_data:()=>$H,webcam:()=>MH,zip:()=>AH});var Xj=Eo(Mf()),Zj=Eo(Mf());function Yj(e,t){return vf(e,t)}function vf(e,t,r=new Map,n=new Set){if(e==null)return null;if(typeof Blob=="function"&&e instanceof Blob)return e.slice();if(n.has(e))throw new Error("Circular references are not supported.");if(r.has(e))return r.get(e);let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(a.recurse)if(bu(e)){let s=Array.isArray(e)?[]:{};n.add(e);for(let i in e){let o=e[i],l=vf(o,t,r,n);s[i]=l}return n.delete(e),e.__proto__&&(s.__proto__=e.__proto__),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return r.set(e,a.value),a.value}function Jj(e,t=s6){return a6(e,t)}function a6(e,t,r=new Set){let n=e[0];if(r.has(n))throw new Error("Circular references are not supported.");let a=t(e);if(a.recurse&&a.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(a.recurse)if(bu(n)){let s=Array.isArray(n)?[]:{};r.add(n);for(let i in n){let o=e.map(u=>u[i]),l=a6(o,t,r);s[i]=l}return r.delete(n),s}else throw new Error(`Can't recurse into non-iterable type: ${n}`);else return a.value}function s6(e){return e===null?null:bu(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function i6(e,t){let r=new Map;vf(e,t,r);for(let n of Array.from(r.keys())){let a=r.get(n);if(w.isPromise(a)){let s=await a;r.set(n,s)}}return vf(e,t,r)}function bu(e){let t=!1;if(J().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:r}=Gv();t=e instanceof r}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof rt)&&!(e instanceof Promise)&&!t)}function Qj(e){return e==null||eH(e)||Array.isArray(e)||typeof e=="object"&&e instanceof rt||w.isTypedArray(e)}function eH(e){return e===null||typeof e!="object"&&typeof e!="function"}function tH(e){return Yj(e,rH)}function rH(e){return e instanceof rt?{value:e.clone(),recurse:!1}:bu(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var o6=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),r=this.get(t);return this.set(t,this.pop()),r}},l6=class extends o6{constructor(){super(l6.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),r=this.length();for(let n=0;n<r;n++)t[n]=this.get(this.wrap(this.begin+n));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=r}},u6=l6;u6.INITIAL_CAPACITY=32;function d6(e){return new sH(e)}function Sx(e){return new iH(e)}function nH(e,t){return new p6(e,t)}function aH(e,t=h6.FAIL){return new mH(e,t)}var gr=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],r=await e.next();for(;!r.done;)t.push(r.value),r=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),r=e(t.value);for(;!t.done&&r;)t=await this.next(),r=e(t.value)}handleErrors(e){return new cH(this,e)}filter(e){return new pH(this,e)}map(e){return new hH(this,e)}mapAsync(e){return new av(this,e)}serialMapAsync(e){return new av(this,e).serial()}flatmap(e){return new fH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new dH(this,e,t)}columnMajorBatch(e,t=!0,r=s6){return this.rowMajorBatch(e,t).map(n=>Jj(n,r))}concatenate(e,t){return new p6(d6([this,e]),t)}take(e){return e<0||e==null?this:new uH(this,e)}skip(e){return e<0||e==null?this:new lH(this,e)}prefetch(e){return new c6(this,e)}shuffle(e,t){return new gH(this,e,t)}serial(){return new oH(this)}},sH=class extends gr{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:tH(e),done:!1}}},iH=class extends gr{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},oH=class extends gr{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},lH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;re(e.value)}return this.upstream.next()}},uH=class extends gr{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},dH=class extends gr{constructor(e,t,r=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=r,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},pH=class extends gr{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;re(e.value)}}},hH=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ia.getTensorsInContainer(e.value),r=this.transform(e.value),n=ia.getTensorsInContainer(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},cH=class extends gr{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},av=class extends gr{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ia.getTensorsInContainer(e.value),r=await this.transform(e.value),n=ia.getTensorsInContainer(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return{value:r,done:!1}}},Cx=class extends gr{constructor(){super();this.outputQueue=new u6,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},fH=class extends Cx{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ia.getTensorsInContainer(e.value),r=this.transform(e.value),n=ia.getTensorsInContainer(r);this.outputQueue.pushAll(r);for(let a of t)ia.isTensorInList(a,n)||a.dispose();return!0}},p6=class extends gr{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let r=await this.moreIterators.next();if(r.done)return{value:null,done:!0};this.iterator=r.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},h6=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(h6||{}),mH=class extends gr{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,r=0;function n(s){return s instanceof gr?{value:s.next().then(i=>(t++,i.done&&r++,i.value)),recurse:!1}:{value:null,recurse:!0}}let a=await i6(this.iterators,n);if(t===r)return{value:null,done:!0};if(r>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:a,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},c6=class extends gr{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new o6(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},gH=class extends c6{constructor(e,t,r){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Zj.alea(r||w.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},dd=class{constructor(){this.size=null}batch(e,t=!0){let r=this;w.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let n;return this.size===1/0||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),an(async()=>(await r.iterator()).columnMajorBatch(e,t,xH),n)}concatenate(e){let t=this,r;return this.size===1/0||e.size===1/0?r=1/0:this.size!=null&&e.size!=null?r=this.size+e.size:r=null,an(async()=>(await t.iterator()).concatenate(await e.iterator()),r)}filter(e){let t=this,r;return this.size===1/0?r=1/0:r=null,an(async()=>(await t.iterator()).filter(n=>K(()=>e(n))),r)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return an(async()=>(await t.iterator()).map(r=>K(()=>e(r))),this.size)}mapAsync(e){let t=this;return an(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return an(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,r;return this.size!=null&&e>0?r=this.size*e:e===0?r=0:this.size!=null&&(e===void 0||e<0)?r=1/0:r=null,an(async()=>{let n=Sx(async()=>({value:await t.iterator(),done:!1}));return nH(n.take(e))},r)}skip(e){let t=this,r;return this.size!=null&&e>=0&&this.size>=e?r=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?r=0:r=null,an(async()=>(await t.iterator()).skip(e),r)}shuffle(e,t,r=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let n=this,a=Xj.alea(t||w.now().toString());return an(async()=>{let s=a.int32();return r&&(s+=a.int32()),(await n.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,r;return this.size!=null&&this.size>e?r=e:this.size!=null&&this.size<=e?r=this.size:r=null,an(async()=>(await t.iterator()).take(e),r)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};dd.MAX_BUFFER_SIZE=1e4;function an(e,t=null){return new class extends dd{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function yH(e){return an(async()=>d6(e),e.length)}function AH(e){if(!bu(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let r=0;r<e.length;r++)t=t==null?e[r].size:Math.min(t,e[r].size);else if(e instanceof Object)for(let r in e)t=t==null?e[r].size:Math.min(t,e[r].size);return an(async()=>{let r=await i6(e,n=>{if(n instanceof dd)return{value:n.iterator(),recurse:!1};if(bu(n))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return aH(r,1)},t)}function xH(e){if(e===null)return null;let t=e[0];return Qj(t)?{value:bH(e),recurse:!1}:{value:null,recurse:!0}}function bH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof rt?sr(e):ct(e)}var f6=class extends dd{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
+`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},Dc='"',ip=Symbol("out"),sv=Symbol("field"),Lc=Symbol("quote"),B1=Symbol("quoteafterquote"),iv=Symbol("quoteinquote"),m6=class extends dd{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new f6(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((n,a)=>(n[a]=n[a]+1||1,n),{}),r=Object.keys(t).filter(n=>t[n]>1);if(w.assert(r.length===0,()=>"Duplicate column names found: "+r.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),r={},n={};for(let a=0;a<this.fullColumnNames.length;a++){let s=this.fullColumnNames[a],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[a],l=null;if(o==="")if(i&&i.default!==void 0)l=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);l=void 0}else{let u=Number(o);if(isNaN(u))i&&i.dtype==="bool"?l=this.getBoolean(o):l=o;else if(!i||!i.dtype)l=u;else switch(i.dtype){case"float32":l=u;break;case"int32":l=Math.floor(u);break;case"bool":l=this.getBoolean(o);break;default:l=u}}i&&i.isLabel?n[s]=l:r[s]=l}}return Object.keys(n).length===0?r:{xs:r,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let r=[],n=0,a=e.length,s=ip;for(let i=0;i<a;i++)switch(s){case ip:switch(e.charAt(i)){case Dc:n=i+1,s=Lc;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;r.push(""),s=ip;break;default:s=sv,n=i;break}break;case sv:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i)),s=ip,n=i+1;break;default:}break;case Lc:switch(e.charAt(i)){case Dc:s=B1;break;default:}break;case B1:switch(e.charAt(i)){case this.delimiter:r.push(e.substring(n,i-1)),s=ip,n=i+1;break;case Dc:s=Lc;break;default:s=iv;break}break;case iv:switch(e.charAt(i)){case Dc:s=Lc;break;default:}break;default:}if(s===B1?r.push(e.substring(n,a-1)):r.push(e.substring(n)),t&&r.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${r}`);return r}},g6=class extends gr{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(J().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new g6(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(r){throw new Error(`Error thrown while initializing video stream: ${r.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,r=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(r.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(r.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],r=0;return new Promise(n=>{let a=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++r===this.numFrames&&(clearInterval(a),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,r=new Float32Array(e.length*t);return e.forEach((n,a)=>r.set(n,a*t)),r}getTensorFromAudioDataArray(e,t){let r=new Float32Array(w.sizeFromShape(t));return r.set(e,r.length-e.length),ct(r,t)}},y6=class extends gr{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=St([0],"int32"),this.webcamConfig.centerCrop){let r=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,a=(1-r)/2,s=(1-n)/2,i=a+r,o=n+s;this.cropBox=oa([s,a,o,i],[1,4])}else this.cropBox=oa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(J().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let r=new y6(e,t);return await r.start(),r}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=$n.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Ht(me(e,"float32"),0),r;r=Ie.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=r.shape;return G(r,n.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},A6=class{},x6=class extends gr{split(e){return new vH(this,e)}},vH=class extends x6{constructor(e,t){super();this.upstream=e,this.impl=new wH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},wH=class extends Cx{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let r of t.slice(0,-1))this.outputQueue.push(r);return this.carryover=t[t.length-1],!0}},kH=class extends gr{decodeUTF8(){return new IH(this)}},IH=class extends x6{constructor(e){super();this.upstream=e,this.impl=new SH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},SH=class extends Cx{constructor(e){super();if(this.upstream=e,J().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Gv();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let r;return J().get("IS_BROWSER")?r=this.decoder.decode(t,{stream:!0}):r=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(r),!0}},b6=class extends kH{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(J().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((e,t)=>{let r=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,r)));else{let n=new FileReader;n.onload=s=>{let i=n.result;if(i instanceof 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I=Math.min(t.inWidth,h+_),O=Number.NEGATIVE_INFINITY,z=-1;for(let j=x;j<b;j+=i){let X=j-A;for(let D=S;D<E;D+=o){let Q=D-C;for(let V=M;V<I;V+=l){let ee=V-_,Y=e.get(m,j,D,V,g);Y>=O&&(O=Y,z=X*d*h+Q*d+ee)}}}r.set(z,m,y,v,R,g)}}}return r}function yK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;Te(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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d=N.computePool3DInfo(s.shape,i,o,1,l,u),h=d.strideDepth,p=d.strideHeight,c=d.strideWidth,f=d.filterDepth,m=d.filterHeight,g=d.filterWidth,y=d.dilationDepth,A=d.dilationHeight,x=d.dilationWidth,b=d.effectiveFilterDepth,v=d.effectiveFilterHeight,C=d.effectiveFilterWidth,S=b-1-d.padInfo.front,E=C-1-d.padInfo.left,R=v-1-d.padInfo.top,_=We(s.shape,"float32"),M=1/(f*m*g),I=r.bufferSync(a);for(let O=0;O<d.batchSize;++O)for(let z=0;z<d.inChannels;++z)for(let j=0;j<d.inDepth;++j)for(let X=0;X<d.inHeight;++X)for(let D=0;D<d.inWidth;++D){let Q=j-S,V=X-R,ee=D-E,Y=0;for(let se=0;se<b;se+=y){let Z=(Q+se)/h;if(!(Z<0||Z>=d.outDepth||Math.floor(Z)!==Z))for(let ae=0;ae<v;ae+=A){let de=(V+ae)/p;if(!(de<0||de>=d.outHeight||Math.floor(de)!==de))for(let Ae=0;Ae<C;Ae+=x){let be=(ee+Ae)/c;be<0||be>=d.outWidth||Math.floor(be)!==be||(Y+=I.get(O,Z,de,be,z))}}}_.set(Y*M,O,j,X,D,z)}return r.makeTensorInfo(_.shape,_.dtype,_.values)}var wK={kernelName:zf,backendName:"cpu",kernelFunc:vK};function kK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Te([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,v=We(i.shape,"float32"),C=1/(c*f),S=r.data.get(a.dataId).values,E=We(a.shape,"float32",S);for(let R=0;R<d.batchSize;++R)for(let _=0;_<d.inChannels;++_)for(let M=0;M<d.inHeight;++M)for(let I=0;I<d.inWidth;++I){let O=M-b,z=I-x,j=0;for(let X=0;X<y;X+=m){let D=(O+X)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Q=0;Q<A;Q+=g){let V=(z+Q)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,_))}}v.set(j*C,R,M,I,_)}return r.makeTensorInfo(v.shape,v.dtype,v.values)}var IK={kernelName:_f,backendName:"cpu",kernelFunc:kK};function 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r.makeTensorInfo(a.shape,a.dtype,m)}var CK={kernelName:ai,backendName:"cpu",kernelFunc:SK};function TK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Fn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=So({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var NK={kernelName:Fo,backendName:"cpu",kernelFunc:TK};function EK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Rx(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var 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rr(p.outShape,a.dtype),v=w.computeStrides(a.shape),C=w.computeStrides(s.shape),S=v[0],E=x?v[1]:v[2],R=x?v[2]:1,_=x?1:v[1],M=b.strides[0],I=x?b.strides[1]:b.strides[2],O=x?b.strides[2]:1,z=x?1:b.strides[1],j=r.data.get(a.dataId).values,X=r.data.get(s.dataId).values,D=b.values;for(let Q=0;Q<p.batchSize;++Q){let V=Q*S,ee=Q*M;for(let Y=0;Y<p.outHeight;++Y){let se=ee+Y*I,Z=Y*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=Z+ae*m;if(de<0||de>=p.inHeight)continue;let Ae=ae*C[0],be=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let Me=se+Ee*O,De=Ee*p.strideWidth-y;for(let Be=0;Be<f;++Be){let Ze=De+Be*g;if(Ze<0||Ze>=p.inWidth)continue;let ot=Ae+Be*C[1],dt=be+Ze*R,pt=ot;for(let $e=0;$e<p.inChannels;++$e){let vt=j[dt+$e*_];for(let gt=0;gt<p.outChannels;++gt)D[Me+gt*z]+=vt*X[pt+gt];pt+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var LK={kernelName:Ks,backendName:"cpu",kernelFunc:yI};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"conv2dBackpropFilter");let h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new rr(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=r.data.get(a.dataId).values,C=r.data.get(s.dataId).values,S=new rr(a.shape,a.dtype,v),E=new rr(s.shape,s.dtype,C);for(let R=0;R<m;++R){let _=Math.max(0,Math.ceil((b-R)/c)),M=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let I=0;I<g;++I){let O=Math.max(0,Math.ceil((x-I)/f)),z=Math.min(p.outWidth,(p.inWidth+x-I)/f);for(let j=0;j<p.inChannels;++j)for(let X=0;X<p.outChannels;++X){let D=0;for(let Q=0;Q<p.batchSize;++Q)for(let V=_;V<M;++V){let ee=R+V*c-b;for(let Y=O;Y<z;++Y){let se=I+Y*f-x;y?D+=S.get(Q,ee,se,j)*E.get(Q,V,Y,X):D+=S.get(Q,j,ee,se)*E.get(Q,X,V,Y)}}A.set(D,R,I,j,X)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var WK={kernelName:Lf,backendName:"cpu",kernelFunc:BK};function VK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Te([a,s],"conv2dBackpropInput");let h=w.computeStrides(s.shape),p=w.computeStrides(a.shape),c=N.convertConv2DDataFormat(u),f=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),m=new rr(f.inShape,"float32"),g=m.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,v]=h,{batchSize:C,filterHeight:S,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:I,outHeight:O,outWidth:z,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let D=S-1-f.padInfo.top,Q=E-1-f.padInfo.left,V=c==="channelsLast",ee=m.strides[0],Y=V?m.strides[1]:m.strides[2],se=V?m.strides[2]:1,Z=V?1:m.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<C;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<_;++De){let Be=De-D,Ze=Math.max(0,Math.ceil(Be/j)),ot=Math.min(O,(S+Be)/j);for(let dt=0;dt<M;++dt){let pt=dt-Q,$e=Math.max(0,Math.ceil(pt/X)),vt=Math.min(z,(E+pt)/X),gt=0;for(let ur=Ze;ur<ot;++ur){let Xr=ur*j-Be;for(let Jt=$e;Jt<vt;++Jt){let dr=Jt*X-pt,Xn=ae*Ee+de*ur+Ae*Jt,Zr=x*(S-1-Xr)+b*(E-1-dr)+v*Me;for(let Qt=0;Qt<I;++Qt){let xn=y[Xn+be*Qt],bn=A[Zr+Qt];gt+=xn*bn}}}let Rr=ee*Ee+Y*De+se*dt+Z*Me;g[Rr]=gt}}return r.makeTensorInfo(m.shape,m.dtype,m.values)}var UK={kernelName:Xs,backendName:"cpu",kernelFunc:VK};function GK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Te([a,s],"conv3d");let u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:f,dilationWidth:m,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new rr(u.outShape,a.dtype),v=r.data.get(a.dataId).values,C=r.data.get(s.dataId).values,S=b.values,E=w.computeStrides(a.shape),R=w.computeStrides(s.shape);for(let _=0;_<u.batchSize;++_){let M=_*E[0],I=_*b.strides[0];for(let O=0;O<u.outDepth;++O){let z=I+O*b.strides[1],j=O*u.strideDepth-y;for(let X=0;X<d;++X){let D=j+X*c;if(D<0||D>=u.inDepth)continue;let Q=X*R[0],V=M+D*E[1];for(let ee=0;ee<u.outHeight;++ee){let Y=z+ee*b.strides[2],se=ee*u.strideHeight-x;for(let Z=0;Z<h;++Z){let ae=se+Z*f;if(ae<0||ae>=u.inHeight)continue;let de=Q+Z*R[1],Ae=V+ae*E[2];for(let be=0;be<u.outWidth;++be){let Ee=Y+be*u.outChannels,Me=be*u.strideWidth-A;for(let De=0;De<p;++De){let Be=Me+De*m;if(Be<0||Be>=u.inWidth)continue;let Ze=de+De*R[2],ot=Ae+Be*u.inChannels,dt=Ze;for(let pt=0;pt<u.inChannels;++pt){let $e=v[ot+pt];for(let vt=0;vt<u.outChannels;++vt)S[Ee+vt]+=$e*C[dt+vt];dt+=u.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var jK={kernelName:Wp,backendName:"cpu",kernelFunc:GK};function HK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Te([a,s],"conv3dBackpropFilterV2");let u=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=N.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,f=h.strideWidth,m=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new rr(h.filterShape,"float32"),x=A.values,[b,v,C,S]=A.strides,E=r.data.get(s.dataId).values,[R,_,M,I]=d,O=r.data.get(a.dataId).values,[z,j,X,D]=u,Q=h.padInfo.front,V=h.padInfo.left,ee=h.padInfo.top;for(let Y=0;Y<m;++Y){let se=Math.max(0,Math.ceil((Q-Y)/p)),Z=Math.min(h.outDepth,(h.inDepth+Q-Y)/p),ae=Y*b;for(let de=0;de<g;++de){let Ae=Math.max(0,Math.ceil((ee-de)/c)),be=Math.min(h.outHeight,(h.inHeight+ee-de)/c),Ee=de*v+ae;for(let Me=0;Me<y;++Me){let De=Math.max(0,Math.ceil((V-Me)/f)),Be=Math.min(h.outWidth,(h.inWidth+V-Me)/f),Ze=Me*C+Ee;for(let ot=0;ot<h.inChannels;++ot){let dt=ot*S+Ze;for(let pt=0;pt<h.outChannels;++pt){let $e=0;for(let vt=0;vt<h.batchSize;++vt){let gt=vt*z,Rr=vt*R;for(let ur=se;ur<Z;++ur){let Xr=(Y+ur*p-Q)*j+gt,Jt=ur*_+Rr;for(let dr=Ae;dr<be;++dr){let 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eX(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,f=s.shape[0],[m,g]=o,y=We([f,m,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,v=w.computeStrides(a.shape),C=w.computeStrides(y.shape);for(let S=0;S<f;S++){let E=S*4,R=A[E],_=A[E+1],M=A[E+2],I=A[E+3],O=x[S];if(O>=d)continue;let z=m>1?(M-R)*(h-1)/(m-1):0,j=g>1?(I-_)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let D=m>1?R*(h-1)+X*z:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Q=0;Q<g;Q++)for(let V=0;V<c;V++){let ee=V+Q*C[2]+X*C[1]+S*C[0];y.values[ee]=u}continue}if(l==="bilinear"){let Q=Math.floor(D),V=Math.ceil(D),ee=D-Q;for(let Y=0;Y<g;Y++){let se=g>1?_*(p-1)+Y*j:.5*(_+I)*(p-1);if(se<0||se>p-1){for(let Ae=0;Ae<c;Ae++){let be=Ae+Y*C[2]+X*C[1]+S*C[0];y.values[be]=u}continue}let Z=Math.floor(se),ae=Math.ceil(se),de=se-Z;for(let Ae=0;Ae<c;Ae++){let be=Ae+Z*v[2]+Q*v[1]+O*v[0],Ee=b[be];be=Ae+ae*v[2]+Q*v[1]+O*v[0];let 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h=Dr(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=Fn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var nX={kernelName:Po,backendName:"cpu",kernelFunc:rX};function aX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Rx(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=T6(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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kK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;Te([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=d.strideHeight,p=d.strideWidth,c=d.filterHeight,f=d.filterWidth,m=d.dilationHeight,g=d.dilationWidth,y=d.effectiveFilterHeight,A=d.effectiveFilterWidth,x=A-1-d.padInfo.left,b=y-1-d.padInfo.top,v=We(i.shape,"float32"),C=1/(c*f),S=r.data.get(a.dataId).values,E=We(a.shape,"float32",S);for(let R=0;R<d.batchSize;++R)for(let _=0;_<d.inChannels;++_)for(let M=0;M<d.inHeight;++M)for(let I=0;I<d.inWidth;++I){let O=M-b,z=I-x,j=0;for(let X=0;X<y;X+=m){let D=(O+X)/h;if(!(D<0||D>=d.outHeight||Math.floor(D)!==D))for(let Q=0;Q<A;Q+=g){let V=(z+Q)/p;V<0||V>=d.outWidth||Math.floor(V)!==V||(j+=E.get(R,D,V,_))}}v.set(j*C,R,M,I,_)}return r.makeTensorInfo(v.shape,v.dtype,v.values)}var IK={kernelName:Pf,backendName:"cpu",kernelFunc:kK};function 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r.makeTensorInfo(a.shape,a.dtype,m)}var CK={kernelName:ai,backendName:"cpu",kernelFunc:SK};function TK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;Te([a],"batchToSpaceND");let o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Mt({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Fn({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Mt({inputs:{x:f},backend:r,attrs:{shape:d}}),g=So({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(m),g}var NK={kernelName:Fo,backendName:"cpu",kernelFunc:TK};function EK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.data.get(a.dataId).values,l=r.data.get(s.dataId).values,u=Ex(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var 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rr(p.outShape,a.dtype),v=w.computeStrides(a.shape),C=w.computeStrides(s.shape),S=v[0],E=x?v[1]:v[2],R=x?v[2]:1,_=x?1:v[1],M=b.strides[0],I=x?b.strides[1]:b.strides[2],O=x?b.strides[2]:1,z=x?1:b.strides[1],j=r.data.get(a.dataId).values,X=r.data.get(s.dataId).values,D=b.values;for(let Q=0;Q<p.batchSize;++Q){let V=Q*S,ee=Q*M;for(let Y=0;Y<p.outHeight;++Y){let se=ee+Y*I,Z=Y*p.strideHeight-A;for(let ae=0;ae<c;++ae){let de=Z+ae*m;if(de<0||de>=p.inHeight)continue;let Ae=ae*C[0],be=V+de*E;for(let Ee=0;Ee<p.outWidth;++Ee){let Me=se+Ee*O,De=Ee*p.strideWidth-y;for(let Be=0;Be<f;++Be){let Ze=De+Be*g;if(Ze<0||Ze>=p.inWidth)continue;let ot=Ae+Be*C[1],dt=be+Ze*R,pt=ot;for(let $e=0;$e<p.inChannels;++$e){let vt=j[dt+$e*_];for(let gt=0;gt<p.outChannels;++gt)D[Me+gt*z]+=vt*X[pt+gt];pt+=p.outChannels}}}}}}return r.makeTensorInfo(b.shape,b.dtype,D)}var LK={kernelName:Ks,backendName:"cpu",kernelFunc:yI};function BK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"conv2dBackpropFilter");let h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),{strideHeight:c,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",A=new rr(p.filterShape,"float32"),x=p.padInfo.left,b=p.padInfo.top,v=r.data.get(a.dataId).values,C=r.data.get(s.dataId).values,S=new rr(a.shape,a.dtype,v),E=new rr(s.shape,s.dtype,C);for(let R=0;R<m;++R){let _=Math.max(0,Math.ceil((b-R)/c)),M=Math.min(p.outHeight,(p.inHeight+b-R)/c);for(let I=0;I<g;++I){let O=Math.max(0,Math.ceil((x-I)/f)),z=Math.min(p.outWidth,(p.inWidth+x-I)/f);for(let j=0;j<p.inChannels;++j)for(let X=0;X<p.outChannels;++X){let D=0;for(let Q=0;Q<p.batchSize;++Q)for(let V=_;V<M;++V){let ee=R+V*c-b;for(let Y=O;Y<z;++Y){let se=I+Y*f-x;y?D+=S.get(Q,ee,se,j)*E.get(Q,V,Y,X):D+=S.get(Q,j,ee,se)*E.get(Q,X,V,Y)}}A.set(D,R,I,j,X)}}}return r.makeTensorInfo(A.shape,A.dtype,A.values)}var WK={kernelName:Df,backendName:"cpu",kernelFunc:BK};function VK(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n;Te([a,s],"conv2dBackpropInput");let h=w.computeStrides(s.shape),p=w.computeStrides(a.shape),c=N.convertConv2DDataFormat(u),f=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,c),m=new rr(f.inShape,"float32"),g=m.values,y=r.data.get(a.dataId).values,A=r.data.get(s.dataId).values,[x,b,v]=h,{batchSize:C,filterHeight:S,filterWidth:E,inChannels:R,inHeight:_,inWidth:M,outChannels:I,outHeight:O,outWidth:z,strideHeight:j,strideWidth:X}=f;c=f.dataFormat;let D=S-1-f.padInfo.top,Q=E-1-f.padInfo.left,V=c==="channelsLast",ee=m.strides[0],Y=V?m.strides[1]:m.strides[2],se=V?m.strides[2]:1,Z=V?1:m.strides[1],ae=p[0],de=V?p[1]:p[2],Ae=V?p[2]:1,be=V?1:p[1];for(let Ee=0;Ee<C;++Ee)for(let Me=0;Me<R;++Me)for(let De=0;De<_;++De){let Be=De-D,Ze=Math.max(0,Math.ceil(Be/j)),ot=Math.min(O,(S+Be)/j);for(let dt=0;dt<M;++dt){let pt=dt-Q,$e=Math.max(0,Math.ceil(pt/X)),vt=Math.min(z,(E+pt)/X),gt=0;for(let ur=Ze;ur<ot;++ur){let Xr=ur*j-Be;for(let Jt=$e;Jt<vt;++Jt){let dr=Jt*X-pt,Xn=ae*Ee+de*ur+Ae*Jt,Zr=x*(S-1-Xr)+b*(E-1-dr)+v*Me;for(let Qt=0;Qt<I;++Qt){let xn=y[Xn+be*Qt],bn=A[Zr+Qt];gt+=xn*bn}}}let Rr=ee*Ee+Y*De+se*dt+Z*Me;g[Rr]=gt}}return r.makeTensorInfo(m.shape,m.dtype,m.values)}var UK={kernelName:Xs,backendName:"cpu",kernelFunc:VK};function GK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n;Te([a,s],"conv3d");let u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),{filterDepth:d,filterHeight:h,filterWidth:p,dilationDepth:c,dilationHeight:f,dilationWidth:m,padInfo:g}=u,y=g.front,A=g.left,x=g.top,b=new rr(u.outShape,a.dtype),v=r.data.get(a.dataId).values,C=r.data.get(s.dataId).values,S=b.values,E=w.computeStrides(a.shape),R=w.computeStrides(s.shape);for(let _=0;_<u.batchSize;++_){let M=_*E[0],I=_*b.strides[0];for(let O=0;O<u.outDepth;++O){let z=I+O*b.strides[1],j=O*u.strideDepth-y;for(let X=0;X<d;++X){let D=j+X*c;if(D<0||D>=u.inDepth)continue;let Q=X*R[0],V=M+D*E[1];for(let ee=0;ee<u.outHeight;++ee){let Y=z+ee*b.strides[2],se=ee*u.strideHeight-x;for(let Z=0;Z<h;++Z){let ae=se+Z*f;if(ae<0||ae>=u.inHeight)continue;let de=Q+Z*R[1],Ae=V+ae*E[2];for(let be=0;be<u.outWidth;++be){let Ee=Y+be*u.outChannels,Me=be*u.strideWidth-A;for(let De=0;De<p;++De){let Be=Me+De*m;if(Be<0||Be>=u.inWidth)continue;let Ze=de+De*R[2],ot=Ae+Be*u.inChannels,dt=Ze;for(let pt=0;pt<u.inChannels;++pt){let $e=v[ot+pt];for(let vt=0;vt<u.outChannels;++vt)S[Ee+vt]+=$e*C[dt+vt];dt+=u.outChannels}}}}}}}}return r.makeTensorInfo(b.shape,b.dtype,b.values)}var jK={kernelName:Bp,backendName:"cpu",kernelFunc:GK};function HK(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n;Te([a,s],"conv3dBackpropFilterV2");let u=w.computeStrides(a.shape),d=w.computeStrides(s.shape),h=N.computeConv3DInfo(a.shape,l,i,1,o),p=h.strideDepth,c=h.strideHeight,f=h.strideWidth,m=h.filterDepth,g=h.filterHeight,y=h.filterWidth,A=new rr(h.filterShape,"float32"),x=A.values,[b,v,C,S]=A.strides,E=r.data.get(s.dataId).values,[R,_,M,I]=d,O=r.data.get(a.dataId).values,[z,j,X,D]=u,Q=h.padInfo.front,V=h.padInfo.left,ee=h.padInfo.top;for(let Y=0;Y<m;++Y){let se=Math.max(0,Math.ceil((Q-Y)/p)),Z=Math.min(h.outDepth,(h.inDepth+Q-Y)/p),ae=Y*b;for(let de=0;de<g;++de){let Ae=Math.max(0,Math.ceil((ee-de)/c)),be=Math.min(h.outHeight,(h.inHeight+ee-de)/c),Ee=de*v+ae;for(let Me=0;Me<y;++Me){let De=Math.max(0,Math.ceil((V-Me)/f)),Be=Math.min(h.outWidth,(h.inWidth+V-Me)/f),Ze=Me*C+Ee;for(let ot=0;ot<h.inChannels;++ot){let dt=ot*S+Ze;for(let pt=0;pt<h.outChannels;++pt){let $e=0;for(let vt=0;vt<h.batchSize;++vt){let gt=vt*z,Rr=vt*R;for(let ur=se;ur<Z;++ur){let Xr=(Y+ur*p-Q)*j+gt,Jt=ur*_+Rr;for(let dr=Ae;dr<be;++dr){let 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ot=Ze-be,dt=Math.max(0,Math.ceil(ot/ae)),pt=Math.min(Y,(O+ot)/ae);for(let $e=0;$e<Q;++$e){let vt=$e-Ee,gt=Math.max(0,Math.ceil(vt/de)),Rr=Math.min(se,(z+vt)/de);for(let ur=0;ur<V;++ur){let Xr=ur-Me,Jt=Math.max(0,Math.ceil(Xr/Ae)),dr=Math.min(Z,(j+Xr)/Ae),Xn=0;for(let Zr=dt;Zr<pt;++Zr){let Qt=Zr*ae-ot;for(let xn=gt;xn<Rr;++xn){let bn=xn*de-vt;for(let ps=Jt;ps<dr;++ps){let Xi=ps*Ae-Xr,Xh=x*De+b*Zr+v*xn+C*ps,hs=E*(O-1-Qt)+R*(z-1-bn)+_*(j-1-Xi)+M*Be;for(let Da=0;Da<ee;++Da){let Wd=A[Xh+Da],Dl=S[hs+Da];Xn+=Wd*Dl}}}}c[f*De+m*Ze+g*$e+y*ur+Be]=Xn}}}return r.makeTensorInfo(p.shape,p.dtype,p.values)}var XK={kernelName:Bf,backendName:"cpu",kernelFunc:KK},ZK=mt(Zs,e=>Math.cos(e)),YK={kernelName:Zs,backendName:"cpu",kernelFunc:ZK},JK=mt(Ys,e=>Math.cosh(e)),QK={kernelName:Ys,backendName:"cpu",kernelFunc:JK};function eX(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,[d,h,p,c]=a.shape,f=s.shape[0],[m,g]=o,y=We([f,m,g,c],"float32"),A=r.data.get(s.dataId).values,x=r.data.get(i.dataId).values,b=r.data.get(a.dataId).values,v=w.computeStrides(a.shape),C=w.computeStrides(y.shape);for(let S=0;S<f;S++){let E=S*4,R=A[E],_=A[E+1],M=A[E+2],I=A[E+3],O=x[S];if(O>=d)continue;let z=m>1?(M-R)*(h-1)/(m-1):0,j=g>1?(I-_)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let D=m>1?R*(h-1)+X*z:.5*(R+M)*(h-1);if(D<0||D>h-1){for(let Q=0;Q<g;Q++)for(let V=0;V<c;V++){let ee=V+Q*C[2]+X*C[1]+S*C[0];y.values[ee]=u}continue}if(l==="bilinear"){let Q=Math.floor(D),V=Math.ceil(D),ee=D-Q;for(let Y=0;Y<g;Y++){let se=g>1?_*(p-1)+Y*j:.5*(_+I)*(p-1);if(se<0||se>p-1){for(let Ae=0;Ae<c;Ae++){let be=Ae+Y*C[2]+X*C[1]+S*C[0];y.values[be]=u}continue}let Z=Math.floor(se),ae=Math.ceil(se),de=se-Z;for(let Ae=0;Ae<c;Ae++){let be=Ae+Z*v[2]+Q*v[1]+O*v[0],Ee=b[be];be=Ae+ae*v[2]+Q*v[1]+O*v[0];let Me=b[be];be=Ae+Z*v[2]+V*v[1]+O*v[0];let De=b[be];be=Ae+ae*v[2]+V*v[1]+O*v[0];let Be=b[be],Ze=Ee+(Me-Ee)*de,ot=De+(Be-De)*de;be=Ae+Y*C[2]+X*C[1]+S*C[0],y.values[be]=Ze+(ot-Ze)*ee}}}else for(let Q=0;Q<g;++Q){let V=g>1?_*(p-1)+Q*j:.5*(_+I)*(p-1);if(V<0||V>p-1){for(let se=0;se<c;se++){let Z=se+Q*C[2]+X*C[1]+S*C[0];y.values[Z]=u}continue}let ee=Math.round(V),Y=Math.round(D);for(let se=0;se<c;se++){let Z=se+ee*v[2]+Y*v[1]+O*v[0],ae=se+Q*C[2]+X*C[1]+S*C[0];y.values[ae]=b[Z]}}}}return r.makeTensorInfo(y.shape,y.dtype,y.values)}var tX={kernelName:_o,backendName:"cpu",kernelFunc:eX};function rX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n;Te(a,"cumsum");let l=N.getAxesPermutation([s],a.shape.length),u=a;l!=null&&(u=Fn({inputs:{x:a},backend:r,attrs:{perm:l}}));let d=N.getInnerMostAxes(1,a.shape.length)[0];if(d!==u.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${u.shape.length-1} but got axis=${d}`);let h=Dr(u.dtype,"int32"),p=w.makeZerosTypedArray(w.sizeFromShape(u.shape),h),c=r.data.get(u.dataId).values,f=u.shape[u.shape.length-1],m=o?(y,A)=>y+f-A-1:(y,A)=>y+A;for(let y=0;y<c.length;y+=f)for(let A=0;A<f;A++){let x=m(y,A);if(A===0)p[x]=i?0:c[x];else{let b=m(y,A-1);p[x]=i?c[b]+p[b]:c[x]+p[b]}}let g=r.makeTensorInfo(u.shape,h,p);if(l!=null){let y=N.getUndoAxesPermutation(l),A=Fn({inputs:{x:g},backend:r,attrs:{perm:y}});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(u),A}return g}var nX={kernelName:Po,backendName:"cpu",kernelFunc:rX};function aX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.data.get(a.dataId).values,u=r.data.get(s.dataId).values,d=Ex(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=T6(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be 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Got strides ${i} and dilations '${p}'`);let c=N.computeConv2DInfo(a.shape,s.shape,i,p,o,u,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:A}=c,x=A.left,b=A.top,v=c.outChannels/c.inChannels,C=new rr(c.outShape,a.dtype),S=r.data.get(a.dataId).values,E=r.data.get(s.dataId).values,R=C.values;for(let _=0;_<c.batchSize;++_){let M=_*d[0],I=_*C.strides[0];for(let O=0;O<c.outHeight;++O){let z=I+O*C.strides[1],j=O*c.strideHeight-b;for(let X=0;X<f;++X){let D=j+X*g;if(D<0||D>=c.inHeight)continue;let Q=X*h[0],V=M+D*d[1];for(let ee=0;ee<c.outWidth;++ee){let Y=z+ee*C.strides[2],se=ee*c.strideWidth-x;for(let Z=0;Z<m;++Z){let ae=se+Z*y;if(ae<0||ae>=c.inWidth)continue;let de=Q+Z*h[1],Ae=V+ae*c.inChannels,be=Y,Ee=de;for(let Me=0;Me<c.inChannels;++Me){let De=S[Ae+Me];for(let Be=0;Be<v;++Be)R[be+Be]+=De*E[Ee+Be];be+=v,Ee+=v}}}}}}return r.makeTensorInfo(C.shape,C.dtype,C.values)}var lX={kernelName:Js,backendName:"cpu",kernelFunc:AI};function uX(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n;Te([a,s],"depthwiseConv2dNativeBackpropFilter");let h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),{strideHeight:p,strideWidth:c,filterHeight:f,filterWidth:m}=h,g=new rr(h.filterShape,"float32"),y=h.padInfo.left,A=h.padInfo.top,x=h.outChannels/h.inChannels,b=r.data.get(a.dataId).values,v=new rr(a.shape,a.dtype,b),C=r.data.get(s.dataId).values,S=new rr(s.shape,s.dtype,C);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((A-E)/p)),_=Math.min(h.outHeight,(h.inHeight+A-E)/p);for(let M=0;M<m;++M){let I=Math.max(0,Math.ceil((y-M)/c)),O=Math.min(h.outWidth,(h.inWidth+y-M)/c);for(let z=0;z<h.outChannels;++z){let j=Math.trunc(z/x),X=z%x,D=0;for(let Q=0;Q<h.batchSize;++Q)for(let V=R;V<_;++V){let ee=E+V*p-A;for(let Y=I;Y<O;++Y){let se=M+Y*c-y;D+=v.get(Q,ee,se,j)*S.get(Q,V,Y,z)}}g.set(D,E,M,j,X)}}}return r.makeTensorInfo(g.shape,g.dtype,g.values)}var dX={kernelName:Vf,backendName:"cpu",kernelFunc:uX};function pX(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n;Te([a,s],"depthwiseConv2DNativeBackpropInput");let h=w.computeStrides(a.shape),p=w.computeStrides(s.shape),c=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),f=new rr(c.inShape,"float32"),m=f.values,[g,y,A]=f.strides,x=r.data.get(a.dataId).values,[b,v,C]=h,S=r.data.get(s.dataId).values,[E,R,_]=p,{batchSize:M,filterHeight:I,filterWidth:O,inChannels:z,inHeight:j,inWidth:X,outChannels:D,outHeight:Q,outWidth:V,strideHeight:ee,strideWidth:Y}=c,se=I-1-c.padInfo.top,Z=O-1-c.padInfo.left,ae=D/z;for(let de=0;de<M;++de)for(let Ae=0;Ae<z;++Ae)for(let be=0;be<j;++be){let Ee=be-se,Me=Math.max(0,Math.ceil(Ee/ee)),De=Math.min(Q,(I+Ee)/ee);for(let Be=0;Be<X;++Be){let Ze=Be-Z,ot=Math.max(0,Math.ceil(Ze/Y)),dt=Math.min(V,(O+Ze)/Y),pt=0;for(let $e=Me;$e<De;++$e){let vt=$e*ee-Ee;for(let gt=ot;gt<dt;++gt){let 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n=1,a=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,n,a,0,t.textureFormatFloat,t.textureTypeFloat,null);let s=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,s),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(s),i}function JJ(e,t){let r=Bx(e,t),n=e.createTexture();e.bindTexture(e.TEXTURE_2D,n);let a=1,s=1;e.texImage2D(e.TEXTURE_2D,0,r.internalFormatHalfFloat,a,s,0,r.textureFormatFloat,r.textureTypeHalfFloat,null);let i=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,i),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(i),o}function ZI(e){return 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       bool isnan_custom(float val) {
-        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
+        uint floatToUint = floatBitsToUint(val);
+        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
       }
 
       bvec4 isnan_custom(vec4 val) {
@@ -105,11 +106,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Sl(e,t,r="index"){let n=w.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function i0(e,t,r="index"){let n=w.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function QJ(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function eQ(e,t,r="index"){let n=e.map((s,i)=>i),a=QJ(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function Vx(e){let t=w.computeStrides(e).map(r=>r.toString());return`
+    `),{version:e,attribute:t,varyingVs:r,varyingFs:n,texture2D:a,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:l,defineRound:u}}function Sl(e,t,r="index"){let n=w.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / ${a}`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * ${a}`:`index -= ${e[s]} * ${a}`;return`${i}; ${o};`}).join("")}function s0(e,t,r="index"){let n=w.computeStrides(t);return n.map((a,s)=>{let i=`int ${e[s]} = ${r} / outShapeStrides[${s}]`,o=s===n.length-1?`int ${e[s+1]} = ${r} - ${e[s]} * outShapeStrides[${s}]`:`index -= ${e[s]} * outShapeStrides[${s}]`;return`${i}; ${o};`}).join("")}function QJ(e,t){let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function eQ(e,t,r="index"){let n=e.map((s,i)=>i),a=QJ(n,t);return a.map((s,i)=>{let o=`int ${e[i]} = ${r} / ${a[i]}`,l=i===a.length-1?`int ${e[i+1]} = ${r} - ${e[i]} * ${a[i]}`:`index -= ${e[i]} * ${a[i]}`;return`${o}; ${l};`}).join("")}function Wx(e){let t=w.computeStrides(e).map(r=>r.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function Ux(){return`
+`}function Vx(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
@@ -152,10 +153,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
-`,{getBroadcastDims:JI}=N;function tQ(e,t,r){let n=[];if(e.forEach(p=>{let c=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=Gx(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
+`,{getBroadcastDims:JI}=N;function tQ(e,t,r){let n=[];if(e.forEach(p=>{let c=w.sizeFromShape(p.shapeInfo.logicalShape);if(p.shapeInfo.isUniform?n.push(`uniform float ${p.name}${c>1?`[${c}]`:""};`):(n.push(`uniform sampler2D ${p.name};`),n.push(`uniform int offset${p.name};`)),r.enableShapeUniforms){let{uniformShape:f}=Ux(r.packedInputs,p.shapeInfo.logicalShape,p.shapeInfo.texShape);switch(f.length){case 1:n.push(`uniform int ${p.name}Shape;`);break;case 2:n.push(`uniform ivec2 ${p.name}Shape;`);break;case 3:n.push(`uniform ivec3 ${p.name}Shape;`);break;case 4:n.push(`uniform ivec4 ${p.name}Shape;`);break;default:break}n.push(`uniform ivec2 ${p.name}TexShape;`)}}),r.enableShapeUniforms){switch(t.logicalShape.length){case 1:n.push("uniform int outShape;");break;case 2:n.push("uniform ivec2 outShape;"),n.push("uniform int outShapeStrides;");break;case 3:n.push("uniform ivec3 outShape;"),n.push("uniform ivec2 outShapeStrides;");break;case 4:n.push("uniform ivec4 outShape;"),n.push("uniform ivec3 outShapeStrides;");break;default:break}n.push("uniform ivec2 outTexShape;")}r.customUniforms&&r.customUniforms.forEach(p=>{n.push(`uniform ${p.type} ${p.name}${p.arrayIndex?`[${p.arrayIndex}]`:""};`)});let a=n.join(`
 `),s=e.map(p=>rQ(p,t,r.packedInputs,r.enableShapeUniforms)).join(`
 `),i=t.texShape,o=Ur(),l=sQ(o),u,d,h=lQ(o);return t.isPacked?(u=nQ(t.logicalShape,i,r.enableShapeUniforms),d=oQ(o)):(u=aQ(t.logicalShape,i,r.enableShapeUniforms),d=iQ(o)),r.packedInputs&&(h+=hQ),[h,l,d,a,u,s,r.userCode].join(`
-`)}function md(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return IQ(e,t);case 1:return CQ(e,t);case 2:return NQ(e,t);case 3:return RQ(e,t);case 4:return FQ(e,t);case 5:return $Q(e);case 6:return PQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function QI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return kQ(e);case 1:return SQ(e,t);case 2:return TQ(e,t);case 3:return EQ(e,t);default:return MQ(e,t)}}function rQ(e,t,r=!1,n){let a="";r?a+=QI(e,n):a+=md(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=_Q(e,t):a+=zQ(e,t)),a}function nQ(e,t,r){switch(e.length){case 0:return eS();case 1:return cQ(e,t,r);case 2:return vQ(e,t,r);case 3:return mQ(e,t,r);default:return yQ(e,t,r)}}function aQ(e,t,r){switch(e.length){case 0:return eS();case 1:return fQ(e,t,r);case 2:return wQ(e,t,r);case 3:return gQ(e,t,r);case 4:return AQ(e,t,r);case 5:return xQ(e,t);case 6:return bQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function sQ(e){return`
+`)}function fd(e,t=!1){let r=e.shapeInfo.logicalShape;switch(r.length){case 0:return IQ(e,t);case 1:return CQ(e,t);case 2:return NQ(e,t);case 3:return RQ(e,t);case 4:return FQ(e,t);case 5:return $Q(e);case 6:return PQ(e);default:throw new Error(`${r.length}-D input sampling is not yet supported`)}}function QI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return kQ(e);case 1:return SQ(e,t);case 2:return TQ(e,t);case 3:return EQ(e,t);default:return MQ(e,t)}}function rQ(e,t,r=!1,n){let a="";r?a+=QI(e,n):a+=fd(e,n);let s=e.shapeInfo.logicalShape,i=t.logicalShape;return s.length<=i.length&&(r?a+=_Q(e,t):a+=zQ(e,t)),a}function nQ(e,t,r){switch(e.length){case 0:return e8();case 1:return cQ(e,t,r);case 2:return vQ(e,t,r);case 3:return mQ(e,t,r);default:return yQ(e,t,r)}}function aQ(e,t,r){switch(e.length){case 0:return e8();case 1:return fQ(e,t,r);case 2:return wQ(e,t,r);case 3:return gQ(e,t,r);case 4:return AQ(e,t,r);case 5:return xQ(e,t);case 6:return bQ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function sQ(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -265,7 +266,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function eS(){return`
+`;function e8(){return`
     int getOutputCoords() {
       return 0;
     }
@@ -362,7 +363,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${i0(["r","c","d"],e)}
+    ${s0(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
 `;let n=Sl(["r","c","d"],e);return`
@@ -419,7 +420,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${i0(["r","c","d","d2"],e)}
+      ${s0(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
   `;let n=Sl(["r","c","d","d2"],e);return`
@@ -573,7 +574,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function CQ(e,t){let r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1);if(e.shapeInfo.isUniform)return`
       float ${n}(int index) {
-        ${gd(e)}
+        ${md(e)}
       }
     `;let a=e.shapeInfo.texShape,s=a[0],i=a[1];if(i===1&&s===1)return`
       float ${n}(int index) {
@@ -643,15 +644,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
       return sampleTexture(${n}, uv);
     }
-  `}let{newShape:i,keptDims:o}=w.squeezeShape(r),l=i;if(l.length<r.length){let p=yd(e,l),c=["row","col"];return`
-      ${md(p,t)}
+  `}let{newShape:i,keptDims:o}=w.squeezeShape(r),l=i;if(l.length<r.length){let p=gd(e,l),c=["row","col"];return`
+      ${fd(p,t)}
       float ${a}(int row, int col) {
-        return ${a}(${Ad(c,o)});
+        return ${a}(${yd(c,o)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${r[1]}, 1)));
-        ${gd(e)}
+        ${md(e)}
       }
     `;let u=s[0],d=s[1],h=Cl(n);return d===1?t?`
       float ${a}(int row, int col) {
@@ -691,10 +692,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${u}, ${d}, index);
     return sampleTexture(${n}, uv);
   }
-`}function EQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=yd(e,p),m=["b","row","col"];return`
+`}function EQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(r[0]===1){let p=r.slice(1),c=[1,2],f=gd(e,p),m=["b","row","col"];return`
         ${QI(f,t)}
         vec4 ${a}(int b, int row, int col) {
-          return ${a}(${Ad(m,c)});
+          return ${a}(${yd(m,c)});
         }
       `}let o=Ur();if(t)return`
     vec4 ${a}(int b, int row, int col) {
@@ -711,16 +712,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${l}, ${u}, ${h}, ${d}, b, row, col);
       return ${o.texture2D}(${n}, uv);
     }
-  `}function RQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let m=yd(e,u),g=["row","col","depth"];return`
-        ${md(m,t)}
+  `}function RQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[1]*r[2],i=r[2],{newShape:o,keptDims:l}=w.squeezeShape(r),u=o;if(u.length<r.length){let m=gd(e,u),g=["row","col","depth"];return`
+        ${fd(m,t)}
         float ${a}(int row, int col, int depth) {
-          return ${a}(${Ad(g,l)});
+          return ${a}(${yd(g,l)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${s}, ${i}, 1)));
-        ${gd(e)}
+        ${md(e)}
       }
     `;let d=e.shapeInfo.texShape,h=d[0],p=d[1],c=e.shapeInfo.flatOffset;if(p===s&&c==null)return t?`
       float ${a}(int row, int col, int depth) {
@@ -789,16 +790,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}, ${u});
       return ${a.texture2D}(${r}, uv);
     }
-  `}function FQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=yd(e,l),x=["row","col","depth","depth2"];return`
-      ${md(A,t)}
+  `}function FQ(e,t){let r=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),s=r[3],i=r[2]*s,o=r[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(r);if(l.length<r.length){let A=gd(e,l),x=["row","col","depth","depth2"];return`
+      ${fd(A,t)}
       float ${a}(int row, int col, int depth, int depth2) {
-        return ${a}(${Ad(x,u)});
+        return ${a}(${yd(x,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${a}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${s}, 1)));
-        ${gd(e)}
+        ${md(e)}
       }
     `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1],f=`int stride2 = ${n}Shape[3];`,m=`int stride1 = ${n}Shape[2] * stride2;`,g=`int stride0 = ${n}Shape[1] * stride1;`;if(c===o&&d==null)return t?`
       float ${a}(int row, int col, int depth, int depth2) {
@@ -859,10 +860,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index + ${y});
       return sampleTexture(${n}, uv);
     }
-  `}function $Q(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=yd(e,l),g=["row","col","depth","depth2","depth3"];return`
-      ${md(m)}
+  `}function $Q(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),a=t[4],s=t[3]*a,i=t[2]*s,o=t[1]*i,{newShape:l,keptDims:u}=w.squeezeShape(t);if(l.length<t.length){let m=gd(e,l),g=["row","col","depth","depth2","depth3"];return`
+      ${fd(m)}
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
-        return ${n}(${Ad(g,u)});
+        return ${n}(${yd(g,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
@@ -870,7 +871,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${s}, ${a})) +
           depth3;
-        ${gd(e)}
+        ${md(e)}
       }
     `;let d=e.shapeInfo.flatOffset,h=e.shapeInfo.texShape,p=h[0],c=h[1];if(c===o&&d==null)return`
       float ${n}(int row, int col, int depth, int depth2, int depth3) {
@@ -900,11 +901,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${p}, ${c}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function PQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=yd(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
-      ${md(g)}
+  `}function PQ(e){let t=e.shapeInfo.logicalShape,r=e.name,n="get"+r.charAt(0).toUpperCase()+r.slice(1),{newShape:a,keptDims:s}=w.squeezeShape(t);if(a.length<t.length){let g=gd(e,a),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${fd(g)}
       float ${n}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${n}(${Ad(y,s)});
+        return ${n}(${yd(y,s)});
       }
     `}let i=t[5],o=t[4]*i,l=t[3]*o,u=t[2]*l,d=t[1]*u;if(e.shapeInfo.isUniform)return`
       float ${n}(int row, int col, int depth,
@@ -915,7 +916,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${gd(e)}
+        ${md(e)}
       }
     `;let h=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],f=p[1];if(f===d&&h==null)return`
       float ${n}(int row, int col, int depth,
@@ -950,7 +951,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${c}, ${f}, index);
       return sampleTexture(${r}, uv);
     }
-  `}function gd(e){let t=e.name,r=w.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
+  `}function md(e){let t=e.name,r=w.sizeFromShape(e.shapeInfo.logicalShape);return r<2?`return ${t};`:`
     for (int i = 0; i < ${r}; i++) {
       if (i == index) {
         return ${t}[i];
@@ -981,9 +982,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${p}
       return get${n}(${f});
     }
-  `}function bt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Gx(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function yd(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function Ad(e,t){return t.map(r=>e[r]).join(", ")}function OQ(e,t,r,n){let a=r.map((b,v)=>{let C={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(C.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[v],shapeInfo:C}}),s=a.map(b=>b.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=tQ(a,i,t),l=RI(e.gl,o),u=e.createProgram(l),d=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(u,"INFINITY",!1));let p=!1,c={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let v=t.variableNames[b];c[v]=e.getUniformLocation(u,v,p),c[`offset${v}`]=e.getUniformLocation(u,`offset${v}`,p),t.enableShapeUniforms&&(f[`${v}Shape`]=e.getUniformLocation(u,`${v}Shape`,p),m[`${v}TexShape`]=e.getUniformLocation(u,`${v}TexShape`,p))}let g,y,A;t.enableShapeUniforms&&(g=e.getUniformLocation(u,"outShape",p),A=e.getUniformLocation(u,"outShapeStrides",p),y=e.getUniformLocation(u,"outTexShape",p));let x=[];return t.customUniforms&&t.customUniforms.forEach((b,v)=>{x[v]=e.getUniformLocation(u,b.name,p)}),{program:t,fragmentShader:l,source:o,webGLProgram:u,uniformLocations:c,customUniformLocations:x,inShapeInfos:s,outShapeInfo:i,infLoc:d,nanLoc:h,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:A,outTexShapeLocation:y}}function dv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function DQ(e,t,r,n,a){t.program.enableShapeUniforms||(dv(t.inShapeInfos,r),dv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=Gx(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function LQ(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=Gx(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${J().getNumber("WEBGL_VERSION")}`,s}function nn(e){return J().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var BQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
+  `}function bt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ux(e,t,r){let{newShape:n,keptDims:a}=w.squeezeShape(t),s=t.length,i=e&&s===3&&t[0]===1,o=i?t.slice(1):n,l=!e&&s>1&&!w.arraysEqual(t,r)&&n.length<s||i;return{useSqueezeShape:l,uniformShape:l?o:t,keptDims:a}}function gd(e,t){let r=JSON.parse(JSON.stringify(e));return r.shapeInfo.logicalShape=t,r}function yd(e,t){return t.map(r=>e[r]).join(", ")}function OQ(e,t,r,n){let a=r.map((b,v)=>{let C={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(C.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[v],shapeInfo:C}}),s=a.map(b=>b.shapeInfo),i={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked,flatOffset:null},o=tQ(a,i,t),l=RI(e.gl,o),u=e.createProgram(l),d=null,h=e.getUniformLocation(u,"NAN",!1);J().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(u,"INFINITY",!1));let p=!1,c={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let v=t.variableNames[b];c[v]=e.getUniformLocation(u,v,p),c[`offset${v}`]=e.getUniformLocation(u,`offset${v}`,p),t.enableShapeUniforms&&(f[`${v}Shape`]=e.getUniformLocation(u,`${v}Shape`,p),m[`${v}TexShape`]=e.getUniformLocation(u,`${v}TexShape`,p))}let g,y,A;t.enableShapeUniforms&&(g=e.getUniformLocation(u,"outShape",p),A=e.getUniformLocation(u,"outShapeStrides",p),y=e.getUniformLocation(u,"outTexShape",p));let x=[];return t.customUniforms&&t.customUniforms.forEach((b,v)=>{x[v]=e.getUniformLocation(u,b.name,p)}),{program:t,fragmentShader:l,source:o,webGLProgram:u,uniformLocations:c,customUniformLocations:x,inShapeInfos:s,outShapeInfo:i,infLoc:d,nanLoc:h,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:A,outTexShapeLocation:y}}function uv(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((r,n)=>{let a=r.logicalShape,s=t[n],i=s.shape;if(!w.arraysEqual(a,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${a} and ${i} must match`);if(r.isUniform&&s.isUniform)return;let o=r.texShape,l=s.isUniform?null:s.texData.texShape;if(!w.arraysEqual(o,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${l} must match`)})}function DQ(e,t,r,n,a){t.program.enableShapeUniforms||(uv(t.inShapeInfos,r),uv([t.outShapeInfo],[n]));let s=n.texData.texture,i=n.texData.texShape;n.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,i[0],i[1]):e.setOutputMatrixTexture(s.texture,i[0],i[1]),e.setProgram(t.webGLProgram),J().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),r.forEach((l,u)=>{let d=t.program.variableNames[u],h=t.uniformLocations[d],p=t.uniformLocations[`offset${d}`],c=t.inShapesLocations[`${d}Shape`],f=t.inTexShapesLocations[`${d}TexShape`];if(c){let{uniformShape:m}=Ux(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(c,new Int32Array(m));break;case 2:e.gl.uniform2iv(c,new Int32Array(m));break;case 3:e.gl.uniform3iv(c,new Int32Array(m));break;case 4:e.gl.uniform4iv(c,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),h!=null){if(l.isUniform){if(w.sizeFromShape(l.shape)<2)e.gl.uniform1f(h,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(h,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,h,u)}});let o=t.outShapeLocation;if(o)switch(n.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(n.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(n.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(n.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(n.shape));break;default:break}if(t.outShapeStridesLocation){let l=w.computeStrides(n.shape);switch(n.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,n.texData.texShape[0],n.texData.texShape[1]),t.program.customUniforms&&a&&t.program.customUniforms.forEach((l,u)=>{let d=t.customUniformLocations[u],h=a[u];if(l.type==="float")e.gl.uniform1fv(d,h);else if(l.type==="vec2")e.gl.uniform2fv(d,h);else if(l.type==="vec3")e.gl.uniform3fv(d,h);else if(l.type==="vec4")e.gl.uniform4fv(d,h);else if(l.type==="int")e.gl.uniform1iv(d,h);else if(l.type==="ivec2")e.gl.uniform2iv(d,h);else if(l.type==="ivec3")e.gl.uniform3iv(d,h);else if(l.type==="ivec4")e.gl.uniform4iv(d,h);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function LQ(e,t,r){let n="";t.concat(r).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let l=i.texData.texShape,{useSqueezeShape:u,uniformShape:d,keptDims:h}=Ux(e.packedInputs,i.shape,l),p="",c="",f="";if(d.length===1&&e.packedInputs){let v=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${v[0]>1}_${v[1]>1}`}else if(d.length===2&&!e.packedInputs)c=`${d[0]>1}_${d[1]>1}`;else if(d.length>2&&!e.packedInputs){let v=w.computeStrides(d);f=`${v[0]===l[1]}_${v[v.length-1]===l[1]}`}let m=i.shape.length,g=d.length===2&&w.arraysEqual(i.shape,l),y=w.sizeFromShape(i.shape)===1,A=N.getBroadcastDims(i.shape,r.shape),x=!e.packedInputs&&m===r.shape.length&&w.arraysEqual(l,r.texData.texShape),b=e.packedInputs||d.length>2?"":`${l[0]>1}_${l[1]>1}`;n+=`${m}_${x}_${u?h:""}_${d.length}_${y}_${A}_${g}_${p}_${c}_${f}_${b}_${o}`}else{let l=i.isUniform?"uniform":i.texData.texShape;n+=`${i.shape}_${l}_${o}`}});let a=e.userCode,s=e.constructor.name;return s+="_"+n+"_"+a+`${J().getNumber("WEBGL_VERSION")}`,s}function nn(e){return J().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var BQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?i0(["r","c","d"],e):Sl(["r","c","d"],e)}
+        ${this.enableShapeUniforms?s0(["r","c","d"],e):Sl(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1003,7 +1004,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
     `}},WQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?i0(["r","c","d"],e):Sl(["r","c","d"],e)}
+        ${this.enableShapeUniforms?s0(["r","c","d"],e):Sl(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1037,7 +1038,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${t.output} = encode_float(x);
       }
     `}},GQ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let r=Ur();this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let n="result";t&&(n="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?Ux():Vx(e)}
+      ${this.enableShapeUniforms?Vx():Wx(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1095,7 +1096,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?Ux():Vx(e)}
+        ${this.enableShapeUniforms?Vx():Wx(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1110,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${r.output} = ${a};
         }
-    `}},tS={};Le(tS,{bindVertexProgramAttributeStreams:()=>dS,createBufferFromOutputTexture:()=>cS,createFloat16MatrixTexture:()=>iS,createFloat16PackedMatrixTexture:()=>uS,createFloat32MatrixTexture:()=>sS,createIndexBuffer:()=>aS,createPackedMatrixTexture:()=>lS,createUnsignedBytesMatrixTexture:()=>oS,createVertexBuffer:()=>nS,createVertexShader:()=>rS,downloadByteEncodedFloatMatrixFromOutputTexture:()=>mS,downloadFloat32MatrixFromBuffer:()=>fS,downloadMatrixFromPackedOutputTexture:()=>yS,downloadPackedMatrixFromBuffer:()=>gS,getInternalFormatForFloat16MatrixTexture:()=>Hx,getInternalFormatForFloat16PackedMatrixTexture:()=>Xx,getInternalFormatForFloat32MatrixTexture:()=>jx,getInternalFormatForPackedMatrixTexture:()=>Kx,getInternalFormatForUnsignedBytesMatrixTexture:()=>qx,uploadDenseMatrixToTexture:()=>pS,uploadPixelDataToTexture:()=>hS});function rS(e){let t=Ur(),r=`${t.version}
+    `}},t8={};Le(t8,{bindVertexProgramAttributeStreams:()=>d8,createBufferFromOutputTexture:()=>c8,createFloat16MatrixTexture:()=>i8,createFloat16PackedMatrixTexture:()=>u8,createFloat32MatrixTexture:()=>s8,createIndexBuffer:()=>a8,createPackedMatrixTexture:()=>l8,createUnsignedBytesMatrixTexture:()=>o8,createVertexBuffer:()=>n8,createVertexShader:()=>r8,downloadByteEncodedFloatMatrixFromOutputTexture:()=>m8,downloadFloat32MatrixFromBuffer:()=>f8,downloadMatrixFromPackedOutputTexture:()=>y8,downloadPackedMatrixFromBuffer:()=>g8,getInternalFormatForFloat16MatrixTexture:()=>jx,getInternalFormatForFloat16PackedMatrixTexture:()=>Kx,getInternalFormatForFloat32MatrixTexture:()=>Gx,getInternalFormatForPackedMatrixTexture:()=>qx,getInternalFormatForUnsignedBytesMatrixTexture:()=>Hx,uploadDenseMatrixToTexture:()=>p8,uploadPixelDataToTexture:()=>h8});function r8(e){let t=Ur(),r=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1119,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;we(e,()=>e.finish()),we(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),we(e,()=>e.deleteFramebuffer(this.framebuffer)),we(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),we(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),we(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),s8(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),i8(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),o8(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),h8(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,r,n){this.throwIfDisposed(),p8(this.gl,e,t,r,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),u8(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),l8(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Ry(this.gl,this.framebuffer),this.outputTexture=null),we(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,r){return this.downloadMatrixDriver(e,()=>m8(this.gl,t,r,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,r,n,a,s){return g8(this.gl,e,t,r,n,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return f8(this.gl,e,t)}createBufferFromTexture(e,t,r){this.bindTextureToFrameBuffer(e);let n=c8(this.gl,t,r,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,r;if(J().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,a=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),r=()=>{let s=n.clientWaitSync(a,0,0);return s===n.ALREADY_SIGNALED||s===n.CONDITION_SATISFIED},t=a}else J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),r=()=>this.isQueryAvailable(t,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):r=()=>!0;return{query:t,isFencePassed:r}}downloadMatrixFromPackedTexture(e,t,r){return this.downloadMatrixDriver(e,()=>y8(this.gl,t,r))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=r8(t));let r=MI(t);return we(t,()=>t.attachShader(r,this.vertexShader)),we(t,()=>t.attachShader(r,e)),FI(t,r),this.debug&&qc(t,r),this.vertexAttrsAreBound||(this.setProgram(r),this.vertexAttrsAreBound=d8(t,this.program,this.vertexBuffer)),r}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&we(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&qc(this.gl,this.program),we(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,r=!0){return this.throwIfDisposed(),r?LI(this.gl,e,t):BI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),we(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,r){this.throwIfDisposed(),this.throwIfNoProgram(),WI(this.gl,e,t,r)}setOutputMatrixTexture(e,t,r){this.setOutputMatrixTextureDriver(e,r,t)}setOutputPackedMatrixTexture(e,t,r){this.throwIfDisposed();let[n,a]=hd(t,r);this.setOutputMatrixTextureDriver(e,n,a)}setOutputMatrixWriteRegion(e,t,r,n){this.setOutputMatrixWriteRegionDriver(r,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,r,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&qc(this.gl,this.program),mp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),we(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),we(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fp(this.gl,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.createQuery();return r.beginQuery(n.TIME_ELAPSED_EXT,a),a}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,r=this.getQueryTimerExtensionWebGL2();t.endQuery(r.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let r=this.gl;return r.getQueryParameter(e,r.QUERY_RESULT)/1e6}else{let r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let r=this.gl,n=this.getQueryTimerExtensionWebGL2(),a=r.getQueryParameter(e,r.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}else{let r=this.getQueryTimerExtensionWebGL1(),n=r.getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=HQ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:r}=this.itemsToPoll[t];r()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Kc(this.gl,e,this.framebuffer),this.debug&&mp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Kc(this.gl,this.outputTexture,this.framebuffer),this.debug&&mp(this.gl)):Ry(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let r=t();return this.unbindTextureToFrameBuffer(),r}setOutputMatrixTextureDriver(e,t,r){this.throwIfDisposed();let n=this.gl;Kc(n,e,this.framebuffer),this.debug&&mp(n),this.outputTexture=e,we(n,()=>n.viewport(0,0,t,r)),we(n,()=>n.scissor(0,0,t,r))}setOutputMatrixWriteRegionDriver(e,t,r,n){this.throwIfDisposed(),we(this.gl,()=>this.gl.scissor(e,t,r,n))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function HQ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:qQ,bincountImpl:A8,bincountReduceImpl:KQ,ceilImpl:XQ,concatImpl:ZQ,equalImpl:YQ,expImpl:JQ,expm1Impl:QQ,floorImpl:eee,gatherNdImpl:tee,gatherV2Impl:ree,greaterImpl:nee,greaterEqualImpl:aee,lessImpl:see,lessEqualImpl:iee,linSpaceImpl:oee,logImpl:lee,maxImpl:uee,maximumImpl:dee,minimumImpl:pee,multiplyImpl:hee,negImpl:cee,notEqualImpl:fee,prodImpl:mee,rangeImpl:gee,rsqrtImpl:yee,sigmoidImpl:Aee,simpleAbsImpl:x8,sliceImpl:xee,sparseFillEmptyRowsImpl:bee,sparseReshapeImpl:vee,sparseSegmentReductionImpl:b8,sqrtImpl:wee,stridedSliceImpl:kee,stringNGramsImpl:Iee,stringSplitImpl:See,stringToHashBucketFastImpl:Cee,subImpl:Tee,tileImpl:Nee,topKImpl:Eee,transposeImpl:Xx,uniqueImpl:Ree}=r0;function v8(e,t){return["x","y","z","w","u","v"].slice(0,t).map(r=>`${e}.${r}`)}function zr(e,t){return t===1?[e]:v8(e,t)}function Mee(e,t){if(e===1)return"rc";let r="";for(let n=0;n<e;n++)r+=t[n],n<e-1&&(r+=",");return r}var Fee=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=nn(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1146,7 +1147,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},wS=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},w8=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length);let r="";for(let n=0;n<4;n++){let a="thisRC = rc;";n%2===1&&(a+="thisRC.z += 1;"),n>1&&(a+="thisRC.y += 1;"),r+=`
         ${a}
         ${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1159,7 +1160,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${n>0?"}":""}
       `}this.userCode=`
       ${$ee(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?Ux():Vx(e)}
+      ${this.enableShapeUniforms?Vx():Wx(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1179,7 +1180,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t?eQ(["r","c","d"],"inputShape"):Sl(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var Pee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=hv(t,r),a=cv(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=pv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=hv(r,n),s=cv(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=pv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function _ee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function pv(e,t,r,n,a){let s=zee(t,n),i;if(a){let[l,u]=cd(e[0],e[1]);i=l*u}else{let[l,u]=Ch(e[0],e[1]);i=l*u}let o=_ee(r,s);return i*o}function zee(e,t){switch(e){case 3:return Kx(t);case 4:return Xx(t);case 1:return jx(t);case 0:return Hx(t);case 2:return qx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Oee(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function hv(e,t){if(e===1)return 3;if(e===0||e==null)return Oee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function cv(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ua=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
+  `}var Pee=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,r){let n=pv(t,r),a=hv(e,n,r);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);let s=dv(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,r);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[a].shift();return this.usedTextures[a].push(o),o}let i;return n===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,r,n){if(this.freeTextures==null)return;let a=pv(r,n),s=hv(t,a,n);s in this.freeTextures||(this.freeTextures[s]=[]);let i=dv(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,n),o=J().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let l=this.usedTextures[s],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function _ee(e,t){let r=e;if(t===r.R32F)return 4;if(t===r.R16F)return 2;if(t===r.RGBA32F||t===e.RGBA)return 16;if(t===r.RGBA16F)return 8;if(t===r.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function dv(e,t,r,n,a){let s=zee(t,n),i;if(a){let[l,u]=hd(e[0],e[1]);i=l*u}else{let[l,u]=Sh(e[0],e[1]);i=l*u}let o=_ee(r,s);return i*o}function zee(e,t){switch(e){case 3:return qx(t);case 4:return Kx(t);case 1:return Gx(t);case 0:return jx(t);case 2:return Hx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Oee(e){return J().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function pv(e,t){if(e===1)return 3;if(e===0||e==null)return Oee(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function hv(e,t,r){return`${e[0]}_${e[1]}_${t}_${r}`}var Ua=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1190,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},jn="if (isnan(x)) return x;",Dee="return x;",fv="return abs(x);",Lee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bee=jn+`
+    `}},jn="if (isnan(x)) return x;",Dee="return x;",cv="return abs(x);",Lee="return (x >= 0.0) ? x : (exp(x) - 1.0);",Bee=jn+`
   return (x < 0.0) ? 0.0 : x;
 `,Wee=jn+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
@@ -1241,10 +1242,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},Xee=Un.whereImpl,Zee=1e-7,Yee=1e-4,U1={};function Jee(e){return e in U1||(U1[e]={}),U1[e]}var Qee=J().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),ete=600;function tte(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*ete/1024/1024}var kS=class extends Cu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof pu)t=e;else{let r=fa(J().getNumber("WEBGL_VERSION"),e);t=new pu(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=fa(J().getNumber("WEBGL_VERSION"));t=new pu(r),this.binaryCache=Jee(J().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Pee(this.gpgpu),this.numMBBeforeWarning=tte(),this.texData=new Op(this,kr())}nextDataId(){return kS.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new co(i,Jl):h=new Ua(i,Jl);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new co(n,Jl):c=new Ua(n,Jl);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(J().getBool("DEBUG")&&!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Wc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&kr().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new co(a,Jl):p=new Ua(a,Jl);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=kr().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!TI(r))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Wc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=J().getBool("WEBGL_PACK")&&n===!0,i=s?Zc(t):t,o=s?new UQ(i):new VQ(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Qee){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Xee(e.shape,t)}packedUnaryOp(e,t,r){let n=new co(e.shape,t),a=this.compileAndRun(n,[e],r);return kr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=xS(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,fv,e.dtype);let t=new Ua(e.shape,fv),r=this.compileAndRun(t,[e]);return kr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return kr().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Kee(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Fee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Co(e.shape),...To(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Co(t),...To(t)],s=new wS(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Zc(a),o;n?o=new WQ(i):o=new BQ(i);let l=!0,u=[t!=null?t:Wc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Wc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!_p(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=LQ(e,u,d),p=this.getAndSaveBinary(h,()=>OQ(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),DQ(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Zee:Yee}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=GI(r,o),t.texShape=d),a!=null){let h=Zc(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=cd(d[0],d[1])),o?p=new jQ(h,m):p=new GQ(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),C=this.texData.get(v.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(v.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=rte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},Nh=kS;Nh.nextDataId=0;function rte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var nte="0.0.0";function IS(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}ah.isBrowser()&&Al("webgl",()=>new Nh,2);var ate={forceHalfFloat:IS},SS=`
+    `}},Xee=Un.whereImpl,Zee=1e-7,Yee=1e-4,V1={};function Jee(e){return e in V1||(V1[e]={}),V1[e]}var Qee=J().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),ete=600;function tte(){return J().global.screen==null?1024:J().global.screen.height*J().global.screen.width*window.devicePixelRatio*ete/1024/1024}var k8=class extends Su{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!J().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof du)t=e;else{let r=fa(J().getNumber("WEBGL_VERSION"),e);t=new du(r)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let r=fa(J().getNumber("WEBGL_VERSION"));t=new du(r),this.binaryCache=Jee(J().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Pee(this.gpgpu),this.numMBBeforeWarning=tte(),this.texData=new zp(this,kr())}nextDataId(){return k8.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,r){if((J().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||J().getBool("DEBUG"))&&this.checkNumericalProblems(e),r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:r,values:e,usage:1,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,r,n,a){if(J().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:r,dtype:n,values:t,usage:1,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:r,dtype:n,complexTensorInfos:a,slice:s,shape:i,isPacked:o}=t;if(s!=null){let h;o?h=new co(i,Jl):h=new Ua(i,Jl);let p=this.runWebGLProgram(h,[{dataId:e,shape:i,dtype:n}],n),c=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),c}if(r!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return r;let l=this.activeTimers!=null,u;l&&(u=w.now());let d;if(n==="complex64"){let h=this.readSync(a.real.dataId),p=this.readSync(a.imag.dataId);d=N.mergeRealAndImagArrays(h,p)}else d=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=w.now()-u),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let c=this.pendingRead.get(e);return new Promise(f=>c.push(f))}let t=this.texData.get(e),{values:r,shape:n,slice:a,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(a!=null){let c;o?c=new co(n,Jl):c=new Ua(n,Jl);let f=this.runWebGLProgram(c,[{dataId:e,shape:n,dtype:s}],s),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(r!=null)return this.convertAndCacheOnCPU(e);if(J().getBool("DEBUG")&&!J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&J().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(s!=="complex64"&&J().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let c=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(c.texture.texture,...Bc(n))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let c=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=c[0],m=c[1];d=N.mergeRealAndImagArrays(f,m)}else if(l==null)d=this.getValuesFromTexture(e);else{let c=w.sizeFromShape(n);d=this.gpgpu.downloadFloat32MatrixFromBuffer(l,c)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let c=this.gpgpu.gl;we(c,()=>c.deleteBuffer(l))}let h=this.convertAndCacheOnCPU(e,d),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(c=>c(h)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&kr().removeDataId(e,this),this.pendingDeletes--),h}readToGPU(e,t={}){let r=this.texData.get(e),{values:n,shape:a,slice:s,dtype:i,isPacked:o,texture:l}=r;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(s!=null){let p;o?p=new co(a,Jl):p=new Ua(a,Jl);let c=this.runWebGLProgram(p,[{dataId:e,shape:a,dtype:i}],i),f=this.readToGPU(c,t);return this.disposeIntermediateTensorInfo(c),f}if(l==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),d=kr().makeTensorFromDataId(u.dataId,u.shape,u.dtype),h=this.texData.get(u.dataId);return{tensorRef:d,...h.texture}}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let r=e[t];if(!TI(r))throw J().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${r} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${r} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:r,isPacked:n}=this.texData.get(e),a=w.sizeFromShape(t);if(J().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let h=this.decode(e),p=this.texData.get(h.dataId),c=this.gpgpu.downloadMatrixFromPackedTexture(p.texture.texture,...Bc(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(h),c}let s=J().getBool("WEBGL_PACK")&&n===!0,i=s?Xc(t):t,o=s?new UQ(i):new VQ(i),l=this.runWebGLProgram(o,[{shape:i,dtype:r,dataId:e}],"float32"),u=this.texData.get(l.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(l),d}timerAvailable(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(a);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(J().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:r}=this.texData.get(e);return r!=null&&(this.disposeData(r.real.dataId,t),this.disposeData(r.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:r,texShape:n,usage:a,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,l=this.dataRefCount.get(o);l>1?this.dataRefCount.set(o,l-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,r),this.textureManager.releaseTexture(t,n,a,s)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Qee){return J().getBool("WEBGL_CPU_FORWARD")&&e.every(r=>this.texData.get(r.dataId).texture==null&&w.sizeFromShape(r.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return Xee(e.shape,t)}packedUnaryOp(e,t,r){let n=new co(e.shape,t),a=this.compileAndRun(n,[e],r);return kr().makeTensorFromDataId(a.dataId,a.shape,a.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=x8(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(J().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,cv,e.dtype);let t=new Ua(e.shape,cv),r=this.compileAndRun(t,[e]);return kr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,r){let{dataId:n}=this.makeTensorInfo(e,t,r);return kr().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new Kee(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Fee(e.shape),r=!0;return this.runWebGLProgram(t,[e],e.dtype,null,r)}packedReshape(e,t){let r=[Co(e.shape),...To(e.shape)],n={dtype:e.dtype,shape:r,dataId:e.dataId},a=[Co(t),...To(t)],s=new w8(a,r),i=!0,o=[r],l=this.runWebGLProgram(s,[n],e.dtype,o,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let r=this.texData.get(e),{isPacked:n,shape:a,dtype:s}=r;if(t!=null){let h=w.sizeFromShape(a),p=t[0]*t[1]*4;w.assert(h<=p,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=Xc(a),o;n?o=new WQ(i):o=new BQ(i);let l=!0,u=[t!=null?t:Bc(i)],d=this.runWebGLProgram(o,[{shape:i,dtype:s,dataId:e}],s,u,l,t);return{dtype:s,shape:a,dataId:d.dataId}}runWebGLProgram(e,t,r,n,a=!1,s){let i=this.makeTensorInfo(e.outputShape,r),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=s!=null?s:Bc(e.outputShape);o.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=J().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Pp(y.shape,g.shape)){let A=g,x=g.shape;g.shape=y.shape,g=this.packedReshape(g,x),l.push(g),y=this.texData.get(g.dataId),A.shape=x}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(i.dataId);let d={shape:i.shape,texData:o,isUniform:!1},h=LQ(e,u,d),p=this.getAndSaveBinary(h,()=>OQ(this.gpgpu,e,u,d)),c=this.activeTimers!=null,f;c&&(f=this.startTimer()),DQ(this.gpgpu,p,u,d,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),c&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=J().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!J().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&a===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,r,n,a=!1){return r=r||t[0].dtype,this.runWebGLProgram(e,t,r,n,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(J().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=K(()=>{if(!J().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=J().getBool("DEBUG");J().set("DEBUG",!1);let t=this.abs(Se(1e-8)).dataSync()[0];if(J().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?Zee:Yee}uploadToGPU(e){let t=this.texData.get(e),{shape:r,dtype:n,values:a,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let l=this.activeTimers!=null,u;l&&(u=w.now());let d=t.texShape;if(d==null&&(d=GI(r,o),t.texShape=d),a!=null){let h=Xc(r),p,c=d[1],f=d[0],m=a instanceof Uint8Array||a instanceof Uint8ClampedArray;(o||!m)&&([c,f]=hd(d[0],d[1])),o?p=new jQ(h,m):p=new GQ(h,m);let g=m?[f,c]:d,y=this.makeTensorInfo(g,n),A=this.texData.get(y.dataId);m?A.usage=2:A.usage=1,A.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),c,f,a);let x=[[f,c]],b=!0,v=this.runWebGLProgram(p,[y],n,x,b),C=this.texData.get(v.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(v.dataId),t.values=null,l&&(this.uploadWaitMs+=w.now()-u)}else{let h=this.acquireTexture(d,i,n,o);t.texture=h}}convertAndCacheOnCPU(e,t){let r=this.texData.get(e),{dtype:n}=r;return this.releaseGPUData(e),t!=null&&(r.values=rte(t,n)),r.values}acquireTexture(e,t,r,n){if(this.numBytesInGPU+=this.computeBytes(e,r),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let a=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${a} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},Th=k8;Th.nextDataId=0;function rte(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let r=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let n=0;n<r.length;++n)r[n]=Math.round(e[n]);return r}else throw new Error(`Unknown dtype ${t}`)}var nte="0.0.0";function I8(){J().set("WEBGL_FORCE_F16_TEXTURES",!0)}nh.isBrowser()&&Al("webgl",()=>new Th,2);var ate={forceHalfFloat:I8},S8=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,Iu=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
+`,ku=class{constructor(e,t,r){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.enableShapeUniforms=nn(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1254,12 +1255,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},o0=`
+    `}},i0=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,Eh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=nn(a);let s="";if(n)if(a===0||w.sizeFromShape(this.outputShape)===1)s=`
+`,Nh=class{constructor(e,t,r,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r);let a=this.outputShape.length;this.enableShapeUniforms=nn(a);let s="";if(n)if(a===0||w.sizeFromShape(this.outputShape)===1)s=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1303,13 +1304,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function dn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var ste={kernelName:ii,backendName:"webgl",kernelFunc:dn};function Oi(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=dn({inputs:{x:n},backend:r}),l=dn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var ite={kernelName:Lp,backendName:"webgl",kernelFunc:Oi},CS="return (a < 0.) ? b * a : a;",TS=`
+    `}};function dn(e){let{inputs:t,backend:r}=e,{x:n}=t;return r.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var ste={kernelName:ii,backendName:"webgl",kernelFunc:dn};function Oi(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.texData.get(s.dataId),o=dn({inputs:{x:n},backend:r}),l=dn({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var ite={kernelName:Dp,backendName:"webgl",kernelFunc:Oi},C8="return (a < 0.) ? b * a : a;",T8=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function ote(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(TS,a.shape,i.shape):new Iu(CS,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var lte={kernelName:oi,backendName:"webgl",kernelFunc:ote},NS="return (a < 0.) ? b * a : a;",ES=`
+`;function ote(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=r.makeTensorInfo([],"float32",w.createScalarValue(s,"float32")),o=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(T8,a.shape,i.shape):new ku(C8,a.shape,i.shape),l=r.runWebGLProgram(o,[a,i],"float32");return r.disposeIntermediateTensorInfo(i),l}var lte={kernelName:oi,backendName:"webgl",kernelFunc:ote},N8="return (a < 0.) ? b * a : a;",E8=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function ute(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(ES,n.shape,a.shape):new Iu(NS,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var dte={kernelName:xi,backendName:"webgl",kernelFunc:ute},xd="if (isnan(x)) return x;",pte=`
+`;function ute(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(E8,n.shape,a.shape):new ku(N8,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],"float32")}var dte={kernelName:xi,backendName:"webgl",kernelFunc:ute},Ad="if (isnan(x)) return x;",pte=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,hte=`
@@ -1317,7 +1318,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new co(i.shape,t):d=new Ua(i.shape,e),o.runWebGLProgram(d,[i],l)}}function Ar({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,C={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new Iu(e,l.shape,u.shape);return d.runWebGLProgram(E,[C,S],Dr(b.dtype,v.dtype))}),A=Oi({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Dr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Eh(t,l.shape,u.shape,r):c=new Iu(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function l0(e,t=!1){if(e==="linear")return t?Uee:Dee;if(e==="relu")return t?jee:Bee;if(e==="elu")return t?Gee:Lee;if(e==="relu6")return t?Hee:Wee;if(e==="prelu")return t?ES:NS;if(e==="leakyrelu")return t?TS:CS;if(e==="sigmoid")return t?qee:Vee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var RS=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=nn(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function it({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:r,dtype:n}){return({inputs:a,backend:s})=>{let{x:i}=a,o=s,l=n||i.dtype;if(o.shouldExecuteOnCPU([i])&&r!=null){let h=o.texData.get(i.dataId),p=r(h.values,l);return o.makeTensorInfo(i.shape,l,p)}let u=J().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return u?d=new co(i.shape,t):d=new Ua(i.shape,e),o.runWebGLProgram(d,[i],l)}}function Ar({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:r=!1,supportsComplex:n=!1,cpuKernelImpl:a,dtype:s}){return({inputs:i,backend:o})=>{let{a:l,b:u}=i,d=o;if(n&&l.dtype==="complex64"){let f=d.texData.get(l.dataId),m=d.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(x=>{let[b,v]=x,C={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:v.dataId,dtype:v.dtype,shape:u.shape},E=new ku(e,l.shape,u.shape);return d.runWebGLProgram(E,[C,S],Dr(b.dtype,v.dtype))}),A=Oi({inputs:{real:g,imag:y},backend:d});return d.disposeIntermediateTensorInfo(g),d.disposeIntermediateTensorInfo(y),A}let h=s||Dr(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||d.shouldExecuteOnCPU([l,u]))&&a!=null){let f=d.texData.get(l.dataId).values,m=d.texData.get(u.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[A,x]=a(l.shape,u.shape,g,y,h),b=d.makeTensorInfo(x,h),v=d.texData.get(b.dataId);return v.values=A,b}let p=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,c;return p?c=new Nh(t,l.shape,u.shape,r):c=new ku(e,l.shape,u.shape),d.runWebGLProgram(c,[l,u],h)}}function o0(e,t=!1){if(e==="linear")return t?Uee:Dee;if(e==="relu")return t?jee:Bee;if(e==="elu")return t?Gee:Lee;if(e==="relu6")return t?Hee:Wee;if(e==="prelu")return t?E8:N8;if(e==="leakyrelu")return t?T8:C8;if(e==="sigmoid")return t?qee:Vee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var R8=class{constructor(e,t,r,n=!1,a=!1,s=!1,i=null,o=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=r,this.enableShapeUniforms=nn(this.outputShape.length);let u=n?e[1]:e[2],d=Math.ceil(u/2),h=n?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",c=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1356,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},mv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},gv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
+    `}},fv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},mv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1369,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},yv="return a * b;";function Yx(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new gv(mv.REAL,n.shape,a.shape),d=new gv(mv.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Oi({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=hee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Eh(yv,n.shape,a.shape):i=new Iu(yv,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var cte={kernelName:gi,backendName:"webgl",kernelFunc:Yx};function fte(e,t,r){let n=[Co(e.shape),...To(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Co(t),...To(t)],i=new wS(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!_p(a.shape,l)&&!(d.texture!==null&&_p(d.shape,l))?fte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var mte={kernelName:tl,backendName:"webgl",kernelFunc:ve},Av=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
+    `}},gv="return a * b;";function Zx(e){let{inputs:t,backend:r}=e,{a:n,b:a}=t,s=N.upcastType(n.dtype,a.dtype);if(n.dtype==="complex64"){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),u=new mv(fv.REAL,n.shape,a.shape),d=new mv(fv.IMAG,n.shape,a.shape),h=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:n.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:n.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Oi({inputs:{real:p,imag:c},backend:r});return r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}if(r.shouldExecuteOnCPU([n,a])){let o=r.texData.get(n.dataId),l=r.texData.get(a.dataId),[u,d]=hee(n.shape,a.shape,o.values,l.values,s),h=r.makeTensorInfo(d,s),p=r.texData.get(h.dataId);return p.values=u,h}let i;return J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Nh(gv,n.shape,a.shape):i=new ku(gv,n.shape,a.shape),r.runWebGLProgram(i,[n,a],s)}var cte={kernelName:gi,backendName:"webgl",kernelFunc:Zx};function fte(e,t,r){let n=[Co(e.shape),...To(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},s=[Co(t),...To(t)],i=new w8(s,n),o=!0,l=[n],u=r.runWebGLProgram(i,[a],e.dtype,l,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ve(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{shape:s}=n,i=r,o=w.sizeFromShape(a.shape),l=w.inferFromImplicitShape(s,o),u=w.sizeFromShape(l);w.assert(o===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${a.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(a.dataId);return d.isPacked&&!Pp(a.shape,l)&&!(d.texture!==null&&Pp(d.shape,l))?fte(a,l,i):(i.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype})}var mte={kernelName:tl,backendName:"webgl",kernelFunc:ve},yv=class{constructor(e,t){this.variableNames=["x"];let{windowSize:r,batchSize:n,inSize:a,outSize:s}=e;this.outputShape=[n,s];let i=Math.floor(r/4)*4,o=r%4,l="sumValue += dot(values, ones);";if(t!=null){let d=1/t;l=`sumValue += dot(values * ${w.isInt(d)?d.toPrecision(2):d}, ones);`}let u="";a%r>0&&(u=`
         if (inIdx < 0 || inIdx >= ${a}) {
           return 0.0;
         }
@@ -1514,12 +1515,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function yte(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Tl(e,t,r,n){let a=yte(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new Av({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new Av({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new gte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Ate=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=bt(this.rank),a=xte(t);this.userCode=`
+    `}};function yte(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let r=t.length?t[t.length-1].outSize:e[1],n=N.computeOptimalWindowSize(r);t.push({inSize:r,windowSize:n,outSize:Math.ceil(r/n)})}return t}function Tl(e,t,r,n){let a=yte(e.shape),s=e;for(let i=0;i<a.length;i++){let{inSize:o,windowSize:l,outSize:u}=a[i],d,h;r==="mean"?d=i===0?new yv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},o):new yv({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u}):d=new gte({windowSize:l,inSize:o,batchSize:e.shape[0],outSize:u},r),h=s,s=n.runWebGLProgram(d,[s],t),h.dataId!==e.dataId&&n.disposeIntermediateTensorInfo(h)}return s}var Ate=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[t[s]];this.outputShape=r,this.rank=r.length;let n=bt(this.rank),a=xte(t);this.userCode=`
     void main() {
       ${n} resRC = getOutputCoords();
       setOutput(getA(${a}));
     }
-    `}};function xte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var bte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=bt(this.rank),a=vS("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
+    `}};function xte(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],n=new Array(t);for(let a=0;a<e.length;a++)n[e[a]]=r[a];return n.join()}var bte=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let r=new Array(e.length);for(let u=0;u<r.length;u++)r[u]=e[t[u]];if(this.outputShape=r,this.rank=r.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=bt(this.rank),a=v8("rc",this.rank),s=new Array(this.rank);for(let u=0;u<t.length;u++)s[t[u]]=a[u];let i=`vec2(${s.slice(-2).join()})`,o=`++${a[this.rank-1]} < ${r[this.rank-1]}`,l=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
     void main() {
       ${n} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1536,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function u0(e,t,r){let n=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bte(e.shape,t):new Ate(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function vte(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=u0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=w.sizeFromShape(p),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=nh(e.dtype),A=Tl(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function d0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return vte(a,s,i,r)}var wte={kernelName:Ti,backendName:"webgl",kernelFunc:d0};function Br(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=Zx(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=u0(a,s,i);return u}var kte={kernelName:Fi,backendName:"webgl",kernelFunc:Br},MS=1e3;function Tf({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],C=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),S=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[C,S],R=Math.max(y,A),_=r?C.shape[1]:C.shape[2],M=s!=null,I=i!=null,O=l==="leakyrelu",z=l!=null?l0(l,!0):null,j=M||I||O||z!=null,X;if((c===1||f===1)&&_>MS&&j===!1){let Q=C,V=S;r&&(Q=Br({inputs:{x:C},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=Br({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,Y=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;Y&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=Yx({inputs:{a:se,b:ae},backend:a});X=d0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Dr(e.dtype,t.dtype),V=new RS(b,v,[R,c,f],r,n,M,z,I,O),ee=[C,S];if(s!=null&&ee.push(s),I&&ee.push(i),O){let Y=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(Y),E.push(Y)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function Ite(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Tf({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ste={kernelName:Ts,backendName:"webgl",kernelFunc:Ite},xv="return abs(x);";function Cte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=xS(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new co(n.shape,xv):a=new Ua(n.shape,xv),r.runWebGLProgram(a,[n],n.dtype)}var Tte={kernelName:Mo,backendName:"webgl",kernelFunc:Cte},Nte=jn+`
+    `}};function l0(e,t,r){let n=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new bte(e.shape,t):new Ate(e.shape,t);return r.runWebGLProgram(n,[e],e.dtype)}function vte(e,t,r,n){let a=t,s=e.shape.length,i=w.parseAxisParam(a,e.shape),o=i,l=N.getAxesPermutation(o,s),u=l!=null,d=e;u&&(d=l0(e,l,n),o=N.getInnerMostAxes(o.length,s)),N.assertAxesAreInnerMostDims("sum",o,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,o),c=h;r&&(c=N.expandShapeToKeepDim(h,i));let f=w.sizeFromShape(p),m=w.sizeFromShape(e.shape)/f,g=ve({inputs:{x:d},attrs:{shape:[m,f]},backend:n}),y=rh(e.dtype),A=Tl(g,y,"sum",n),x=ve({inputs:{x:A},attrs:{shape:c},backend:n});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(A),u&&n.disposeIntermediateTensorInfo(d),x}function u0(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return vte(a,s,i,r)}var wte={kernelName:Ti,backendName:"webgl",kernelFunc:u0};function Br(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];let u;if(i.shouldExecuteOnCPU([a])){let d=i.texData.get(a.dataId).values,h=Xx(d,a.shape,a.dtype,s,l);u=i.makeTensorInfo(l,a.dtype);let p=i.texData.get(u.dataId);p.values=h}else u=l0(a,s,i);return u}var kte={kernelName:Fi,backendName:"webgl",kernelFunc:Br},M8=1e3;function Cf({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],C=ve({inputs:{x:e},backend:a,attrs:{shape:b}}),S=ve({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[C,S],R=Math.max(y,A),_=r?C.shape[1]:C.shape[2],M=s!=null,I=i!=null,O=l==="leakyrelu",z=l!=null?o0(l,!0):null,j=M||I||O||z!=null,X;if((c===1||f===1)&&_>M8&&j===!1){let Q=C,V=S;r&&(Q=Br({inputs:{x:C},backend:a,attrs:{perm:[0,2,1]}}),E.push(Q)),n&&(V=Br({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),E.push(V));let ee=f!==1,Y=f===1,se=Q;ee&&(se=ve({inputs:{x:Q},backend:a,attrs:{shape:[R,_,1]}}),E.push(se));let Z=f===1?2:1,ae=V;Y&&(ae=ve({inputs:{x:V},backend:a,attrs:{shape:[R,1,_]}}),E.push(ae));let de=Zx({inputs:{a:se,b:ae},backend:a});X=u0({inputs:{x:de},backend:a,attrs:{axis:Z,keepDims:!0}}),E.push(de)}else{let Q=Dr(e.dtype,t.dtype),V=new R8(b,v,[R,c,f],r,n,M,z,I,O),ee=[C,S];if(s!=null&&ee.push(s),I&&ee.push(i),O){let Y=a.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(Y),E.push(Y)}X=a.runWebGLProgram(V,ee,Q)}let D=ve({inputs:{x:X},backend:a,attrs:{shape:x}});E.push(X);for(let Q of E)a.disposeIntermediateTensorInfo(Q);return D}function Ite(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return Cf({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ste={kernelName:Ts,backendName:"webgl",kernelFunc:Ite},Av="return abs(x);";function Cte(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])&&n.dtype!=="complex64"){let s=r.texData.get(n.dataId),i=x8(s.values);return r.makeTensorInfo(n.shape,n.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new co(n.shape,Av):a=new Ua(n.shape,Av),r.runWebGLProgram(a,[n],n.dtype)}var Tte={kernelName:Mo,backendName:"webgl",kernelFunc:Cte},Nte=jn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,Ete=it({opSnippet:Nte}),Rte={kernelName:Nu,backendName:"webgl",kernelFunc:Ete},Mte=jn+`
+`,Ete=it({opSnippet:Nte}),Rte={kernelName:Tu,backendName:"webgl",kernelFunc:Ete},Mte=jn+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,Fte=it({opSnippet:Mte}),$te={kernelName:Eu,backendName:"webgl",kernelFunc:Fte},bv="return a + b;",Pte=Ar({opSnippet:bv,packedOpSnippet:bv,supportsComplex:!0,cpuKernelImpl:qQ}),_te={kernelName:Ha,backendName:"webgl",kernelFunc:Pte},zte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,Fte=it({opSnippet:Mte}),$te={kernelName:Nu,backendName:"webgl",kernelFunc:Fte},xv="return a + b;",Pte=Ar({opSnippet:xv,packedOpSnippet:xv,supportsComplex:!0,cpuKernelImpl:qQ}),_te={kernelName:Ha,backendName:"webgl",kernelFunc:Pte},zte=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((a,s)=>`T${s}`);let r=[];this.variableNames.forEach(a=>{r.push(`float v${a} = get${a}AtOutCoords();`)});let n=this.variableNames.map(a=>`v${a}`).join(" + ");this.userCode=`
       void main() {
         ${r.join(`
         `)}
@@ -1559,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,Fte=it({opSnippet:Mte}),$te={kernelName:Eu,b
         vec4 result = ${n};
         setOutput(result);
       }
-    `}};function Qc(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return dn({inputs:{x:n[0]},backend:r});if(n.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Qc({inputs:n.slice(0,o),backend:r}),u=Qc({inputs:n.slice(o),backend:r});return Qc({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Dr(o,l)),s=n.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new Ote(n[0].shape,s):new zte(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Dte={kernelName:Vs,backendName:"webgl",kernelFunc:Qc};function Lte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Bte={kernelName:Ru,backendName:"webgl",kernelFunc:Lte};function Wte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Vte={kernelName:Mu,backendName:"webgl",kernelFunc:Wte},Ute=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function Jc(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return dn({inputs:{x:n[0]},backend:r});if(n.length>J().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(n.length/2),l=Jc({inputs:n.slice(0,o),backend:r}),u=Jc({inputs:n.slice(o),backend:r});return Jc({inputs:[l,u],backend:r})}let a=n.map(o=>o.dtype).reduce((o,l)=>Dr(o,l)),s=n.map(o=>o.shape),i=J().getBool("WEBGL_PACK")?new Ote(n[0].shape,s):new zte(n[0].shape,s);return r.runWebGLProgram(i,n,a)}var Dte={kernelName:Vs,backendName:"webgl",kernelFunc:Jc};function Lte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("all",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"all",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Bte={kernelName:Eu,backendName:"webgl",kernelFunc:Lte};function Wte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,o)),N.assertAxesAreInnerMostDims("any",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"any",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var Vte={kernelName:Ru,backendName:"webgl",kernelFunc:Wte},Ute=class{constructor(e,t,r){this.variableNames=["A"];let{windowSize:n,batchSize:a,outSize:s}=e;r||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];let i=t==="max"?">":"<",o=r?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1641,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,Fte=it({opSnippet:Mte}),$te={kernelName:Eu,b
         }
         setOutput(bestIndex);
       }
-    `}};function FS(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new Ute(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=FS(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function $S(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new Gte(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=$S(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function PS(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=FS(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return $S(e,t,n)}function jte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Br({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=PS(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Hte={kernelName:Us,backendName:"webgl",kernelFunc:jte};function qte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Br({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=PS(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Kte={kernelName:Fu,backendName:"webgl",kernelFunc:qte},Xte=jn+`
+    `}};function F8(e,t,r,n=null){let a=t.shape[0],s=t.shape[1];n!=null&&(a=n.shape[0],s=n.shape[1]);let i=N.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:a,outSize:Math.ceil(s/i)},l=new Ute(o,r,n==null),u=[t];n!=null&&u.push(n);let d=e.runWebGLProgram(l,u,"int32");if(d.shape[1]===1)return d;let h=F8(e,t,r,d);return e.disposeIntermediateTensorInfo(d),h}function $8(e,t,r,n=null){let a=n!=null?n.shape:t.shape,s=a[a.length-1],i=N.computeOptimalWindowSize(s),o=new Gte(a,i,r,n==null),l=n==null?[t]:[t,n],u=e.runWebGLProgram(o,l,"int32");if(u.shape.length===t.shape.length){let d=$8(e,t,r,u);return e.disposeIntermediateTensorInfo(u),d}return u}function P8(e,t,r,n){let a=[r];if(N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),a,t.shape.length),!J().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,l=t;o&&(l=e.unpackTensor(t),s.push(l));let[u,d]=N.computeOutAndReduceShapes(l.shape,a),h=w.sizeFromShape(d),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,h]}});s.push(p);let c=F8(e,p,n);s.push(c);let f=ve({inputs:{x:c},backend:e,attrs:{shape:u}});return s.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return $8(e,t,n)}function jte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Br({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=P8(r,l,i[0],"max");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Hte={kernelName:Us,backendName:"webgl",kernelFunc:jte};function qte(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Br({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=P8(r,l,i[0],"min");return u.forEach(h=>r.disposeIntermediateTensorInfo(h)),d}var Kte={kernelName:Mu,backendName:"webgl",kernelFunc:qte},Xte=jn+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,Zte=it({opSnippet:Xte}),Yte={kernelName:$u,backendName:"webgl",kernelFunc:Zte},Jte=jn+"return log(x + sqrt(x * x + 1.0));",Qte=it({opSnippet:Jte}),ere={kernelName:Pu,backendName:"webgl",kernelFunc:Qte},tre=jn+`
+`,Zte=it({opSnippet:Xte}),Yte={kernelName:Fu,backendName:"webgl",kernelFunc:Zte},Jte=jn+"return log(x + sqrt(x * x + 1.0));",Qte=it({opSnippet:Jte}),ere={kernelName:$u,backendName:"webgl",kernelFunc:Qte},tre=jn+`
   return atan(x);
-`,rre=it({opSnippet:tre}),nre={kernelName:_u,backendName:"webgl",kernelFunc:rre},are=pte+`
+`,rre=it({opSnippet:tre}),nre={kernelName:Pu,backendName:"webgl",kernelFunc:rre},are=pte+`
   return atan(a, b);
 `,sre=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+hte+`
   return result;
-`,ire=Ar({opSnippet:are,packedOpSnippet:sre}),ore={kernelName:Ou,backendName:"webgl",kernelFunc:ire},lre=jn+`
+`,ire=Ar({opSnippet:are,packedOpSnippet:sre}),ore={kernelName:zu,backendName:"webgl",kernelFunc:ire},lre=jn+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernelName:zu,backendName:"webgl",kernelFunc:ure},zp=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let S=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernelName:_u,backendName:"webgl",kernelFunc:ure},_p=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,p=e.padInfo.top,c=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),r){let S=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${p}, ${c});
 
@@ -1798,7 +1799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }
         setOutput(${x});
       }
-    `}},Jx=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
+    `}},Yx=class{constructor(e,t,r,n=!1,a=!1){if(this.variableNames=["x"],t==="avg"&&r)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,d=e.dilationHeight,h=e.dilationWidth,p=e.effectiveFilterDepth,c=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let A=t==="avg",x="0.0";if(A||(x="-1.0 / 1e-20"),r){let R=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${l});
         const ivec3 pads = ivec3(${m}, ${g}, ${y});
@@ -1961,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
           setOutput(${v});
         }
       }
-    `}};function pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;fd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return dn({inputs:{x:a},backend:r});let h=new zp(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var hre={kernelName:Gs,backendName:"webgl",kernelFunc:pre};function cre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new Jx(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var fre={kernelName:Dp,backendName:"webgl",kernelFunc:cre},mre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
+    `}};function pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;cd(a,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return dn({inputs:{x:a},backend:r});let h=new _p(d,"avg",!1);return r.runWebGLProgram(h,[a],"float32")}var hre={kernelName:Gs,backendName:"webgl",kernelFunc:pre};function cre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l,dataFormat:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,l,u),p=new Yx(h,"avg",!1);return r.runWebGLProgram(p,[a],"float32")}var fre={kernelName:Op,backendName:"webgl",kernelFunc:cre},mre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,r=e.filterWidth,n=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=o-1-e.padInfo.top,d=l-1-e.padInfo.left,h=1/(t*r);this.userCode=`
       const ivec2 pads = ivec2(${u}, ${d});
       const float avgMultiplier = float(${h});
 
@@ -2059,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function yre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new gre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Are={kernelName:zf,backendName:"webgl",kernelFunc:yre};function xre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;fd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new mre(d);return r.runWebGLProgram(h,[a],i.dtype)}var bre={kernelName:_f,backendName:"webgl",kernelFunc:xre};function vre(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Tf({a,b:s,transposeA:i,transposeB:o,backend:r})}var wre={kernelName:js,backendName:"webgl",kernelFunc:vre},kre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function yre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new gre(p);return r.runWebGLProgram(c,[a],i.dtype)}var Are={kernelName:_f,backendName:"webgl",kernelFunc:yre};function xre(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s;cd([a,s],"avgPoolGrad");let{filterSize:o,strides:l,pad:u}=n,d=N.computePool2DInfo(i.shape,o,l,1,u),h=new mre(d);return r.runWebGLProgram(h,[a],i.dtype)}var bre={kernelName:Pf,backendName:"webgl",kernelFunc:xre};function vre(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return Cf({a,b:s,transposeA:i,transposeB:o,backend:r})}var wre={kernelName:js,backendName:"webgl",kernelFunc:vre},kre=class{constructor(e,t,r,n,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2082,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},Sre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=J().getBool("WEBGL_PACK_NORMALIZATION")?new Ire(n.shape,a.shape,s.shape,d,h,l):new kre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Cre={kernelName:ai,backendName:"webgl",kernelFunc:Sre},Tre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=bt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Nre(this.rank),n,a=e.map((s,i)=>`sourceLoc.${Py[i]} = start[${i}] + coords.${Py[i]};`);n=`
+    `}},Sre=({inputs:e,backend:t,attrs:r})=>{let{x:n,mean:a,variance:s,offset:i,scale:o}=e;w.assert(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=r;l==null&&(l=.001);let u=[n,a,s],d=null;i!=null&&(d=i.shape,u.push(i));let h=null;o!=null&&(h=o.shape,u.push(o));let p=J().getBool("WEBGL_PACK_NORMALIZATION")?new Ire(n.shape,a.shape,s.shape,d,h,l):new kre(n.shape,a.shape,s.shape,d,h,l);return t.runWebGLProgram(p,u,u[0].dtype)},Cre={kernelName:ai,backendName:"webgl",kernelFunc:Sre},Tre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=bt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let r=Nre(this.rank),n,a=e.map((s,i)=>`sourceLoc.${$y[i]} = start[${i}] + coords.${$y[i]};`);n=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${a.join(`
@@ -2092,7 +2093,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         ${n}
         setOutput(getSource(${r}));
       }
-    `}},Py=["x","y","z","w","u","v"];function Nre(e){if(e===1)return"sourceLoc";if(e<=6)return Py.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ere=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=bt(this.rank),r=zr("coords",this.rank),n=zr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
+    `}},$y=["x","y","z","w","u","v"];function Nre(e){if(e===1)return"sourceLoc";if(e<=6)return $y.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ere=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=bt(this.rank),r=zr("coords",this.rank),n=zr("sourceLoc",this.rank),a=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,s=`getChannel(getSource(${n.join()}), ${a})`,i=`
       result.x = ${s};
       if (++${r[this.rank-1]} < ${e[this.rank-1]}) {
         ++${n[this.rank-1]};
@@ -2121,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         ${o}
         setOutput(result);
       }
-    `}};function Rre(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function bd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=xee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ere(l):new Tre(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Rre(a,o,l,r)}var Mre={kernelName:il,backendName:"webgl",kernelFunc:bd},Fre=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Br({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=bd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},$re={kernelName:Fo,backendName:"webgl",kernelFunc:Fre};function Pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=AS(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var _re={kernelName:Of,backendName:"webgl",kernelFunc:Pre};function zre(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Ore={kernelName:Df,backendName:"webgl",kernelFunc:zre},Dre="return float(a != b);",_S=Ar({opSnippet:Dre,cpuKernelImpl:fee,dtype:"bool"}),Lre={kernelName:Ko,backendName:"webgl",kernelFunc:_S};function Rh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return dn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Bre={kernelName:Kp,backendName:"webgl",kernelFunc:Rh},Wre="return float(int(x));";function Vre(e,t){let r=new Ua(e.shape,Wre),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function _y(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return dn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=_y({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Oi({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Rh({inputs:{input:a},backend:r}),o=_y({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=dn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return Vre(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=_S({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Ure={kernelName:Hs,backendName:"webgl",kernelFunc:_y},vv="return ceil(x);",Gre=it({opSnippet:vv,packedOpSnippet:vv,cpuKernelImpl:XQ}),jre={kernelName:qs,backendName:"webgl",kernelFunc:Gre},Hre=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function Rre(e,t,r,n){let a=n.texData.get(e.dataId),s=n.makeTensorInfo(r,e.dtype),i=n.texData.get(s.dataId);Object.assign(i,a),i.refCount=1,i.shape=r,i.dtype=e.dtype;let o=_t.computeFlatOffset(t,w.computeStrides(e.shape));a.slice&&(o+=a.slice.flatOffset),i.slice={flatOffset:o,origDataId:a.slice&&a.slice.origDataId||e.dataId};let l=n.dataRefCount.get(i.slice.origDataId)||1;return n.dataRefCount.set(i.slice.origDataId,l+1),s}function xd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);if(r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.texData.get(a.dataId),p=xee(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}let{isPacked:u}=r.texData.get(a.dataId),d=_t.isSliceContinous(a.shape,o,l);if(u||!d){let h=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ere(l):new Tre(l),p=[o];return r.runWebGLProgram(h,[a],a.dtype,p)}return r.uploadToGPU(a.dataId),Rre(a,o,l,r)}var Mre={kernelName:il,backendName:"webgl",kernelFunc:xd},Fre=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=ve({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Br({inputs:{x:f},backend:r,attrs:{perm:u}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:d}}),y=xd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeIntermediateTensorInfo(A)),y},$re={kernelName:Fo,backendName:"webgl",kernelFunc:Fre};function Pre(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i}=n,o=r.readSync(a.dataId),l=r.readSync(s.dataId),u=A8(o,l,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,u)}var _re={kernelName:zf,backendName:"webgl",kernelFunc:Pre};function zre(e){let{inputs:t,backend:r}=e,{s0:n,s1:a}=t,s=r.readSync(n.dataId),i=r.readSync(a.dataId),o=N.assertAndGetBroadcastShape(Array.from(s),Array.from(i));return r.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var Ore={kernelName:Of,backendName:"webgl",kernelFunc:zre},Dre="return float(a != b);",_8=Ar({opSnippet:Dre,cpuKernelImpl:fee,dtype:"bool"}),Lre={kernelName:Ko,backendName:"webgl",kernelFunc:_8};function Eh(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return dn({inputs:{x:a.complexTensorInfos.real},backend:r})}var Bre={kernelName:qp,backendName:"webgl",kernelFunc:Eh},Wre="return float(int(x));";function Vre(e,t){let r=new Ua(e.shape,Wre),n=t.runWebGLProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Py(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return dn({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Py({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=Oi({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeIntermediateTensorInfo(o),l}if(a.dtype==="complex64"){let i=Eh({inputs:{input:a},backend:r}),o=Py({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=dn({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return Vre(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=_8({inputs:{a,b:i},backend:r});return r.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Ure={kernelName:Hs,backendName:"webgl",kernelFunc:Py},bv="return ceil(x);",Gre=it({opSnippet:bv,packedOpSnippet:bv,cpuKernelImpl:XQ}),jre={kernelName:qs,backendName:"webgl",kernelFunc:Gre},Hre=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
         float value = getAAtOutCoords();
@@ -2156,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function wv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Yre(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new Zre(n.shape),i=[wv(n,a.complexTensorInfos.real),wv(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Jre={kernelName:Bp,backendName:"webgl",kernelFunc:Yre},Qre=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
+    `}};function vv(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Yre(e){let{inputs:t,backend:r}=e,{x:n}=t,a=r.texData.get(n.dataId),s=new Zre(n.shape),i=[vv(n,a.complexTensorInfos.real),vv(n,a.complexTensorInfos.imag)];return r.runWebGLProgram(s,i,i[0].dtype)}var Jre={kernelName:Lp,backendName:"webgl",kernelFunc:Yre},Qre=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let r=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];r.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let n=t.length,a=t[t.length-1];r.push(`else setOutput(getT${n}(yR, yC-${a}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2171,12 +2172,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];h+=`
         if (${l} < ${o[f]}  && ${l} >= ${o[f-1]}) {
           return getChannel(
-            getT${f}(${Uc(i,l,m)}),
-            vec2(${Uc(u,l,m)}));
+            getT${f}(${Vc(i,l,m)}),
+            vec2(${Vc(u,l,m)}));
         }`}let p=o.length,c=o[o.length-1];h+=`
         return getChannel(
-          getT${p}(${Uc(i,l,c)}),
-          vec2(${Uc(u,l,c)}));`,this.userCode=`
+          getT${p}(${Vc(i,l,c)}),
+          vec2(${Vc(u,l,c)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${h}
       }
@@ -2202,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }
         setOutput(result);
       }
-    `}};function Uc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function p0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return dn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var tne={kernelName:Gp,backendName:"webgl",kernelFunc:p0};function su(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Rh({inputs:{input:m},backend:r})),h=e.map(m=>p0({inputs:{input:m},backend:r})),p=su(d,t,r),c=su(h,t,r),f=Oi({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=ZQ(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=su(e.slice(0,d),t,r),p=su(e.slice(d),t,r),c=su([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new ene(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=rne(e,t,r),o=new Qre(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function rne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function zS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return dn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),su(o,s,r)}var nne={kernelName:$o,backendName:"webgl",kernelFunc:zS},OS=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
+    `}};function Vc(e,t,r){let n=e.indexOf(t);return e.map((a,s)=>s===n?`${a} - ${r}`:a).join()}function d0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.texData.get(n.dataId);return dn({inputs:{x:a.complexTensorInfos.imag},backend:r})}var tne={kernelName:Up,backendName:"webgl",kernelFunc:d0};function au(e,t,r){let n=e[0].dtype;if(n==="complex64"){let d=e.map(m=>Eh({inputs:{input:m},backend:r})),h=e.map(m=>d0({inputs:{input:m},backend:r})),p=au(d,t,r),c=au(h,t,r),f=Oi({inputs:{real:p,imag:c},backend:r});return d.forEach(m=>r.disposeIntermediateTensorInfo(m)),h.forEach(m=>r.disposeIntermediateTensorInfo(m)),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let d=e.map(y=>{let A=w.sizeFromShape(y.shape.slice(t));return ve({inputs:{x:y},backend:r,attrs:{shape:[-1,A]}})}),h=d.map(y=>({vals:r.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(d.map(y=>y.shape),1),c=d[0].shape[0]===1,f=ZQ(h,p,n,c),m=N.computeOutShape(e.map(y=>y.shape),t),g=r.makeTensorInfo(m,n,f);return d.forEach(y=>r.disposeIntermediateTensorInfo(y)),g}if(e.length>J().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),h=au(e.slice(0,d),t,r),p=au(e.slice(d),t,r),c=au([h,p],t,r);return r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),c}if(J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new ene(e.map(h=>h.shape),t);return r.runWebGLProgram(d,e,n)}let{tensors2D:s,outShape:i}=rne(e,t,r),o=new Qre(s.map(d=>d.shape)),l=r.runWebGLProgram(o,s,n);s.forEach(d=>r.disposeIntermediateTensorInfo(d));let u=ve({inputs:{x:l},attrs:{shape:i},backend:r});return r.disposeIntermediateTensorInfo(l),u}function rne(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,w.sizeFromShape(a.shape.slice(t))]},backend:r})),outShape:n}}function z8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return dn({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),au(o,s,r)}var nne={kernelName:$o,backendName:"webgl",kernelFunc:z8},O8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,d=e.dilationWidth,h=e.filterHeight,p=e.filterWidth,c=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,A=m?3:1,x="",b="";r&&(n?x=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?x=`float activation(float a) {
@@ -2471,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
 
         ${n.output} = result;
       }
-    `}};function DS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>MS)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(_p(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let C=Tf({a:x,b:v,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),S=n.texData.get(C.dataId);w.assert(S.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,S.shape=r.outShape,g=dn({inputs:{x:C},backend:n}),g.shape=r.outShape,y.push(C)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=Tf({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function LS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),C=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(C);let S=new sne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(S,[v],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,O=o==="leakyrelu",z=o?l0(o,!0):null,j=new RS(_.shape,C.shape,[1,g,r.outChannels],A,x,M,z,I,O),X=[_,C];if(a&&X.push(a),I&&X.push(s),O){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function ine(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=DS({x:a,filter:s,convInfo:p,backend:r});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=LS({x:a,filter:s,convInfo:p,backend:r});else{let m=new OS(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var one={kernelName:Ks,backendName:"webgl",kernelFunc:ine},lne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function D8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=n.texData.get(e.dataId),d=r.inChannels,h=l[0]*l[1]*l[2],p=r.outChannels,c=r.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((h===1||p===1)&&d>M8)&&u.isPacked&&c&&u.texture!=null&&l[2]%2!==0&&w.arraysEqual(u.shape.slice(-3),l.slice(-3))){let A=l[0]*l[1]*(l[2]+1),x={dataId:e.dataId,shape:[1,A,r.inChannels],dtype:e.dtype},b=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,w.assert(Pp(u.shape,x.shape),()=>`packed reshape ${u.shape} to ${x.shape} isn't free`);let v=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}});y.push(v);let C=Cf({a:x,b:v,backend:n,transposeA:f,transposeB:m,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),S=n.texData.get(C.dataId);w.assert(S.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=b,S.shape=r.outShape,g=dn({inputs:{x:C},backend:n}),g.shape=r.outShape,y.push(C)}else{let A=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],x=ve({inputs:{x:e},backend:n,attrs:{shape:[1,A,r.inChannels]}}),b=ve({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}}),v=Cf({a:x,b,transposeA:f,transposeB:m,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});g=ve({inputs:{x:v},backend:n,attrs:{shape:r.outShape}}),y.push(x),y.push(b),y.push(v)}for(let A of y)n.disposeIntermediateTensorInfo(A);return g}function L8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,outWidth:h,outHeight:p,dataFormat:c}=r,f=c==="channelsLast",m=l*u*d,g=p*h,y=[m,g],A=!0,x=!1,b=[],v=ve({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),C=ve({inputs:{x:t},backend:n,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});b.push(v),b.push(C);let S=new sne(y,r),E=[v.shape,[r.padInfo.top,r.padInfo.left],[r.strideHeight,r.strideWidth],[r.dilationHeight,r.dilationWidth],[r.inChannels],[r.filterWidth*r.inChannels],[r.outWidth]],R=n.runWebGLProgram(S,[v],"float32",E),_=ve({inputs:{x:R},backend:n,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(_);let M=a!=null,I=s!=null,O=o==="leakyrelu",z=o?o0(o,!0):null,j=new R8(_.shape,C.shape,[1,g,r.outChannels],A,x,M,z,I,O),X=[_,C];if(a&&X.push(a),I&&X.push(s),O){let ee=n.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));X.push(ee),b.push(ee)}let D=n.runWebGLProgram(j,X,"float32"),Q=f?[1,p,h,r.outChannels]:[1,r.outChannels,p,h],V=ve({inputs:{x:D},backend:n,attrs:{shape:Q}});b.push(D);for(let ee of b)n.disposeIntermediateTensorInfo(ee);return V}function ine(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h),c;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))c=D8({x:a,filter:s,convInfo:p,backend:r});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)c=L8({x:a,filter:s,convInfo:p,backend:r});else{let m=new O8(p);c=r.runWebGLProgram(m,[a,s],"float32")}let f=ve({inputs:{x:c},backend:r,attrs:{shape:p.outShape}});return r.disposeIntermediateTensorInfo(c),f}var one={kernelName:Ks,backendName:"webgl",kernelFunc:ine},lne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2665,7 +2666,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function hne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new lne(p);return r.runWebGLProgram(c,[a,s],"float32")}var cne={kernelName:Lf,backendName:"webgl",kernelFunc:hne};function fne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new une(p);return r.runWebGLProgram(c,[a,s],"float32")}var mne={kernelName:Xs,backendName:"webgl",kernelFunc:fne};function gne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new ane(u);return r.runWebGLProgram(d,[a,s],"float32")}var yne={kernelName:Wp,backendName:"webgl",kernelFunc:gne};function Ane(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new dne(u);return r.runWebGLProgram(d,[a,s],"float32")}var xne={kernelName:Bf,backendName:"webgl",kernelFunc:Ane};function bne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new pne(u);return r.runWebGLProgram(d,[a,s],"float32")}var vne={kernelName:Wf,backendName:"webgl",kernelFunc:bne},wne=xd+`
+    `}};function hne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,dataFormat:l,dimRoundingMode:u,filterShape:d}=n,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,d,i,1,o,u,!1,h),c=new lne(p);return r.runWebGLProgram(c,[a,s],"float32")}var cne={kernelName:Df,backendName:"webgl",kernelFunc:hne};function fne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{inputShape:i,strides:o,pad:l,dataFormat:u,dimRoundingMode:d}=n,h=N.convertConv2DDataFormat(u),p=N.computeConv2DInfo(i,s.shape,o,1,l,d,!1,h),c=new une(p);return r.runWebGLProgram(c,[a,s],"float32")}var mne={kernelName:Xs,backendName:"webgl",kernelFunc:fne};function gne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeConv3DInfo(a.shape,s.shape,i,l,o),d=new ane(u);return r.runWebGLProgram(d,[a,s],"float32")}var yne={kernelName:Bp,backendName:"webgl",kernelFunc:gne};function Ane(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,pad:o,filterShape:l}=n,u=N.computeConv3DInfo(a.shape,l,i,1,o),d=new dne(u);return r.runWebGLProgram(d,[a,s],"float32")}var xne={kernelName:Lf,backendName:"webgl",kernelFunc:Ane};function bne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{pad:i,strides:o,inputShape:l}=n,u=N.computeConv3DInfo(l,s.shape,o,1,i),d=new pne(u);return r.runWebGLProgram(d,[a,s],"float32")}var vne={kernelName:Bf,backendName:"webgl",kernelFunc:bne},wne=Ad+`
   return cos(x);
 `,kne=it({opSnippet:wne}),Ine={kernelName:Zs,backendName:"webgl",kernelFunc:kne},Sne=`
   float e2x = exp(-x);
@@ -2731,20 +2732,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
           setOutput(newValue);
         }
       }
-    `}},Ene=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Nne(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Rne={kernelName:_o,backendName:"webgl",kernelFunc:Ene},kv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${Iv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
+    `}},Ene=e=>{let{inputs:t,backend:r,attrs:n}=e,{image:a,boxes:s,boxInd:i}=t,{cropSize:o,method:l,extrapolationValue:u}=n,d=new Nne(a.shape,s.shape,o,l,u);return r.runWebGLProgram(d,[a,s,i],"float32")},Rne={kernelName:_o,backendName:"webgl",kernelFunc:Ene},wv=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let n=e.length,a=t?"0.0":`getX(${kv(n,"coords")})`,s=e[e.length-1],i="",o="";t?(i=r?`end != ${s-1}`:"end != 0",o=r?"end + 1":"end - 1"):(i=r?`end + pow2 < ${s}`:"end >= pow2",o=r?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${bt(n)} coords = getOutputCoords();
-        int end = ${Sv(n,"coords")};
+        int end = ${Iv(n,"coords")};
         float val = ${a};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${Sv(n,"coords")} = idx;
-          val += getX(${Iv(n,"coords")});
+          ${Iv(n,"coords")} = idx;
+          val += getX(${kv(n,"coords")});
         }
         setOutput(val);
       }
-    `}};function Iv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Sv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Mne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=Br({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=dn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new kv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new kv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=Br({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Fne={kernelName:Po,backendName:"webgl",kernelFunc:Mne};function $ne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=AS(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=KQ(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Pne={kernelName:Vf,backendName:"webgl",kernelFunc:$ne},_ne=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
+    `}};function kv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Iv(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Mne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,exclusive:i,reverse:o}=n,l=a.shape.length,u=N.getAxesPermutation([s],l),d=a;u!=null&&(d=Br({inputs:{x:a},backend:r,attrs:{perm:u}}));let h=N.getInnerMostAxes(1,l)[0];if(h!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${a.shape.length-1} but got axis=${s}`);let p=d.shape[h],c=dn({inputs:{x:d},backend:r});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new wv(d.shape,!1,o),g=[[f]],y=c;c=r.runWebGLProgram(m,[c],c.dtype,g),r.disposeIntermediateTensorInfo(y)}if(i){let f=new wv(d.shape,i,o),m=c;c=r.runWebGLProgram(f,[c],c.dtype),r.disposeIntermediateTensorInfo(m)}if(u!=null){let f=N.getUndoAxesPermutation(u),m=Br({inputs:{x:c},backend:r,attrs:{perm:f}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(d),m}return c}var Fne={kernelName:Po,backendName:"webgl",kernelFunc:Mne};function $ne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,weights:s}=t,{size:i,binaryOutput:o}=n;if(a.shape.length===1){let l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=A8(l,u,s.dtype,s.shape,i);return r.makeTensorInfo([i],s.dtype,d)}else if(a.shape.length===2){let l=r.bufferSync(a),u=r.bufferSync(s),d=KQ(l,u,i,o);return r.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}var Pne={kernelName:Wf,backendName:"webgl",kernelFunc:$ne},_ne=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=r,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2763,7 +2764,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new _ne(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var One={kernelName:zo,backendName:"webgl",kernelFunc:zne},BS=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=nn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=new _ne(f,s,i);return r.runWebGLProgram(m,[a],a.dtype)}var One={kernelName:zo,backendName:"webgl",kernelFunc:zne},B8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=nn(this.outputShape.length);let s=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,l="",u="";r&&(n?l=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${r}
         }`:a?l=`float activation(float a) {
@@ -2816,7 +2817,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         ${u}
         setOutput(result);
       }
-    `}},WS=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=nn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
+    `}},W8=class{constructor(e,t=!1,r=null,n=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=nn(this.outputShape.length);let s=e.outChannels/e.inChannels,i=e.padInfo.left,o=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,d=e.filterWidth,h=d,p=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<d;g++)p+=`
           vec4 xTexelC${g*2};
@@ -3003,7 +3004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         ${f}
         setOutput(result);
       }
-    `}};function Dne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new WS(h):p=new BS(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Lne={kernelName:Js,backendName:"webgl",kernelFunc:Dne},Bne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
+    `}};function Dne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p;J().getBool("WEBGL_PACK_DEPTHWISECONV")&&h.strideWidth<=2&&h.outChannels/h.inChannels===1?p=new W8(h):p=new B8(h);let c=[[h.padInfo.top,h.padInfo.left],[h.strideHeight,h.strideWidth],[h.dilationHeight,h.dilationWidth],[h.inHeight,h.inWidth]];return r.runWebGLProgram(p,[a,s],"float32",c)}var Lne={kernelName:Js,backendName:"webgl",kernelFunc:Dne},Bne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,r=e.strideWidth,n=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3083,13 +3084,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function Vne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new Bne(h);return r.runWebGLProgram(p,[a,s],"float32")}var Une={kernelName:Uf,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Wne(h);return r.runWebGLProgram(p,[a,s],"float32")}var jne={kernelName:Gf,backendName:"webgl",kernelFunc:Gne},Hne=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function Vne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,dy:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,filterShape:d}=n,h=N.computeConv2DInfo(a.shape,d,i,o,l,u,!0),p=new Bne(h);return r.runWebGLProgram(p,[a,s],"float32")}var Une={kernelName:Vf,backendName:"webgl",kernelFunc:Vne};function Gne(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,filter:s}=t,{strides:i,dilations:o,pad:l,dimRoundingMode:u,inputShape:d}=n,h=N.computeConv2DInfo(d,s.shape,i,o,l,u,!0),p=new Wne(h);return r.runWebGLProgram(p,[a,s],"float32")}var jne={kernelName:Uf,backendName:"webgl",kernelFunc:Gne},Hne=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function qne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new Hne(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Kne={kernelName:jf,backendName:"webgl",kernelFunc:qne},Xne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
+    `}};function qne(e){let{inputs:t,backend:r}=e,{x:n}=t,a=[...n.shape,...n.shape],s=w.sizeFromShape(n.shape),i=ve({inputs:{x:n},backend:r,attrs:{shape:[s]}}),o=new Hne(s),l=r.runWebGLProgram(o,[i],i.dtype),u=ve({inputs:{x:l},backend:r,attrs:{shape:a}});return r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(l),u}var Kne={kernelName:Gf,backendName:"webgl",kernelFunc:qne},Xne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:r,padInfo:n,strideHeight:a,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:l,dilationWidth:u}=e,{top:d,left:h}=n;this.userCode=`
       const ivec2 strides = ivec2(${a}, ${s});
       const ivec2 pads = ivec2(${d}, ${h});
       const float neg_infinity = -3.4e38;
@@ -3127,7 +3128,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         float result = curVal;
         setOutput(result);
       }
-    `}};function Zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Xne(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Yne={kernelName:Vp,backendName:"webgl",kernelFunc:Zne};function Jne(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Br({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=Yx({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=d0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var Qne={kernelName:Up,backendName:"webgl",kernelFunc:Jne},eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",tae=`
+    `}};function Zne(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l}=n,u=N.computeDilation2DInfo(a.shape,s.shape,i,o,"NHWC",l),d,h=new Xne(u);d=r.runWebGLProgram(h,[a,s],"float32");let p=ve({inputs:{x:d},backend:r,attrs:{shape:u.outShape}});return r.disposeIntermediateTensorInfo(d),p}var Yne={kernelName:Wp,backendName:"webgl",kernelFunc:Zne};function Jne(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Br({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=ve({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=Zx({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=u0({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeIntermediateTensorInfo(m);return p}var Qne={kernelName:Vp,backendName:"webgl",kernelFunc:Jne},eae="return (x >= 0.0) ? x : (exp(x) - 1.0);",tae=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3139,7 +3140,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
 `,rae=it({opSnippet:eae,packedOpSnippet:tae}),nae={kernelName:ei,backendName:"webgl",kernelFunc:rae},aae="return (b >= 1.0) ? a : a * (b + 1.0);",sae=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,iae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Eh(sae,n.shape,a.shape):new Iu(aae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},oae={kernelName:Hf,backendName:"webgl",kernelFunc:iae},lae=`
+`,iae=e=>{let{inputs:t,backend:r}=e,{dy:n,y:a}=t,s=J().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nh(sae,n.shape,a.shape):new ku(aae,n.shape,a.shape);return r.runWebGLProgram(s,[n,a],n.dtype)},oae={kernelName:jf,backendName:"webgl",kernelFunc:iae},lae=`
   return vec4(equal(a, b));
 `,uae="return float(a == b);",dae=Ar({opSnippet:uae,packedOpSnippet:lae,dtype:"bool",cpuKernelImpl:YQ}),pae={kernelName:Oo,backendName:"webgl",kernelFunc:dae},hae=`
   // Error function is calculated approximately with elementary function.
@@ -3156,7 +3157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,cae=it({opSnippet:hae}),fae={kernelName:Du,backendName:"webgl",kernelFunc:cae},mae=xd+`
+`,cae=it({opSnippet:hae}),fae={kernelName:Ou,backendName:"webgl",kernelFunc:cae},mae=Ad+`
   return exp(x);
 `,gae=`
   vec4 result = exp(x);
@@ -3167,7 +3168,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,VS=it({opSnippet:mae,packedOpSnippet:gae,cpuKernelImpl:JQ,dtype:"float32"}),yae={kernelName:ti,backendName:"webgl",kernelFunc:VS};function zy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Aae={kernelName:Do,backendName:"webgl",kernelFunc:zy},Cv="return exp(x) - 1.0;",xae=it({opSnippet:Cv,packedOpSnippet:Cv,cpuKernelImpl:QQ}),bae={kernelName:Lo,backendName:"webgl",kernelFunc:xae},Tv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,V8=it({opSnippet:mae,packedOpSnippet:gae,cpuKernelImpl:JQ,dtype:"float32"}),yae={kernelName:ti,backendName:"webgl",kernelFunc:V8};function _y(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),ve({inputs:{x:s},backend:n,attrs:{shape:o}})}var Aae={kernelName:Do,backendName:"webgl",kernelFunc:_y},Sv="return exp(x) - 1.0;",xae=it({opSnippet:Sv,packedOpSnippet:Sv,cpuKernelImpl:QQ}),bae={kernelName:Lo,backendName:"webgl",kernelFunc:xae},Cv=class{constructor(e,t,r){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let a=r?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=r?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${a};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3200,12 +3201,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function US(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Tv("real",l,t),d=new Tv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Oi({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function vae(e){let{inputs:t,backend:r}=e,{input:n}=t;return US(n,!1,r)}var wae={kernelName:qf,backendName:"webgl",kernelFunc:vae},kae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function U8(e,t,r){let n=r.texData.get(e.dataId),a=w.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=a/s,o=ve({inputs:{x:e},backend:r,attrs:{shape:[i,s]}}),l=o.shape,u=new Cv("real",l,t),d=new Cv("imag",l,t),h=[{dataId:n.complexTensorInfos.real.dataId,dtype:n.complexTensorInfos.real.dtype,shape:l},{dataId:n.complexTensorInfos.imag.dataId,dtype:n.complexTensorInfos.imag.dtype,shape:l}],p=r.runWebGLProgram(u,h,"float32"),c=r.runWebGLProgram(d,h,"float32"),f=Oi({inputs:{real:p,imag:c},backend:r});r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c);let m=ve({inputs:{x:f},backend:r,attrs:{shape:e.shape}});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(f),m}function vae(e){let{inputs:t,backend:r}=e,{input:n}=t;return U8(n,!1,r)}var wae={kernelName:Hf,backendName:"webgl",kernelFunc:vae},kae=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function Mh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new kae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Iae={kernelName:Lu,backendName:"webgl",kernelFunc:Mh},Sae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function Rh(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new kae(n,a),o=[[a]];return t.runWebGLProgram(i,[],s,o)}}var Iae={kernelName:Du,backendName:"webgl",kernelFunc:Rh},Sae=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3219,7 +3220,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
           }
           setOutput(outputValue);
         }
-    `}},Cae={kernelName:Bo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Sae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Nv="return floor(x);",Tae=it({opSnippet:Nv,packedOpSnippet:Nv,cpuKernelImpl:eee}),Nae={kernelName:ri,backendName:"webgl",kernelFunc:Tae},Eae=`
+    `}},Cae={kernelName:Bo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Sae(r.shape);return n.runWebGLProgram(a,[r],r.dtype)}},Tv="return floor(x);",Tae=it({opSnippet:Tv,packedOpSnippet:Tv,cpuKernelImpl:eee}),Nae={kernelName:ri,backendName:"webgl",kernelFunc:Tae},Eae=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3306,7 +3307,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
 
         ${t.output} = result;
       }
-    `}},_ae={kernelName:Ip,backendName:"webgl",kernelFunc:zae},Ql;function zae(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(Ql==null&&(Ql=document.createElement("canvas").getContext("2d")),Ql.canvas.width=l,Ql.canvas.height=u,Ql.drawImage(a,0,0,l,u),a=Ql.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=J().getBool("WEBGL_PACK")?new Pae(h):new $ae(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function Oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=DS({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=LS({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,C=c==="leakyrelu",S=c?l0(c,!1):null,E=new OS(g,b,S,v,C),R=[a,s];if(i&&R.push(i),o&&R.push(o),C){let _=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Dae={kernelName:Ns,backendName:"webgl",kernelFunc:Oae};function Lae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?l0(p,y):null,x=[a,s],b=i!=null,v=o!=null,C=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),C){let _=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(_),f.push(_)}let S;y?S=new WS(g,b,A,v,C):S=new BS(g,b,A,v,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(S,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var Bae={kernelName:Es,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=bt(t.length),a=bt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},_ae={kernelName:kp,backendName:"webgl",kernelFunc:zae},Ql;function zae(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n,i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,[l,u]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],d=[u,l],h=[u,l,s];(o||i)&&(Ql==null&&(Ql=document.createElement("canvas").getContext("2d")),Ql.canvas.width=l,Ql.canvas.height=u,Ql.drawImage(a,0,0,l,u),a=Ql.canvas);let p=r.makeTensorInfo(d,"int32");r.texData.get(p.dataId).usage=2,r.gpgpu.uploadPixelDataToTexture(r.getTexture(p.dataId),a);let c=J().getBool("WEBGL_PACK")?new Pae(h):new $ae(h),f=r.runWebGLProgram(c,[p],"int32");return r.disposeData(p.dataId),f}function Oae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m),y,A=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=D8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else if(J().getBool("WEBGL_CONV_IM2COL")&&a.shape[0]===1)y=L8({x:a,filter:s,convInfo:g,backend:r,bias:i,activation:c,preluActivationWeights:o,leakyreluAlpha:f});else{let b=i!=null,v=o!=null,C=c==="leakyrelu",S=c?o0(c,!1):null,E=new O8(g,b,S,v,C),R=[a,s];if(i&&R.push(i),o&&R.push(o),C){let _=r.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));R.push(_),A.push(_)}y=r.runWebGLProgram(E,R,"float32")}let x=ve({inputs:{x:y},backend:r,attrs:{shape:g.outShape}});return A.push(y),A.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Dae={kernelName:Ns,backendName:"webgl",kernelFunc:Oae};function Lae(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p,leakyreluAlpha:c}=n,f=[],m=d;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(a.shape,s.shape,l,m,u,h,!0),y=J().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,A=p?o0(p,y):null,x=[a,s],b=i!=null,v=o!=null,C=p==="leakyrelu";if(b&&x.push(i),v&&x.push(o),C){let _=r.makeTensorInfo([],"float32",w.createScalarValue(c,"float32"));x.push(_),f.push(_)}let S;y?S=new W8(g,b,A,v,C):S=new B8(g,b,A,v,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=r.runWebGLProgram(S,x,"float32",E);return f.forEach(_=>r.disposeIntermediateTensorInfo(_)),R}var Bae={kernelName:Es,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t,r){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=r;let n=bt(t.length),a=bt(r.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${n} strides = ${n}(${this.strides});
          void main() {
           ${a} coords = getOutputCoords();
@@ -3324,15 +3325,15 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${n}));
       }
-    `}};function jae(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function GS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(J().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=ree(x,A,f);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Gae(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var Hae={kernelName:Wo,backendName:"webgl",kernelFunc:GS},qae="return float(a > b);",Kae=`
+    `}};function jae(e,t){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push("index"):n.push(`${r[a]}`);return n.join()}function G8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0];if(J().get("DEBUG")){let A=r.readSync(s.dataId),x=a.shape[l];for(let b=0;b<A.length;++b){let v=A[b];w.assert(v<=x-1&&v>=0,()=>`GatherV2: the index value ${v} is not in [0, ${x-1}]`)}}let u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=ve({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=ve({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])||a.dtype==="string"){let A=r.bufferSync(c),x=r.bufferSync(p),b=ree(x,A,f);return h.forEach(v=>r.disposeIntermediateTensorInfo(v)),r.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Gae(p.shape,f),g=r.runWebGLProgram(m,[p,c],p.dtype);h.push(g);let y=ve({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeIntermediateTensorInfo(A)),y}var Hae={kernelName:Wo,backendName:"webgl",kernelFunc:G8},qae="return float(a > b);",Kae=`
   return vec4(greaterThan(a, b));
 `,Xae=Ar({opSnippet:qae,packedOpSnippet:Kae,cpuKernelImpl:nee,dtype:"bool"}),Zae={kernelName:Uo,backendName:"webgl",kernelFunc:Xae},Yae="return float(a >= b);",Jae=`
   return vec4(greaterThanEqual(a, b));
-`,Qae=Ar({opSnippet:Yae,packedOpSnippet:Jae,dtype:"bool",cpuKernelImpl:aee}),ese={kernelName:si,backendName:"webgl",kernelFunc:Qae};function tse(e){let{inputs:t,backend:r}=e,{input:n}=t;return US(n,!0,r)}var rse={kernelName:Kf,backendName:"webgl",kernelFunc:tse},nse="return float(!isnan(x) && !isinf(x));",ase=it({opSnippet:nse,dtype:"bool"}),sse={kernelName:Bu,backendName:"webgl",kernelFunc:ase},ise="return float(isinf(x));",ose=it({opSnippet:ise,dtype:"bool"}),lse={kernelName:Wu,backendName:"webgl",kernelFunc:ose},use="return float(isnan(x));",dse=it({opSnippet:use,dtype:"bool"}),pse={kernelName:Vu,backendName:"webgl",kernelFunc:dse},hse="return float(a < b);",cse=`
+`,Qae=Ar({opSnippet:Yae,packedOpSnippet:Jae,dtype:"bool",cpuKernelImpl:aee}),ese={kernelName:si,backendName:"webgl",kernelFunc:Qae};function tse(e){let{inputs:t,backend:r}=e,{input:n}=t;return U8(n,!0,r)}var rse={kernelName:qf,backendName:"webgl",kernelFunc:tse},nse="return float(!isnan(x) && !isinf(x));",ase=it({opSnippet:nse,dtype:"bool"}),sse={kernelName:Lu,backendName:"webgl",kernelFunc:ase},ise="return float(isinf(x));",ose=it({opSnippet:ise,dtype:"bool"}),lse={kernelName:Bu,backendName:"webgl",kernelFunc:ose},use="return float(isnan(x));",dse=it({opSnippet:use,dtype:"bool"}),pse={kernelName:Wu,backendName:"webgl",kernelFunc:dse},hse="return float(a < b);",cse=`
   return vec4(lessThan(a, b));
 `,fse=Ar({opSnippet:hse,packedOpSnippet:cse,cpuKernelImpl:see,dtype:"bool"}),mse={kernelName:Go,backendName:"webgl",kernelFunc:fse},gse="return float(a <= b);",yse=`
   return vec4(lessThanEqual(a, b));
-`,Ase=Ar({opSnippet:gse,packedOpSnippet:yse,cpuKernelImpl:iee,dtype:"bool"}),xse={kernelName:jo,backendName:"webgl",kernelFunc:Ase};function bse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=oee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var vse={kernelName:Xf,backendName:"webgl",kernelFunc:bse},wse=xd+`
+`,Ase=Ar({opSnippet:gse,packedOpSnippet:yse,cpuKernelImpl:iee,dtype:"bool"}),xse={kernelName:jo,backendName:"webgl",kernelFunc:Ase};function bse(e){let{backend:t,attrs:r}=e,{start:n,stop:a,num:s}=r,i=oee(n,a,s);return t.makeTensorInfo([i.length],"float32",i)}var vse={kernelName:Kf,backendName:"webgl",kernelFunc:bse},wse=Ad+`
   return x < 0.0 ? 0./0. : log(x);
 `,kse=`
   vec4 result = log(x);
@@ -3342,18 +3343,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,Ise=it({opSnippet:wse,packedOpSnippet:kse,cpuKernelImpl:lee}),Sse={kernelName:li,backendName:"webgl",kernelFunc:Ise},Cse=xd+`
+`,Ise=it({opSnippet:wse,packedOpSnippet:kse,cpuKernelImpl:lee}),Sse={kernelName:li,backendName:"webgl",kernelFunc:Ise},Cse=Ad+`
   return log(1.0 + x);
-`,Tse=it({opSnippet:Cse}),Nse={kernelName:Uu,backendName:"webgl",kernelFunc:Tse},Ese="return float(a >= 1.0 && b >= 1.0);",Rse=`
+`,Tse=it({opSnippet:Cse}),Nse={kernelName:Vu,backendName:"webgl",kernelFunc:Tse},Ese="return float(a >= 1.0 && b >= 1.0);",Rse=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,Mse=Ar({opSnippet:Ese,packedOpSnippet:Rse,dtype:"bool"}),Fse={kernelName:Ho,backendName:"webgl",kernelFunc:Mse},$se="return float(!(x >= 1.0));",Pse=it({opSnippet:$se}),_se={kernelName:Gu,backendName:"webgl",kernelFunc:Pse},zse="return float(a >= 1.0 || b >= 1.0);",Ose=`
+`,Mse=Ar({opSnippet:Ese,packedOpSnippet:Rse,dtype:"bool"}),Fse={kernelName:Ho,backendName:"webgl",kernelFunc:Mse},$se="return float(!(x >= 1.0));",Pse=it({opSnippet:$se}),_se={kernelName:Uu,backendName:"webgl",kernelFunc:Pse},zse="return float(a >= 1.0 || b >= 1.0);",Ose=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,Dse=Ar({opSnippet:zse,packedOpSnippet:Ose,dtype:"bool"}),Lse={kernelName:jp,backendName:"webgl",kernelFunc:Dse},Bse=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
+`,Dse=Ar({opSnippet:zse,packedOpSnippet:Ose,dtype:"bool"}),Lse={kernelName:Gp,backendName:"webgl",kernelFunc:Dse},Bse=class{constructor(e,t,r,n,a){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,l=`float(${r}) + float(${n}) * sum`;a===.5?o=`inversesqrt(${l})`:a===1?o=`1.0/(${l})`:o=`exp(log(${l}) * float(-${a}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3434,7 +3435,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},Vse=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new Wse(a.shape,s,i,o,l):new Bse(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},Use={kernelName:Hp,backendName:"webgl",kernelFunc:Vse},Gse=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
+    `}},Vse=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{depthRadius:s,bias:i,alpha:o,beta:l}=n,u=J().getBool("WEBGL_PACK_NORMALIZATION")?new Wse(a.shape,s,i,o,l):new Bse(a.shape,s,i,o,l);return r.runWebGLProgram(u,[a],a.dtype)},Use={kernelName:jp,backendName:"webgl",kernelFunc:Vse},Gse=class{constructor(e,t,r,n,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=r,this.alpha=n,this.beta=a,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3489,14 +3490,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
       }
       setOutput(result);
       }
-    `}},jse=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new Gse(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},Hse={kernelName:Zf,backendName:"webgl",kernelFunc:jse};function qse(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Tl(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function jS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[d[C]];let b=Zx(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=u0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=uee(A,w.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=qse(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var Kse={kernelName:ui,backendName:"webgl",kernelFunc:jS},Xse=SS+`
+    `}},jse=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a,y:s,dy:i}=t,{depthRadius:o,bias:l,alpha:u,beta:d}=n,h=new Gse(a.shape,o,l,u,d);return r.runWebGLProgram(h,[a,s,i],a.dtype)},Hse={kernelName:Xf,backendName:"webgl",kernelFunc:jse};function qse(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Tl(i,e.dtype,"max",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}function j8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=r.shouldExecuteOnCPU([a]),c=a;if(h){if(p){let A=r.texData.get(c.dataId).values,x=new Array(o);for(let C=0;C<x.length;C++)x[C]=a.shape[d[C]];let b=Xx(A,a.shape,a.dtype,d,x);c=r.makeTensorInfo(x,a.dtype);let v=r.texData.get(c.dataId);v.values=b}else c=l0(a,d,r);u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("max",u,o);let[f,m]=N.computeOutAndReduceShapes(c.shape,u),g=f;i&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=r.texData.get(c.dataId).values,x=uee(A,w.sizeFromShape(m),g,a.dtype);y=r.makeTensorInfo(g,a.dtype);let b=r.texData.get(y.dataId);b.values=x}else y=qse(c,m,g,r);return h&&r.disposeIntermediateTensorInfo(c),y}var Kse={kernelName:ui,backendName:"webgl",kernelFunc:j8},Xse=S8+`
   return max(a, b);
 `,Zse=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+o0+`
+  `+i0+`
   return result;
-`,Yse=Ar({opSnippet:Xse,packedOpSnippet:Zse,cpuKernelImpl:dee}),Jse={kernelName:di,backendName:"webgl",kernelFunc:Yse};function Qse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;fd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return dn({inputs:{x:a},backend:r});let h=new zp(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var eie={kernelName:pi,backendName:"webgl",kernelFunc:Qse};function tie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new Jx(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var rie={kernelName:qp,backendName:"webgl",kernelFunc:tie},nie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
+`,Yse=Ar({opSnippet:Xse,packedOpSnippet:Zse,cpuKernelImpl:dee}),Jse={kernelName:di,backendName:"webgl",kernelFunc:Yse};function Qse(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t;cd(a,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1;w.assert(N.eitherStridesOrDilationsAreOne(i,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${u}'`);let d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return dn({inputs:{x:a},backend:r});let h=new _p(d,"max",!1);return r.runWebGLProgram(h,[a],a.dtype)}var eie={kernelName:pi,backendName:"webgl",kernelFunc:Qse};function tie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dataFormat:l,dimRoundingMode:u}=n,d=[1,1,1],h=N.computePool3DInfo(a.shape,s,i,d,o,u,l),p=new Yx(h,"max",!1);return r.runWebGLProgram(p,[a],a.dtype)}var rie={kernelName:Hp,backendName:"webgl",kernelFunc:tie},nie=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,r=e.strideWidth,n=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=a-1-e.padInfo.top,o=s-1-e.padInfo.left,l=a*s-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3606,12 +3607,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
         }
         setOutput(dotProd);
       }
-    `}};function sie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Jx(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new aie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var iie={kernelName:Jf,backendName:"webgl",kernelFunc:sie};function oie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;fd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new zp(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new nie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var lie={kernelName:Yf,backendName:"webgl",kernelFunc:oie};function uie(e,t,r,n){let a=new zp(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new zp(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var die={kernelName:Qf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=uie(n,o,d,l);return[h,p]}};function pie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Tl(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var hie={kernelName:hi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=n.shape[d[S]];let v=Zx(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let C=i.texData.get(f.dataId);C.values=v}else f=u0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=pie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function cie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var fie={kernelName:ci,backendName:"webgl",kernelFunc:cie},mie=SS+`
+    `}};function sie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s}=t,i=s,{filterSize:o,strides:l,pad:u,dimRoundingMode:d}=n,h=[1,1,1],p=N.computePool3DInfo(i.shape,o,l,h,u,d),c=new Yx(p,"max",!0),f=r.runWebGLProgram(c,[i],i.dtype),m=new aie(p),g=r.runWebGLProgram(m,[a,f],i.dtype);return r.disposeIntermediateTensorInfo(f),g}var iie={kernelName:Yf,backendName:"webgl",kernelFunc:sie};function oie(e){let{inputs:t,backend:r,attrs:n}=e,{dy:a,input:s,output:i}=t,o=s;cd([s,i],"maxPoolGrad");let{filterSize:l,strides:u,pad:d,dimRoundingMode:h}=n,p=N.computePool2DInfo(o.shape,l,u,1,d,h),c=!0,f=new _p(p,"max",c),m=r.runWebGLProgram(f,[o],o.dtype),g=new nie(p),y=r.runWebGLProgram(g,[a,m],o.dtype);return r.disposeIntermediateTensorInfo(m),y}var lie={kernelName:Zf,backendName:"webgl",kernelFunc:oie};function uie(e,t,r,n){let a=new _p(r,"max",!1),s=n.runWebGLProgram(a,[e],"float32");a=new _p(r,"max",!0,!0,t);let i=n.runWebGLProgram(a,[e],"float32");return[s,i]}var die={kernelName:Jf,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{filterSize:a,strides:s,pad:i,includeBatchInIndex:o}=t,l=r;w.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let d=N.computePool2DInfo(n.shape,a,s,u,i),[h,p]=uie(n,o,d,l);return[h,p]}};function pie(e,t,r,n){let a=w.sizeFromShape(t),s=w.sizeFromShape(e.shape)/a,i=ve({inputs:{x:e},attrs:{shape:[s,a]},backend:n}),o=Tl(i,"float32","mean",n),l=ve({inputs:{x:o},attrs:{shape:r},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),l}var hie={kernelName:hi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{keepDims:a,axis:s}=t,i=r,o=n.shape.length,l=w.parseAxisParam(s,n.shape),u=l,d=N.getAxesPermutation(u,o),h=d!=null,p=i.shouldExecuteOnCPU([n]),c=[],f=n;if(h){if(p){let x=i.texData.get(f.dataId).values,b=new Array(o);for(let S=0;S<b.length;S++)b[S]=n.shape[d[S]];let v=Xx(x,n.shape,n.dtype,d,b);f=i.makeTensorInfo(b,n.dtype);let C=i.texData.get(f.dataId);C.values=v}else f=l0(n,d,i);c.push(f),u=N.getInnerMostAxes(u.length,o)}N.assertAxesAreInnerMostDims("sum",u,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,u),y=m;a&&(y=N.expandShapeToKeepDim(m,l));let A=pie(f,g,y,i);for(let x of c)i.disposeIntermediateTensorInfo(x);return A}};function cie(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=w.parseAxisParam(s,a.shape),u=l,d=N.getAxesPermutation(u,o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,a.shape.length)),N.assertAxesAreInnerMostDims("min",u,o);let[p,c]=N.computeOutAndReduceShapes(h.shape,u),f=w.sizeFromShape(c),m=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,f]}}),g=Tl(m,m.dtype,"min",r),y;if(i){let A=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:r,attrs:{shape:A}})}else y=ve({inputs:{x:g},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(m),r.disposeIntermediateTensorInfo(g),d!=null&&r.disposeIntermediateTensorInfo(h),y}var fie={kernelName:ci,backendName:"webgl",kernelFunc:cie},mie=S8+`
   return min(a, b);
 `,gie=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+o0+`
+  `+i0+`
   return result;
 `,yie=Ar({opSnippet:mie,packedOpSnippet:gie,cpuKernelImpl:pee}),Aie={kernelName:fi,backendName:"webgl",kernelFunc:yie},xie=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=t.map((u,d)=>u[0]+e[d]+u[1]);let n=e.length,a=bt(n),s=t.map(u=>u[0]).join(","),i=t.map((u,d)=>u[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),l=r==="reflect"?0:1;if(n===1){this.userCode=`
         int start = ${s};
@@ -3702,9 +3703,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ure=it({opSnippet:lre}),dre={kernel
   return mod(a, b);`,Iie=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+o0+`
+  `+i0+`
   return result;
-`,Sie=Ar({opSnippet:kie,packedOpSnippet:Iie}),Cie={kernelName:ju,backendName:"webgl",kernelFunc:Sie},Tie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
+`,Sie=Ar({opSnippet:kie,packedOpSnippet:Iie}),Cie={kernelName:Gu,backendName:"webgl",kernelFunc:Sie},Tie=class{constructor(e,t,r){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,r],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3746,7 +3747,7 @@ return a / b;`,Eie=`
   }
 
   return result;
-`,HS=Ar({opSnippet:Nie,packedOpSnippet:Eie,checkOutOfBounds:!0}),Rie={kernelName:Qs,backendName:"webgl",kernelFunc:HS},Ev="return a - b;",qS=Ar({opSnippet:Ev,packedOpSnippet:Ev,supportsComplex:!0,cpuKernelImpl:Tee}),Mie={kernelName:Ri,backendName:"webgl",kernelFunc:qS};function KS(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=jS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=qS({inputs:{a,b:u},backend:r}),h=VS({inputs:{x:d},backend:r}),p=d0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=HS({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var Fie={kernelName:Ni,backendName:"webgl",kernelFunc:KS};function $ie(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:KS({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Tie(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Pie={kernelName:em,backendName:"webgl",kernelFunc:$ie},_ie=jn+`
+`,H8=Ar({opSnippet:Nie,packedOpSnippet:Eie,checkOutOfBounds:!0}),Rie={kernelName:Qs,backendName:"webgl",kernelFunc:H8},Nv="return a - b;",q8=Ar({opSnippet:Nv,packedOpSnippet:Nv,supportsComplex:!0,cpuKernelImpl:Tee}),Mie={kernelName:Ri,backendName:"webgl",kernelFunc:q8};function K8(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=j8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=ve({inputs:{x:o},backend:r,attrs:{shape:l}}),d=q8({inputs:{a,b:u},backend:r}),h=V8({inputs:{x:d},backend:r}),p=u0({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=ve({inputs:{x:p},backend:r,attrs:{shape:l}}),f=H8({inputs:{a:h,b:c},backend:r});return r.disposeIntermediateTensorInfo(o),r.disposeIntermediateTensorInfo(u),r.disposeIntermediateTensorInfo(d),r.disposeIntermediateTensorInfo(h),r.disposeIntermediateTensorInfo(p),r.disposeIntermediateTensorInfo(c),f}var Fie={kernelName:Ni,backendName:"webgl",kernelFunc:K8};function $ie(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{numSamples:s,seed:i,normalized:o}=n,l=o?a:K8({inputs:{logits:a},backend:r,attrs:{dim:a.shape.length-1}}),u=l.shape[0],d=l.shape[1],h=new Tie(u,d,s),p=[[i]],c=r.runWebGLProgram(h,[l],"int32",p);return o||r.disposeIntermediateTensorInfo(l),c}var Pie={kernelName:Qf,backendName:"webgl",kernelFunc:$ie},_ie=jn+`
   return -x;
 `,zie=`
   vec4 result = -x;
@@ -3758,14 +3759,14 @@ return a / b;`,Eie=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function Oie(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=cee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new co(n.shape,zie):a=new Ua(n.shape,_ie),r.runWebGLProgram(a,[n],n.dtype)}var Die={kernelName:qo,backendName:"webgl",kernelFunc:Oie},Lie=Un.nonMaxSuppressionV3Impl;function Bie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Lie(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Wie={kernelName:Xo,backendName:"webgl",kernelFunc:Bie},Vie=Un.nonMaxSuppressionV4Impl;function Uie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=Vie(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Gie={kernelName:Hu,backendName:"webgl",kernelFunc:Uie},jie=Un.nonMaxSuppressionV5Impl;function Hie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=jie(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var qie={kernelName:Zo,backendName:"webgl",kernelFunc:Hie},Kie=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function Oie(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.texData.get(n.dataId),[i,o]=cee(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a;return J().getBool("WEBGL_PACK_UNARY_OPERATIONS")?a=new co(n.shape,zie):a=new Ua(n.shape,_ie),r.runWebGLProgram(a,[n],n.dtype)}var Die={kernelName:qo,backendName:"webgl",kernelFunc:Oie},Lie=Un.nonMaxSuppressionV3Impl;function Bie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Lie(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Wie={kernelName:Xo,backendName:"webgl",kernelFunc:Bie},Vie=Un.nonMaxSuppressionV4Impl;function Uie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,padToMaxOutputSize:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),{selectedIndices:p,validOutputs:c}=Vie(d,h,i,o,l,u);return[r.makeTensorInfo([p.length],"int32",new Int32Array(p)),r.makeTensorInfo([],"int32",new Int32Array([c]))]}var Gie={kernelName:ju,backendName:"webgl",kernelFunc:Uie},jie=Un.nonMaxSuppressionV5Impl;function Hie(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=jie(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var qie={kernelName:Zo,backendName:"webgl",kernelFunc:Hie},Kie=class{constructor(e,t,r,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${n}), float(${r}),
                       float(index == coords.y)));
       }
-    `}},Xie=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new Kie(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Zie={kernelName:Jo,backendName:"webgl",kernelFunc:Xie};function Nf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Rh({inputs:{input:n},backend:r}),s=Nf({inputs:{x:a},backend:r}),i=p0({inputs:{input:n},backend:r}),o=Nf({inputs:{x:i},backend:r}),l=Oi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Mh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Yie={kernelName:ml,backendName:"webgl",kernelFunc:Nf};function XS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Rh({inputs:{input:n},backend:r}),s=XS({inputs:{x:a},backend:r}),i=p0({inputs:{input:n},backend:r}),o=Nf({inputs:{x:i},backend:r}),l=Oi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Mh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Jie={kernelName:Yo,backendName:"webgl",kernelFunc:XS};function Qie(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return zy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=zy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=zS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var eoe={kernelName:Qo,backendName:"webgl",kernelFunc:Qie},toe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=bt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
+    `}},Xie=e=>{let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=w.sizeFromShape(a.shape),u=new Kie(l,s,i,o),d=ve({inputs:{x:a},backend:r,attrs:{shape:[l]}}),h=r.runWebGLProgram(u,[d],a.dtype);r.disposeIntermediateTensorInfo(d);let p=[...a.shape,s],c=ve({inputs:{x:h},backend:r,attrs:{shape:p}});return r.disposeIntermediateTensorInfo(h),c},Zie={kernelName:Jo,backendName:"webgl",kernelFunc:Xie};function Tf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Eh({inputs:{input:n},backend:r}),s=Tf({inputs:{x:a},backend:r}),i=d0({inputs:{input:n},backend:r}),o=Tf({inputs:{x:i},backend:r}),l=Oi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Rh({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Yie={kernelName:ml,backendName:"webgl",kernelFunc:Tf};function X8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Eh({inputs:{input:n},backend:r}),s=X8({inputs:{x:a},backend:r}),i=d0({inputs:{input:n},backend:r}),o=Tf({inputs:{x:i},backend:r}),l=Oi({inputs:{real:s,imag:o},backend:r});return r.disposeIntermediateTensorInfo(a),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(i),r.disposeIntermediateTensorInfo(o),l}else return Rh({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var Jie={kernelName:Yo,backendName:"webgl",kernelFunc:X8};function Qie(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return _y({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=_y({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=z8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeIntermediateTensorInfo(d)),u}var eoe={kernelName:Qo,backendName:"webgl",kernelFunc:Qie},toe=class{constructor(e,t,r){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let n=e.length,a=bt(n),s=t.map(l=>l[0]).join(","),i=t.map((l,u)=>l[0]+e[u]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
         int start = ${s};
         int end = ${i};
 
@@ -3814,7 +3815,7 @@ return a / b;`,Eie=`
         ${c}
         setOutput(result);
       }
-    `}},ZS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Mh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new roe(a.shape,s,i):new toe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},noe={kernelName:yi,backendName:"webgl",kernelFunc:ZS},aoe=`
+    `}},Z8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return Rh({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=J().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new roe(a.shape,s,i):new toe(a.shape,s,i),l=[[i]];return r.runWebGLProgram(o,[a],a.dtype,l)},noe={kernelName:yi,backendName:"webgl",kernelFunc:Z8},aoe=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3837,9 +3838,9 @@ return a / b;`,Eie=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+o0+`
+  `+i0+`
   return result;
-`,ioe=Ar({opSnippet:aoe,packedOpSnippet:soe}),ooe={kernelName:Ai,backendName:"webgl",kernelFunc:ioe};function loe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=Br({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=mee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=nh(a.dtype),x=Tl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var uoe={kernelName:el,backendName:"webgl",kernelFunc:loe},YS=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=gee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},doe={kernelName:qu,backendName:"webgl",kernelFunc:YS},poe="return 1.0 / x;",hoe=it({opSnippet:poe}),coe={kernelName:Ku,backendName:"webgl",kernelFunc:hoe},foe=jn+`
+`,ioe=Ar({opSnippet:aoe,packedOpSnippet:soe}),ooe={kernelName:Ai,backendName:"webgl",kernelFunc:ioe};function loe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n,o=a.shape.length,l=[],u=w.parseAxisParam(s,a.shape),d=u,h=N.getAxesPermutation(d,o),p=a;h!=null&&(p=Br({inputs:{x:a},backend:r,attrs:{perm:h}}),d=N.getInnerMostAxes(d.length,o),l.push(p)),N.assertAxesAreInnerMostDims("prod",d,o);let c;if(r.shouldExecuteOnCPU([p])){let f=r.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=mee(p.shape,p.dtype,f,d);c=r.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,d),g=w.sizeFromShape(m),y=ve({inputs:{x:p},backend:r,attrs:{shape:[-1,g]}}),A=rh(a.dtype),x=Tl(y,A,"prod",r);c=ve({inputs:{x},backend:r,attrs:{shape:f}}),l.push(y),l.push(x)}if(i){l.push(c);let f=N.expandShapeToKeepDim(c.shape,u);c=ve({inputs:{x:c},backend:r,attrs:{shape:f}})}return l.forEach(f=>r.disposeIntermediateTensorInfo(f)),c}var uoe={kernelName:el,backendName:"webgl",kernelFunc:loe},Y8=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=gee(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},doe={kernelName:Hu,backendName:"webgl",kernelFunc:Y8},poe="return 1.0 / x;",hoe=it({opSnippet:poe}),coe={kernelName:qu,backendName:"webgl",kernelFunc:hoe},foe=jn+`
   return (x < 0.0) ? 0.0 : x;
 `,moe=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
@@ -4054,7 +4055,7 @@ return a / b;`,Eie=`
 
         setOutput(accumulator);
       }
-    `}};function Toe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Coe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Noe={kernelName:rm,backendName:"webgl",kernelFunc:Toe},Eoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function Toe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new Coe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var Noe={kernelName:tm,backendName:"webgl",kernelFunc:Toe},Eoe=class{constructor(e,t,r,n,a){this.variableNames=["A"],this.outputShape=[];let[s,i,o,l]=e;this.outputShape=[s,t,r,l];let u=[n&&t>1?i-1:i,n&&r>1?o-1:o],d=[n&&t>1?t-1:t,n&&r>1?r-1:r],h=n?"0.5":"0.0",p;a?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/d[0]},
           ${u[1]/d[1]});
@@ -4117,7 +4118,7 @@ return a / b;`,Eie=`
 
         setOutput(newValue);
       }
-    `}};function Moe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Roe(a.shape,l,u,s,i):new Eoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Foe={kernelName:Xu,backendName:"webgl",kernelFunc:Moe},$oe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
+    `}};function Moe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=J().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Roe(a.shape,l,u,s,i):new Eoe(a.shape,l,u,s,i);return r.runWebGLProgram(d,[a],a.dtype)}var Foe={kernelName:Ku,backendName:"webgl",kernelFunc:Moe},$oe=class{constructor(e,t,r){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,a]=t,[,s,i]=e,o=[r&&s>1?n-1:n,r&&i>1?a-1:a],l=[r&&s>1?s-1:s,r&&i>1?i-1:i],u=o[0]/l[0],d=o[1]/l[1],h=1/u,p=1/d,c=Math.ceil(h)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4187,7 +4188,7 @@ return a / b;`,Eie=`
 
         setOutput(accumulator);
       }
-    `}};function Poe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new $oe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var _oe={kernelName:tm,backendName:"webgl",kernelFunc:Poe},zoe=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
+    `}};function Poe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a,dy:s}=t,{alignCorners:i}=n,o=new $oe(s.shape,a.shape,i);return r.runWebGLProgram(o,[s],s.dtype)}var _oe={kernelName:em,backendName:"webgl",kernelFunc:Poe},zoe=class{constructor(e,t){this.variableNames=["x"];let r=e.length;if(r>4)throw new Error(`WebGL backend: Reverse of rank-${r} tensor is not yet supported`);if(this.outputShape=e,r===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -4259,7 +4260,7 @@ return a / b;`,Eie=`
       return base + 1.0;
     }
   }
-`,Uoe=it({opSnippet:Voe}),Goe={kernelName:nl,backendName:"webgl",kernelFunc:Uoe},joe="return inversesqrt(x);",Hoe=it({opSnippet:joe,cpuKernelImpl:yee}),qoe={kernelName:ki,backendName:"webgl",kernelFunc:Hoe},JS=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=bt(a.length),l=bt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
+`,Uoe=it({opSnippet:Voe}),Goe={kernelName:nl,backendName:"webgl",kernelFunc:Uoe},joe="return inversesqrt(x);",Hoe=it({opSnippet:joe,cpuKernelImpl:yee}),qoe={kernelName:ki,backendName:"webgl",kernelFunc:Hoe},J8=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=bt(a.length),l=bt(s.length),u="";r===1?u="i":r===2&&(u="i, j");let d=`getIndices(${u})`,h="";n===1?h="i":n===2&&(h="i, coords[1]");let p=`getUpdates(${h})`,c=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${a});
 
         void main() {
@@ -4279,7 +4280,7 @@ return a / b;`,Eie=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function Koe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new JS(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var Xoe={kernelName:al,backendName:"webgl",kernelFunc:Koe},Zoe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=bt(r);this.userCode=`
+      `}};function Koe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=ve({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=ve({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=r.makeTensorInfo([],"float32",new Float32Array([0])),g=new J8(l,o,c.shape.length,f.shape.length,d,p),y=r.runWebGLProgram(g,[f,c,m],f.dtype),A=ve({inputs:{x:y},backend:r,attrs:{shape:i}});return r.disposeIntermediateTensorInfo(c),r.disposeIntermediateTensorInfo(f),r.disposeIntermediateTensorInfo(y),r.disposeIntermediateTensorInfo(m),A}var Xoe={kernelName:al,backendName:"webgl",kernelFunc:Koe},Zoe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.outputShape=t;let n,a;if(r>4)throw Error(`Where for rank ${r} is not yet supported`);if(r===1)a="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],l=[];for(let u=0;u<t.length;u++)l.push(`${i[u]}`),u<e&&o.push(`${i[u]}`);n=o.join(),a=l.join()}let s=bt(r);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         float cVal = getC(${n});
@@ -4295,7 +4296,7 @@ return a / b;`,Eie=`
   float scaleAlpha = ${N.SELU_SCALEALPHA};
   float scale = ${N.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,ele=it({opSnippet:Qoe}),tle={kernelName:Zu,backendName:"webgl",kernelFunc:ele},rle=xd+`
+`,ele=it({opSnippet:Qoe}),tle={kernelName:Xu,backendName:"webgl",kernelFunc:ele},rle=Ad+`
   return 1.0 / (1.0 + exp(-1.0 * x));
 `,nle=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
@@ -4310,7 +4311,7 @@ return a / b;`,Eie=`
 `,ale=it({opSnippet:rle,packedOpSnippet:nle,cpuKernelImpl:Aee}),sle={kernelName:Si,backendName:"webgl",kernelFunc:ale},ile=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,ole=it({opSnippet:ile}),lle={kernelName:Yu,backendName:"webgl",kernelFunc:ole},ule=xd+`
+`,ole=it({opSnippet:ile}),lle={kernelName:Zu,backendName:"webgl",kernelFunc:ole},ule=Ad+`
   return sin(x);
 `,dle=it({opSnippet:ule}),ple={kernelName:Ii,backendName:"webgl",kernelFunc:dle},hle=`
   float e2x = exp(x);
@@ -4335,15 +4336,15 @@ return a / b;`,Eie=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,gle=it({opSnippet:mle}),yle={kernelName:Ju,backendName:"webgl",kernelFunc:gle},Ale=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=ZS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Br({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},xle={kernelName:ll,backendName:"webgl",kernelFunc:Ale};function ble(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,gle=it({opSnippet:mle}),yle={kernelName:Yu,backendName:"webgl",kernelFunc:gle},Ale=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=Z8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=ve({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Br({inputs:{x:f},backend:r,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeIntermediateTensorInfo(y)),g},xle={kernelName:ll,backendName:"webgl",kernelFunc:Ale};function ble(e){let{inputs:t,backend:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
          ${s.shape}`);if(n.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
          ${n.shape}`);if(a.shape.length!==1)throw new Error(`Values must be a vector, saw:
          ${a.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
-        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=bee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var vle={kernelName:Xp,backendName:"webgl",kernelFunc:ble};function wle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=vee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var kle={kernelName:Qu,backendName:"webgl",kernelFunc:wle};function Ile(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+        ${i.shape}`);let o=r.readSync(n.dataId),l=r.readSync(a.dataId),u=r.readSync(s.dataId),d=r.readSync(i.dataId)[0],[h,p,c,f,m]=bee(o,n.shape,n.dtype,l,a.dtype,u,d);return[r.makeTensorInfo(p,n.dtype,h),r.makeTensorInfo([p[0]],a.dtype,c),r.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),r.makeTensorInfo([m.length],n.dtype,new Int32Array(m))]}var vle={kernelName:Kp,backendName:"webgl",kernelFunc:ble};function wle(e){let{inputs:t,backend:r}=e,{inputIndices:n,inputShape:a,newShape:s}=t;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${n.shape}`);if(a.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(r.readSync(a.dataId)),o=r.readSync(n.dataId),l=Array.from(r.readSync(s.dataId)),[u,d,h]=vee(o,n.shape,n.dtype,i,l);return[r.makeTensorInfo(d,n.dtype,u),r.makeTensorInfo([h.length],s.dtype,new Int32Array(h))]}var kle={kernelName:Ju,backendName:"webgl",kernelFunc:wle};function Ile(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=bS(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Sle={kernelName:Zp,backendName:"webgl",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=b8(i,n.shape,n.dtype,o,l,!0);return r.makeTensorInfo(d,n.dtype,u)}var Sle={kernelName:Xp,backendName:"webgl",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:r}=e,{data:n,indices:a,segmentIds:s}=t;if(n.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(a.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${a.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=bS(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var Tle={kernelName:Yp,backendName:"webgl",kernelFunc:Cle};function Nle(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new JS(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var Ele={kernelName:Jp,backendName:"webgl",kernelFunc:Nle};function Rle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=bd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Mle={kernelName:ul,backendName:"webgl",kernelFunc:Rle},Rv="return sqrt(x);",Fle=it({opSnippet:Rv,packedOpSnippet:Rv,cpuKernelImpl:wee}),$le={kernelName:Ci,backendName:"webgl",kernelFunc:Fle},Ple="return x * x;",_le=it({opSnippet:Ple}),zle={kernelName:ed,backendName:"webgl",kernelFunc:_le},Mv="return (a - b) * (a - b);",Ole=Ar({opSnippet:Mv,packedOpSnippet:Mv}),Dle={kernelName:Ei,backendName:"webgl",kernelFunc:Ole};function Lle({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=jn+`
+             ${s.shape}`);let i=r.readSync(n.dataId),o=r.readSync(a.dataId),l=r.readSync(s.dataId),[u,d]=b8(i,n.shape,n.dtype,o,l);return r.makeTensorInfo(d,n.dtype,u)}var Tle={kernelName:Zp,backendName:"webgl",kernelFunc:Cle};function Nle(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=new J8(u,l,a.shape.length,s.shape.length,d,[h,1],p),f=r.runWebGLProgram(c,[s,a,i],s.dtype),m=ve({inputs:{x:f},backend:r,attrs:{shape:o}});return r.disposeIntermediateTensorInfo(f),m}var Ele={kernelName:Yp,backendName:"webgl",kernelFunc:Nle};function Rle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=xd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Mle={kernelName:ul,backendName:"webgl",kernelFunc:Rle},Ev="return sqrt(x);",Fle=it({opSnippet:Ev,packedOpSnippet:Ev,cpuKernelImpl:wee}),$le={kernelName:Ci,backendName:"webgl",kernelFunc:Fle},Ple="return x * x;",_le=it({opSnippet:Ple}),zle={kernelName:Qu,backendName:"webgl",kernelFunc:_le},Rv="return (a - b) * (a - b);",Ole=Ar({opSnippet:Rv,packedOpSnippet:Rv}),Dle={kernelName:Ei,backendName:"webgl",kernelFunc:Ole};function Lle({inputs:e,attrs:t,backend:r}){let{x:n}=e,a=jn+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `,s=new Ua(n.shape,a);return r.runWebGLProgram(s,[n],n.dtype)}var Ble={kernelName:$i,backendName:"webgl",kernelFunc:Lle},Wle=class{constructor(e,t,r){this.variableNames=["x"],this.outputShape=r;let n=r.length,a=bt(r.length),s=bt(r.length),i="";if(n===1)i="coords * strides + begin";else{let o=0;i=r.map((l,u)=>(o++,r.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${o-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${a} begin = ${a}(${e});
@@ -4353,7 +4354,7 @@ return a / b;`,Eie=`
         ${s} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function Vle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),E=bd({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),E=We(a.shape,a.dtype,S),R=kee(c,E,b,A);v=r.makeTensorInfo(f,a.dtype,R.values)}else{let S=new Wle(A,b,c);v=r.runWebGLProgram(S,[a],a.dtype)}let C=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),C}var Ule={kernelName:dl,backendName:"webgl",kernelFunc:Vle};function Gle(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Iee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var jle={kernelName:Qp,backendName:"webgl",kernelFunc:Gle};function Hle(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=See(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var qle={kernelName:nm,backendName:"webgl",kernelFunc:Hle};function Kle(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Cee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var Xle={kernelName:am,backendName:"webgl",kernelFunc:Kle},Zle="return tan(x);",Yle=it({opSnippet:Zle}),Jle={kernelName:pl,backendName:"webgl",kernelFunc:Yle},Qle=`
+    `}};function Vle(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=ve({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let S=_t.computeOutShape(A,x,b),E=xd({inputs:{x:a},backend:r,attrs:{begin:A,size:S}});v=ve({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeIntermediateTensorInfo(E)}else if(r.shouldExecuteOnCPU([a])){let S=r.readSync(a.dataId),E=We(a.shape,a.dtype,S),R=kee(c,E,b,A);v=r.makeTensorInfo(f,a.dtype,R.values)}else{let S=new Wle(A,b,c);v=r.runWebGLProgram(S,[a],a.dtype)}let C=ve({inputs:{x:v},backend:r,attrs:{shape:f}});return r.disposeIntermediateTensorInfo(v),C}var Ule={kernelName:dl,backendName:"webgl",kernelFunc:Vle};function Gle(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=Iee(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var jle={kernelName:Jp,backendName:"webgl",kernelFunc:Gle};function Hle(e){let{inputs:t,backend:r,attrs:n}=e,{skipEmpty:a}=n,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=r.readSync(s.dataId),l=r.readSync(i.dataId)[0],[u,d,h]=See(o,l,a),p=d.length;return[r.makeTensorInfo([p,2],"int32",u),r.makeTensorInfo([p],"string",d),r.makeTensorInfo([2],"int32",new Int32Array(h))]}var qle={kernelName:rm,backendName:"webgl",kernelFunc:Hle};function Kle(e){let{inputs:t,backend:r,attrs:n}=e,{numBuckets:a}=n,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");let i=r.readSync(s.dataId),o=Cee(i,a);return r.makeTensorInfo(s.shape,"int32",o)}var Xle={kernelName:nm,backendName:"webgl",kernelFunc:Kle},Zle="return tan(x);",Yle=it({opSnippet:Zle}),Jle={kernelName:pl,backendName:"webgl",kernelFunc:Yle},Qle=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
 `,eue=it({opSnippet:Qle}),tue={kernelName:Mi,backendName:"webgl",kernelFunc:eue},rue=class{constructor(e,t){this.variableNames=["A"];let r=new Array(e.length);for(let s=0;s<r.length;s++)r[s]=e[s]*t[s];this.outputShape=r,this.rank=r.length;let n=bt(this.rank),a=nue(e);this.userCode=`
@@ -4361,7 +4362,7 @@ return a / b;`,Eie=`
         ${n} resRC = getOutputCoords();
         setOutput(getA(${a}));
       }
-    `}};function nue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function QS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Nee(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new rue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var aue={kernelName:Ka,backendName:"webgl",kernelFunc:QS},sue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
+    `}};function nue(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let r=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],n=[];for(let a=0;a<e.length;a++)n.push(`imod(${r[a]}, ${e[a]})`);return n.join()}function Q8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reps:s}=n;if(a.dtype==="string"||a.shape.length>5){let o=r.readSync(a.dataId),l=a.dtype==="string"?o.map(h=>w.decodeString(h)):o,u=We(a.shape,a.dtype,l),d=Nee(u,s);return r.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new rue(a.shape,s);return r.runWebGLProgram(i,[a],a.dtype)}var aue={kernelName:Ka,backendName:"webgl",kernelFunc:Q8},sue=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
        void main() {
          ivec2 coords = getOutputCoords();
          int batch = coords[0];
@@ -4435,7 +4436,7 @@ return a / b;`,Eie=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function no(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Fv(e){let t=1;for(;t<e;)t*=2;return t}function oue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=J().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=J().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=Eee(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Mh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=w.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&no(r,c);let g=Fv(s),y=Fv(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),O=new sue(M),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(O,I,"int32",z),no(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new iue([f,R/2]),I=[[d],[A===null?1:0],[g]],O=A;A=r.runWebGLProgram(M,_,"int32",I),no(r,O);let z=g/2,j=z*2;for(let X=z;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=bd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),no(r,v);let C=GS({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});no(r,m);let S=u.slice(0,-1);S.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:S},backend:r}),no(r,v);let E=C;return C=ve({inputs:{x:C},attrs:{shape:S},backend:r}),no(r,E),[C,A]}var lue={kernelName:hl,backendName:"webgl",kernelFunc:oue},uue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function no(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Mv(e){let t=1;for(;t<e;)t*=2;return t}function oue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=J().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=J().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=a.shape,d=u[u.length-1];if(r.shouldExecuteOnCPU([a])||d<o||s>l){let R=r.readSync(a.dataId),[_,M]=Eee(R,u,a.dtype,s,i);return[r.makeTensorInfo(_.shape,_.dtype,_.values),r.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[r.makeTensorInfo(u,a.dtype,[]),r.makeTensorInfo(u,"int32",[])];if(d===1)return[a,Rh({attrs:{shape:u,dtype:"int32",value:0},backend:r})];let h=r.texData.get(a.dataId),p=h!==null&&h.isPacked,c=p?r.unpackTensor(a):a,f=w.sizeFromShape(u)/d,m=ve({inputs:{x:c},attrs:{shape:[f,d]},backend:r});p&&no(r,c);let g=Mv(s),y=Mv(d),A=null,x=()=>A===null?[m,m]:[m,A],b=(R,_,M)=>{let I=x(),O=new sue(M),z=[[d],[A===null?1:0],[Number.NEGATIVE_INFINITY],[R],[_]],j=A;A=r.runWebGLProgram(O,I,"int32",z),no(r,j)};for(let R=1;R<g;R*=2){let _=R*2;for(let M=R;M>=1;M/=2)b(_,M,[f,y])}for(let R=y;R>g;R/=2){let _=x(),M=new iue([f,R/2]),I=[[d],[A===null?1:0],[g]],O=A;A=r.runWebGLProgram(M,_,"int32",I),no(r,O);let z=g/2,j=z*2;for(let X=z;X>=1;X/=2)b(j,X,A.shape)}let v=A;A=xd({inputs:{x:A},backend:r,attrs:{begin:0,size:[f,s]}}),no(r,v);let C=G8({inputs:{x:m,indices:A},backend:r,attrs:{axis:1,batchDims:1}});no(r,m);let S=u.slice(0,-1);S.push(s),v=A,A=ve({inputs:{x:A},attrs:{shape:S},backend:r}),no(r,v);let E=C;return C=ve({inputs:{x:C},attrs:{shape:S},backend:r}),no(r,E),[C,A]}var lue={kernelName:hl,backendName:"webgl",kernelFunc:oue},uue=class{constructor(e,t,r,n,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=r==="nearest"?1:2,o;switch(n){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4547,7 +4548,7 @@ return a / b;`,Eie=`
               }
               setOutput(outputValue);
             }
-        `}};function due(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new uue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var pue={kernelName:cl,backendName:"webgl",kernelFunc:due};function hue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;fd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=Ree(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var cue={kernelName:sm,backendName:"webgl",kernelFunc:hue};function fue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=bd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var mue={kernelName:fl,backendName:"webgl",kernelFunc:fue},gue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
+        `}};function due(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new uue(h,p,i,o,l,g);return r.runWebGLProgram(y,[a,s],"float32")}var pue={kernelName:cl,backendName:"webgl",kernelFunc:due};function hue(e){let{inputs:t,attrs:r,backend:n}=e,{axis:a}=r,{x:s}=t;cd(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=n.readSync(s.dataId),{outputValues:o,outputShape:l,indices:u}=Ree(i,a,s.shape,s.dtype);return[n.makeTensorInfo(l,s.dtype,o),n.makeTensorInfo([u.length],"int32",u)]}var cue={kernelName:am,backendName:"webgl",kernelFunc:hue};function fue(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=xd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=ve({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeIntermediateTensorInfo(m)),f}var mue={kernelName:fl,backendName:"webgl",kernelFunc:fue},gue=class{constructor(e,t){this.variableNames=["x","segmentIds"];let r=e.windowSize,n=e.batchSize,a=e.inSize,s=e.numSegments,i=s*Math.ceil(a/r);this.outputShape=[n,i];let o="0.0",l="sumValue",u=Math.floor(r/4)*4,d=r%4,h=`
         sumValue += dot(values, segFilter);
     `,p="";a%r>0&&(p=`
         if (inIdx < 0 || inIdx >= ${a}) {
@@ -4653,10 +4654,10 @@ return a / b;`,Eie=`
         }
         setOutput(${l});
       }
-    `}};function yue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=nh(a.dtype),g=(b,v,C,S,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},O=new gue(I,v),z=r.compileAndRun(O,[b,C],S);if(l.push(z),z.shape[1]===E)return z;let j=YS({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=QS({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(z,v,X,S,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=Br({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Aue={kernelName:eh,backendName:"webgl",kernelFunc:yue},xue=[Ste,Tte,Rte,$te,_te,Dte,Bte,Vte,Hte,Kte,Yte,ere,nre,ore,dre,hre,fre,Are,bre,wre,Cre,$re,_re,Ore,Ure,jre,Xre,ite,Jre,nne,one,cne,mne,yne,xne,vne,Ine,Tne,Rne,Fne,Pne,One,Lne,Une,jne,Kne,Yne,Qne,nae,oae,pae,fae,yae,Aae,bae,wae,Iae,Cae,Nae,Fae,_ae,Dae,Bae,Uae,Hae,Zae,ese,ste,rse,tne,sse,lse,pse,lte,mse,xse,vse,Sse,Nse,Fse,_se,Lse,Use,Hse,Kse,Jse,eie,rie,iie,lie,die,hie,fie,Aie,wie,Cie,Pie,cte,Die,Wie,Gie,qie,Lre,Zie,Jie,eoe,noe,ooe,dte,uoe,doe,Bre,Rie,coe,yoe,voe,mte,Soe,Noe,Foe,_oe,Loe,Woe,Goe,qoe,Xoe,Joe,tle,sle,lle,ple,fle,Mre,Fie,yle,xle,vle,kle,Sle,Tle,Ele,Mle,$le,zle,Dle,Ble,Ule,jle,qle,Xle,Mie,wte,Jle,tue,aue,lue,pue,kte,cue,mue,Aue,Yie];for(let e of xue)Wn(e);var Pa=J();Pa.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Pa.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Pa.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Pa.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Pa.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Pa.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Pa.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Pa.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function bue(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function mr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function ef(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Qx(){return`
+    `}};function yue(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,segmentIds:s}=t,{numSegments:i}=n,o=a.shape.length,l=[],u=0,d=N.getAxesPermutation([u],o),h=a;d!=null&&(h=Br({inputs:{x:a},backend:r,attrs:{perm:d}}),l.push(h),u=N.getInnerMostAxes(1,o)[0]);let p=N.segment_util.computeOutShape(h.shape,u,i),c=w.sizeFromShape([h.shape[u]]),f=ve({inputs:{x:h},backend:r,attrs:{shape:[-1,c]}});l.push(f);let m=rh(a.dtype),g=(b,v,C,S,E)=>{let R=b.shape[0],_=b.shape[1],M=N.segment_util.segOpComputeOptimalWindowSize(_,E),I={windowSize:M,inSize:_,batchSize:R,numSegments:E},O=new gue(I,v),z=r.compileAndRun(O,[b,C],S);if(l.push(z),z.shape[1]===E)return z;let j=Y8({backend:r,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=Q8({inputs:{x:j},backend:r,attrs:{reps:[_/M]}});return l.push(j),l.push(X),g(z,v,X,S,E)},y=g(f,"unsortedSegmentSum",s,m,i),A=ve({inputs:{x:y},backend:r,attrs:{shape:p}}),x=A;if(d!=null){l.push(A);let b=N.getUndoAxesPermutation(d);x=Br({inputs:{x},backend:r,attrs:{perm:b}})}return l.forEach(b=>r.disposeIntermediateTensorInfo(b)),x}var Aue={kernelName:Qp,backendName:"webgl",kernelFunc:yue},xue=[Ste,Tte,Rte,$te,_te,Dte,Bte,Vte,Hte,Kte,Yte,ere,nre,ore,dre,hre,fre,Are,bre,wre,Cre,$re,_re,Ore,Ure,jre,Xre,ite,Jre,nne,one,cne,mne,yne,xne,vne,Ine,Tne,Rne,Fne,Pne,One,Lne,Une,jne,Kne,Yne,Qne,nae,oae,pae,fae,yae,Aae,bae,wae,Iae,Cae,Nae,Fae,_ae,Dae,Bae,Uae,Hae,Zae,ese,ste,rse,tne,sse,lse,pse,lte,mse,xse,vse,Sse,Nse,Fse,_se,Lse,Use,Hse,Kse,Jse,eie,rie,iie,lie,die,hie,fie,Aie,wie,Cie,Pie,cte,Die,Wie,Gie,qie,Lre,Zie,Jie,eoe,noe,ooe,dte,uoe,doe,Bre,Rie,coe,yoe,voe,mte,Soe,Noe,Foe,_oe,Loe,Woe,Goe,qoe,Xoe,Joe,tle,sle,lle,ple,fle,Mre,Fie,yle,xle,vle,kle,Sle,Tle,Ele,Mle,$le,zle,Dle,Ble,Ule,jle,qle,Xle,Mie,wte,Jle,tue,aue,lue,pue,kte,cue,mue,Aue,Yie];for(let e of xue)Wn(e);var Pa=J();Pa.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Pa.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Pa.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Pa.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Pa.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Pa.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Pa.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Pa.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Pa.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function bue(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let r=e.length,n=e.map(s=>`${t}[${s}]`),a=new Array(r-1);a[r-2]=n[r-1];for(let s=r-3;s>=0;--s)a[s]=`(${a[s+1]} * ${n[s+1]})`;return a}function mr(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function Qc(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Jx(){return`
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `}function Di(){return`
-  ${Qx()}
+  ${Jx()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -4693,7 +4694,7 @@ return a / b;`,Eie=`
     }
   `),n===!0)return a.push(`
       struct Matrix0 {
-        numbers: array<${ef(t.dtype,r.isVec4)}>;
+        numbers: array<${Qc(t.dtype,r.isVec4)}>;
       };
       struct Uniform {
         size            : i32;
@@ -4704,8 +4705,8 @@ return a / b;`,Eie=`
 
       @group(0) @binding(0) var<storage, write> result : Matrix0;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[$v,a.join(`
-`),Pv(t.shape),r.getUserCode()].join(`
+    `),[Fv,a.join(`
+`),$v(t.shape),r.getUserCode()].join(`
 `);let s="struct Uniforms { NAN : f32; ";r.variableNames.forEach((d,h)=>{s+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${mr(e[h].shape.length)}; `}),s+=`outShape : ${mr(t.shape.length)} ; `;let i=t.shape.length-1;s+=`
        outShapeStrides: ${mr(i)}; `,r.size&&(s+="size : i32; "),r.uniforms&&(s+=r.uniforms),s+="};",a.push(s),r.atomic?a.push(`
     struct Matrix0 {
@@ -4715,21 +4716,21 @@ return a / b;`,Eie=`
     @group(0) @binding(0) var<storage, read_write> result : Matrix0;
   `):a.push(`
     struct Matrix0 {
-        numbers: array<${ef(t.dtype,r.isVec4)}>;
+        numbers: array<${Qc(t.dtype,r.isVec4)}>;
     };
 
     @group(0) @binding(0) var<storage, write> result : Matrix0;
   `),r.variableNames.forEach((d,h)=>{a.push(`
     struct Matrix${1+h} {
-      numbers: array<${ef(e[h].dtype,r.isVec4)}>;
+      numbers: array<${Qc(e[h].dtype,r.isVec4)}>;
     };
     @group(0) @binding(${1+h}) var<storage, read> ${d} : Matrix${1+h};
     `)}),s!==""&&a.push(`
     @group(0) @binding(${1+r.variableNames.length}) var<uniform> uniforms : Uniforms;
-    `);let[o,l]=Tue(t.shape,r.dispatchLayout),u=[$v,a.join(`
-`),Pv(t.shape),o,wue(t.shape.length)];if(r.atomic||u.push(kue(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Iue(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
+    `);let[o,l]=Tue(t.shape,r.dispatchLayout),u=[Fv,a.join(`
+`),$v(t.shape),o,wue(t.shape.length)];if(r.atomic||u.push(kue(t.shape,t.dtype,r.isVec4)),l===t.shape.length){let d=e.map(h=>Iue(h,t.shape,r.isVec4,r.dispatchLayout.x.length===t.shape.length)).join(`
 `);u.push(d)}return u.push(r.getUserCode()),u.join(`
-`)}var $v=`
+`)}var Fv=`
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -4764,20 +4765,17 @@ return a / b;`,Eie=`
     return res;
   }
 
-  fn isNanCustom(val : f32) -> bool {
-    if (val > 0.0) {
-      return false;
-    }
-    if (val < 0.0) {
-      return false;
-    }
-    if (val == 0.0) {
-      return false;
-    }
-    return true;
+  // NaN defination in IEEE 754-1985 is :
+  //   - sign = either 0 or 1.
+  //   - biased exponent = all 1 bits.
+  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
+  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
+  fn isnan(val: f32) -> bool {
+    let floatToUint: u32 = bitcast<u32>(val);
+    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
   }
-  fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
-    return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
+  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
+    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
 `;function wue(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
@@ -4796,7 +4794,7 @@ return a / b;`,Eie=`
           return dot(coords, vec4<i32>(
             uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
         }
-        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function kue(e,t,r){let n=e.length,a=ef(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function kue(e,t,r){let n=e.length,a=Qc(t,r),s;if(r?s=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result.numbers[flatIndex] = ${a}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4905,7 +4903,7 @@ return a / b;`,Eie=`
     }
     `,s];let i="",o=[r,n,a],l=0;for(let p=0;p<o.length;p++){let c=o[p];if(c.length!==0)if(l+=c.length,c.length===1)i+=`let d${c[0]} = i32(globalId[${p}]);`;else{let f=bue(c,"uniforms.outShape");i+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)i+=`let d${c[m]} = index${p} / ${f[m]};`,m===f.length-1?i+=`let d${c[m+1]} = index${p} - d${c[m]} * ${f[m]};`:i+=`index${p} = index${p} - d${c[m]} * ${f[m]};`}}let u=[];for(let p=0;p<l;p++)u.push(`d${p}`);let d=mr(l),h=`fn getOutputCoords() -> ${d} {
     ${i}
-  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function Pv(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=mr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+  `;return u.length===0?h+=`return ${d}(0); }`:h+=`return ${d}(${u.join(",")}); }`,[h,l]}function $v(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let r=w.computeStrides(e),n=mr(t),a=[];for(let i=0;i<t;i++)a.push(`d${i}`);if(r.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
       return vec2<i32>(d0, d1);
     }`;let s="var index2 = index;"+r.map((i,o)=>{let l=`let ${a[o]} = index2 / uniforms.outShapeStrides[${o}]`,u=o===r.length-1?`let ${a[o+1]} = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${a[o]} * uniforms.outShapeStrides[${o}]`;return`${l}; ${u};`}).join("");return`
@@ -4913,11 +4911,11 @@ return a / b;`,Eie=`
       ${s}
       return ${n}(${a.join(",")});
     }
-  `}var e8={};Le(e8,{ArrayBufferToTypedArray:()=>r8,GPUBytesPerElement:()=>Oy,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>eb,computeWorkGroupSizeForMatMul:()=>t8,computeWorkPerThreadForConv2d:()=>tb,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>rb,tilesFitEvenlyIntoShape:()=>ja});var eu=65535,yo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function ja(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(yo(e.x.map(l=>t[l]))/(r[0]*n[0])),e.y?Math.ceil(yo(e.y.map(l=>t[l]))/(r[1]*n[1])):1,e.z?Math.ceil(yo(e.z.map(l=>t[l]))/(r[2]*n[2])):1];if(a<=eu&&s<=eu&&i<=eu)return[a,s,i];w.assert(a>eu&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let o=Math.ceil(Math.sqrt(a));return o>eu?(o=Math.ceil(Math.cbrt(a)),w.assert(o<=eu,()=>"Total dispatch size exceeds WebGPU maximum."),[o,o,o]):[o,o,1]}function eb(e,t){let r=yo(e.x.map(a=>t[a])),n=yo(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function t8(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function tb(e,t){let r=yo(e.x.map(a=>t[a])),n=yo(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,r)=>r)}}function Oy(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function r8(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function rb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Nue="return a + b;",Eue="return areal * breal - aimag * bimag;",Rue="return areal * bimag + aimag * breal;",Mue="return a / b;",Fue="return a * b;",$ue="return (a - b) * (a - b);",Pue="return a - b;",_ue="return f32(a == b);",zue="return vec4<f32>(a == b);",Oue="return f32(a > b);",Due="return vec4<f32>(a > b);",Lue="return f32(a >= b);",Bue="return vec4<f32>(a >= b);",Wue="return f32(a < b);",Vue="return vec4<f32>(a < b);",Uue="return f32(a <= b);",Gue="return vec4<f32>(a <= b);",jue="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Hue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  `}var eS={};Le(eS,{ArrayBufferToTypedArray:()=>rS,GPUBytesPerElement:()=>zy,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>Qx,computeWorkGroupSizeForMatMul:()=>tS,computeWorkPerThreadForConv2d:()=>eb,flatDispatchLayout:()=>Xe,isWebGPUSupported:()=>tb,tilesFitEvenlyIntoShape:()=>ja});var yo=e=>{let t=1;for(let r=0;r<e.length;r++)t*=e[r];return t};function ja(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((r,n)=>r%e[n]===0)}function Oe(e,t,r=[1,1,1],n=[1,1,1]){let[a,s,i]=[Math.ceil(yo(e.x.map(o=>t[o]))/(r[0]*n[0])),e.y?Math.ceil(yo(e.y.map(o=>t[o]))/(r[1]*n[1])):1,e.z?Math.ceil(yo(e.z.map(o=>t[o]))/(r[2]*n[2])):1];return[a,s,i]}function Qx(e,t){let r=yo(e.x.map(a=>t[a])),n=yo(e.y.map(a=>t[a]));return r<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function tS(e,t,r){return e===1?[32,1,1]:r===1?[1,32,1]:[8,8,1]}function eb(e,t){let r=yo(e.x.map(a=>t[a])),n=yo(e.y.map(a=>t[a]));return r<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function Xe(e){return{x:e.map((t,r)=>r)}}function zy(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function rS(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function tb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var Nue="return a + b;",Eue="return areal * breal - aimag * bimag;",Rue="return areal * bimag + aimag * breal;",Mue="return a / b;",Fue="return a * b;",$ue="return (a - b) * (a - b);",Pue="return a - b;",_ue="return f32(a == b);",zue="return vec4<f32>(a == b);",Oue="return f32(a > b);",Due="return vec4<f32>(a > b);",Lue="return f32(a >= b);",Bue="return vec4<f32>(a >= b);",Wue="return f32(a < b);",Vue="return vec4<f32>(a < b);",Uue="return f32(a <= b);",Gue="return vec4<f32>(a <= b);",jue="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Hue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`,que=`
-  if (isNanCustom(a)) { return a; }
-  if (isNanCustom(b)) { return b; }
-  `,n8=`
+  if (isnan(a)) { return a; }
+  if (isnan(b)) { return b; }
+  `,nS=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
   }
@@ -4988,19 +4986,19 @@ return a / b;`,Eie=`
     resultTemp.a = 1.0;
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
-  ${n8}
+  ${nS}
   return resultTemp;
   `,ede="if (a < 0.0) { return b * a; }  return a;",tde=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function _v(e,t){let r=t?n8:que;return t?`
+  `;function Pv(e,t){let r=t?nS:que;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
-    let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
+    let isNaN = isnanVec4(a) | isnanVec4(b);
     `+r+`
     return resultTemp;
   `:r+`
     return ${e}(a, b);
-  `}function Fh(e,t){switch(e){case 0:return Fue;case 1:return Nue;case 2:return Pue;case 3:return Mue;case 4:return t?zue:_ue;case 5:return t?Due:Oue;case 6:return t?Bue:Lue;case 7:return t?Vue:Wue;case 8:return t?Gue:Uue;case 9:return t?Hue:jue;case 10:return t?Yue:Zue;case 11:return $ue;case 12:return t?Xue:Kue;case 14:return t?tde:ede;case 15:return _v("max",t);case 16:return _v("min",t);case 13:return t?Que:Jue;case 17:return Eue;case 18:return Rue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var rde="return abs(a);",nde="return ceil(a);",ade="return cos(a);",sde=`
+  `}function Mh(e,t){switch(e){case 0:return Fue;case 1:return Nue;case 2:return Pue;case 3:return Mue;case 4:return t?zue:_ue;case 5:return t?Due:Oue;case 6:return t?Bue:Lue;case 7:return t?Vue:Wue;case 8:return t?Gue:Uue;case 9:return t?Hue:jue;case 10:return t?Yue:Zue;case 11:return $ue;case 12:return t?Xue:Kue;case 14:return t?tde:ede;case 15:return Pv("max",t);case 16:return Pv("min",t);case 13:return t?Que:Jue;case 17:return Eue;case 18:return Rue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var rde="return abs(a);",nde="return ceil(a);",ade="return cos(a);",sde=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
 `,ide="return exp(a) - 1.0;",ode="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",lde=`
@@ -5021,7 +5019,7 @@ return a / b;`,Eie=`
 `,ude="return exp(a);",dde="return floor(a);",pde="return a;",hde=`if (a < 0.0) { return 1.0/0.0; }
   return log(a);`,cde="return f32(!(a >= 1.0));",fde="return -a;",mde="return (a < 0.0) ? b * a : a;",gde="if (a < 0.0) { return uniforms.alpha * a; } return a;",yde="if(a < 0.0) { return 0.0; } return a;",Ade="return clamp(a, 0.0, 6.0);",xde="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",bde=`
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
-  let isNaN = isNanCustomVec4(a);
+  let isNaN = isnanVec4(a);
 
   if (isNaN.r) {
     resFloat.r = a.r;
@@ -5042,7 +5040,7 @@ return a / b;`,Eie=`
 `,Sde="return sqrt(a);",Cde="return a * a;",Tde=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,Nde="return f32(i32((a)));";function iu(e,t){switch(e){case 0:return rde;case 2:return ade;case 3:return sde;case 1:return nde;case 4:return t?lde:ode;case 5:return ude;case 6:return ide;case 7:return dde;case 8:return pde;case 9:return hde;case 10:return cde;case 11:return fde;case 12:return mde;case 15:return gde;case 13:return t?bde:yde;case 14:return t?xde:Ade;case 16:return vde;case 19:return wde;case 17:return kde;case 18:return Ide;case 20:return Sde;case 21:return Cde;case 22:return Tde;case 23:return Nde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function es(e,t=!1){if(e===null)return null;if(e==="linear")return iu(8);if(e==="relu")return iu(13,t);if(e==="elu")return iu(4,t);if(e==="relu6")return iu(14,t);if(e==="prelu")return Fh(14,t);if(e==="sigmoid")return iu(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function a8(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
+`,Nde="return f32(i32((a)));";function su(e,t){switch(e){case 0:return rde;case 2:return ade;case 3:return sde;case 1:return nde;case 4:return t?lde:ode;case 5:return ude;case 6:return ide;case 7:return dde;case 8:return pde;case 9:return hde;case 10:return cde;case 11:return fde;case 12:return mde;case 15:return gde;case 13:return t?bde:yde;case 14:return t?xde:Ade;case 16:return vde;case 19:return wde;case 17:return kde;case 18:return Ide;case 20:return Sde;case 21:return Cde;case 22:return Tde;case 23:return Nde;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function es(e,t=!1){if(e===null)return null;if(e==="linear")return su(8);if(e==="relu")return su(13,t);if(e==="elu")return su(4,t);if(e==="relu6")return su(14,t);if(e==="prelu")return Mh(14,t);if(e==="sigmoid")return su(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function aS(e,t,r,n){return w.assert(n%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
   var<workgroup> mm_Asub : array<array<vec4<f32>, ${n/e[0]}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<vec4<f32>, ${r/e[0]}>, ${n}>;
 
@@ -5146,8 +5144,8 @@ return a / b;`,Eie=`
           setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], value);
         }
       }
-      ${a8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}};function nb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
+      ${aS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
+    `}};function rb(e,t){let r=t[1]*e[1],n=t[0]*e[0],a=r>n?r:n;return`
     var<workgroup> mm_Asub : array<array<f32, ${a}>, ${r}>;
     var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${a}>;
     ${Di()}
@@ -5274,7 +5272,7 @@ return a / b;`,Eie=`
         mm_write(globalRow, globalCol, acc, globalId);
       }
     }
-  `}var s8=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=t8(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[ja(a,this.aShape.slice(1)),ja(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
+  `}var sS=class{constructor(e,t,r,n=!1,a=!1,s=null,i=null,o=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=n?e[1]:e[2];this.workGroupSize=tS(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(r=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]),w.arraysEqual(this.dispatch,[1,1,1])&&(r=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[r,r,1]));let u=s!=null,d=o!=null;u&&this.variableNames.push("bias"),d&&this.variableNames.push("preluActivationWeights"),this.workPerThread=r,this.aShape=e,this.transposeA=n,this.transposeB=a,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=d;let h=this.outputShape[2],p=this.transposeB?[this.outputShape[0],h,l]:[this.outputShape[0],l,h];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${n}_${a}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.workPerThread,n=t>r?t:r;this.outputShape[1]===1&&(n*=4),w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let a=[t,n],s=[n,r];return[ja(a,this.aShape.slice(1)),ja(s,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
              return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
            }
            return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
@@ -5316,7 +5314,7 @@ return a / b;`,Eie=`
         ${n}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1]>1?nb([this.workPerThread,this.workPerThread,1],this.workGroupSize):Rde(this.workGroupSize)}
+      ${this.outputShape[1]>1?rb([this.workPerThread,this.workPerThread,1],this.workGroupSize):Rde(this.workGroupSize)}
     `}};function Mde(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
     ${Di()}
@@ -5498,10 +5496,10 @@ return a / b;`,Eie=`
         }
       }
       ${$de(this.workGroupSize)}
-    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var _de={kernelName:tl,backendName:"webgpu",kernelFunc:qe};function ab({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],C=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),S=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[C,S],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new Fde([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new Pde(b,v,[R,c,f],s,l,i):_?M=new Ede(b,[R,c,f],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new s8(b,[R,c,f],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[C,S];s&&I.push(s),i&&I.push(i);let O=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}],z=a.runWebGPUProgram(M,I,e.dtype,O),j=qe({inputs:{x:z},backend:a,attrs:{shape:x}});E.push(z);for(let X of E)a.disposeData(X.dataId);return j}function zde(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return ab({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ode={kernelName:Ts,backendName:"webgpu",kernelFunc:zde},zv=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+    `}};function qe(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s),o=w.sizeFromShape(i);return w.assert(s===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${n.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var _de={kernelName:tl,backendName:"webgpu",kernelFunc:qe};function nb({a:e,b:t,transposeA:r,transposeB:n,backend:a,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:l=null}){let u=e.shape.length,d=t.shape.length,h=r?e.shape[u-2]:e.shape[u-1],p=n?t.shape[d-1]:t.shape[d-2],c=r?e.shape[u-1]:e.shape[u-2],f=n?t.shape[d-2]:t.shape[d-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=w.sizeFromShape(m),A=w.sizeFromShape(g),x=yl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([c,f]);w.assert(h===p,()=>`Error in matMul: inner shapes (${h}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${r} and transposeB=${n} must match.`);let b=r?[y,h,c]:[y,c,h],v=n?[A,f,p]:[A,p,f],C=qe({inputs:{x:e},backend:a,attrs:{shape:b}}),S=qe({inputs:{x:t},backend:a,attrs:{shape:v}}),E=[C,S],R=Math.max(y,A),_=h%4===0&&f%4===0&&!r&&!n&&f>=32,M;c*f<=32?M=new Fde([R,c,f],r,n,s,l,i):!r&&!n&&(c<=16&&(f<=512||p>=2*f)||f<=16&&(c<=512||h>=2*c))?M=new Pde(b,v,[R,c,f],s,l,i):_?M=new Ede(b,[R,c,f],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),s,l,i):M=new sS(b,[R,c,f],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),r,n,s,l,i);let I=[C,S];s&&I.push(s),i&&I.push(i);let O=[{type:"int32",data:[c]},{type:"int32",data:[f]},{type:"int32",data:[h]}],z=a.runWebGPUProgram(M,I,e.dtype,O),j=qe({inputs:{x:z},backend:a,attrs:{shape:x}});E.push(z);for(let X of E)a.disposeData(X.dataId);return j}function zde(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n;return nb({a,b:s,transposeA:l,transposeB:u,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:h,activation:d})}var Ode={kernelName:Ts,backendName:"webgpu",kernelFunc:zde},_v=class{constructor(e,t,r){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
-        ${Fh(this.op,!1)}
+        ${Mh(this.op,!1)}
       }
 
       ${tt()}
@@ -5517,7 +5515,7 @@ return a / b;`,Eie=`
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
-          ${Fh(this.op,!1)}
+          ${Mh(this.op,!1)}
         }
         var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
         ${tt()}
@@ -5542,7 +5540,7 @@ return a / b;`,Eie=`
         }
         `}},Lde=class{constructor(e,t,r){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
-        ${Fh(this.op,this.isVec4)}
+        ${Mh(this.op,this.isVec4)}
       }
       ${tt()}
         if (index < uniforms.size) {
@@ -5551,9 +5549,9 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-    `}},i8=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
+    `}},iS=class{constructor(e,t,r){this.variableNames=["A","B"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,r),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
-        ${Fh(this.op,!1)}
+        ${Mh(this.op,!1)}
       }
       ${tt()}
         if (index < uniforms.size) {
@@ -5562,9 +5560,9 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function Ov(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new Lde(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new Dde(e,t,r,a):new i8(e,t,r)}function Ln(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Bde={kernelName:ii,backendName:"webgpu",kernelFunc:Ln};function vd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Ln({inputs:{x:n},backend:r}),l=Ln({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Wde={kernelName:Lp,backendName:"webgpu",kernelFunc:vd},$h=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function zv(e,t,r){if(w.arraysEqual(t,r)&&w.sizeFromShape(t)%4===0)return new Lde(e,t,r);let n=t.length===1&&r.length>1&&t[0]<1024,a=r.length===1&&t.length>1&&r[0]<1024;return n||a?new Dde(e,t,r,a):new iS(e,t,r)}function Ln(e){let{inputs:t}=e,{x:r}=t;return e.backend.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}var Bde={kernelName:ii,backendName:"webgpu",kernelFunc:Ln};function bd(e){let{inputs:t,backend:r}=e,{real:n,imag:a}=t,s=r.makeTensorInfo(n.shape,"complex64"),i=r.tensorMap.get(s.dataId),o=Ln({inputs:{x:n},backend:r}),l=Ln({inputs:{x:a},backend:r});return i.complexTensorInfos={real:o,imag:l},s}var Wde={kernelName:Dp,backendName:"webgpu",kernelFunc:bd},Fh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${iu(this.op,!1)}
+        ${su(this.op,!1)}
       }
       ${tt()}
         if (index < uniforms.size) {
@@ -5572,7 +5570,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function xr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new $h(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Gr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=Ov(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Dr(y.dtype,A.dtype))});else{let g=new zv(17,i.shape,o.shape),y=new zv(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=vd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Dr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=Ov(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:Vde,ceilImpl:Ude,concatImpl:Gde,equalImpl:jde,expImpl:Hde,expm1Impl:qde,floorImpl:Kde,gatherNdImpl:Xde,gatherV2Impl:Zde,greaterEqualImpl:Yde,greaterImpl:Jde,lessEqualImpl:Qde,lessImpl:epe,logImpl:tpe,maxImpl:rpe,maximumImpl:npe,minimumImpl:ape,multiplyImpl:spe,negImpl:ipe,notEqualImpl:ope,prodImpl:lpe,rangeImpl:upe,rsqrtImpl:dpe,simpleAbsImpl:ppe,sliceImpl:hpe,stridedSliceImpl:cpe,stringNGramsImpl:fpe,subImpl:mpe,tileImpl:gpe,topKImpl:ype,transposeImpl:Ape,uniqueImpl:bAe}=n0,xpe=xr({opType:0,cpuKernelImpl:ppe}),bpe={kernelName:Mo,backendName:"webgpu",kernelFunc:xpe},vpe=Gr({opSnippet:1,cpuKernelImpl:Vde,supportsComplex:!0}),wpe={kernelName:Ha,backendName:"webgpu",kernelFunc:vpe},kpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
+      `}};function xr({opType:e,cpuKernelImpl:t,dtype:r}){return({inputs:n,backend:a})=>{let{x:s}=n,i=a,o=r||s.dtype;if(i.shouldExecuteOnCPU([s])&&t!=null){let u=i.tensorMap.get(s.dataId),d=t(u.values,o);return i.makeTensorInfo(s.shape,o,d)}let l=new Fh(s.shape,e);return i.runWebGPUProgram(l,[s],o)}}function Gr({opSnippet:e,cpuKernelImpl:t,supportsComplex:r=!1,dtype:n}){return({inputs:a,backend:s})=>{let{a:i,b:o}=a,l=s;if(r&&i.dtype==="complex64"){let h=l.tensorMap.get(i.dataId),p=l.tensorMap.get(o.dataId),c,f;if(e!==0)[c,f]=[[h.complexTensorInfos.real,p.complexTensorInfos.real],[h.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,A]=g,x={dataId:y.dataId,dtype:y.dtype,shape:i.shape},b={dataId:A.dataId,dtype:A.dtype,shape:o.shape},v=zv(e,i.shape,o.shape);return l.runWebGPUProgram(v,[x,b],Dr(y.dtype,A.dtype))});else{let g=new _v(17,i.shape,o.shape),y=new _v(18,i.shape,o.shape),A=[{dataId:h.complexTensorInfos.real.dataId,dtype:h.complexTensorInfos.real.dtype,shape:i.shape},{dataId:h.complexTensorInfos.imag.dataId,dtype:h.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape}];c=l.runWebGPUProgram(g,A,"float32"),f=l.runWebGPUProgram(y,A,"float32")}let m=bd({inputs:{real:c,imag:f},backend:l});return l.disposeData(c.dataId),l.disposeData(f.dataId),m}let u=n||Dr(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||l.shouldExecuteOnCPU([i,o]))&&t!=null){let h=l.tensorMap.get(i.dataId).values,p=l.tensorMap.get(o.dataId).values,c=i.dtype==="string"?N.fromUint8ToStringArray(h):h,f=i.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(i.shape,o.shape,c,f,u);return l.makeTensorInfo(g,u,m)}let d=zv(e,i.shape,o.shape);return l.runWebGPUProgram(d,[i,o],u)}}var{addImpl:Vde,ceilImpl:Ude,concatImpl:Gde,equalImpl:jde,expImpl:Hde,expm1Impl:qde,floorImpl:Kde,gatherNdImpl:Xde,gatherV2Impl:Zde,greaterEqualImpl:Yde,greaterImpl:Jde,lessEqualImpl:Qde,lessImpl:epe,logImpl:tpe,maxImpl:rpe,maximumImpl:npe,minimumImpl:ape,multiplyImpl:spe,negImpl:ipe,notEqualImpl:ope,prodImpl:lpe,rangeImpl:upe,rsqrtImpl:dpe,simpleAbsImpl:ppe,sliceImpl:hpe,stridedSliceImpl:cpe,stringNGramsImpl:fpe,subImpl:mpe,tileImpl:gpe,topKImpl:ype,transposeImpl:Ape,uniqueImpl:bAe}=r0,xpe=xr({opType:0,cpuKernelImpl:ppe}),bpe={kernelName:Mo,backendName:"webgpu",kernelFunc:xpe},vpe=Gr({opSnippet:1,cpuKernelImpl:Vde,supportsComplex:!0}),wpe={kernelName:Ha,backendName:"webgpu",kernelFunc:vpe},kpe=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,r)=>`T${r}`),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(r=>{e.push(`let v${r} = get${r}ByOutputCoords(coords);`)});let t=this.variableNames.map(r=>`v${r}`).join(" + ");return`
       ${tt()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5584,7 +5582,7 @@ return a / b;`,Eie=`
           }
         }
       }
-    `}};function Ipe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Ln({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Dr(o,l)),s=n.map(o=>o.shape),i=new kpe(s);return r.runWebGPUProgram(i,n,a)}var Spe={kernelName:Vs,backendName:"webgpu",kernelFunc:Ipe},o8=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
+    `}};function Ipe(e){let{inputs:t,backend:r}=e,n=t;if(n.length===1)return Ln({inputs:{x:n[0]},backend:r});let a=n.map(o=>o.dtype).reduce((o,l)=>Dr(o,l)),s=n.map(o=>o.shape),i=new kpe(s);return r.runWebGPUProgram(i,n,a)}var Spe={kernelName:Vs,backendName:"webgpu",kernelFunc:Ipe},oS=class{constructor(e,t,r){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let n=[t];N.assertAxesAreInnerMostDims("arg"+r.charAt(0).toUpperCase()+r.slice(1),n,e.length),this.op=r==="min"?"<":">";let[a]=N.computeOutAndReduceShapes(e,n);this.outputShape=a.length===0?[1]:a,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
       var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
     `,t=(n,a)=>this.outputShape.length===1?n:`${n}[${a}]`,r=n=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${n}]`;return`
@@ -5635,7 +5633,7 @@ return a / b;`,Eie=`
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
-          if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {
+          if (!isnan(candidate) && candidate ${this.op} bestValue) {
             bestValue = candidate;
             bestIndex = k;
           }
@@ -5667,7 +5665,7 @@ return a / b;`,Eie=`
     `}},Cpe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];this.outputShape=r,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${Qx()}
+      ${Jx()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5699,7 +5697,7 @@ return a / b;`,Eie=`
           }
         }
       }
-    `}};function Npe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Nl(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Ape(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new Cpe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new Tpe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var Epe={kernelName:Fi,backendName:"webgpu",kernelFunc:Nl};function Rpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Nl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new o8(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Mpe={kernelName:Us,backendName:"webgpu",kernelFunc:Rpe};function Fpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Nl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new o8(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var $pe={kernelName:Fu,backendName:"webgpu",kernelFunc:Fpe},l8=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function Npe(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let r=new Array(t);for(let n=0;n<e.length;n++)r[e[n]]=`resRC[${n}]`;return r.join()}function Nl(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{perm:s}=n,i=r,o=a.shape.length,l=new Array(o);for(let d=0;d<l.length;d++)l[d]=a.shape[s[d]];if(r.shouldExecuteOnCPU([a])){let d=i.tensorMap.get(a.dataId).values,h=Ape(d,a.shape,a.dtype,s,l);return r.makeTensorInfo(l,a.dtype,h)}if(a.shape.length===2&&w.arraysEqual(s,[1,0])){let d=new Cpe(a.shape,s);return i.runWebGPUProgram(d,[a],a.dtype)}let u=new Tpe(a.shape,s);return i.runWebGPUProgram(u,[a],a.dtype)}var Epe={kernelName:Fi,backendName:"webgpu",kernelFunc:Nl};function Rpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Nl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],l.shape.length);let d=new oS(l.shape,i[0],"max"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var Mpe={kernelName:Us,backendName:"webgpu",kernelFunc:Rpe};function Fpe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s}=n,i=w.parseAxisParam(s,a.shape),o=N.getAxesPermutation(i,a.shape.length),l=a,u=[];o!=null&&(l=Nl({inputs:{x:a},backend:r,attrs:{perm:o}}),u.push(l),i=N.getInnerMostAxes(i.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],l.shape.length);let d=new oS(l.shape,i[0],"min"),h=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=r.runWebGPUProgram(d,[l],"int32",h);return u.forEach(c=>r.disposeData(c.dataId)),p}var $pe={kernelName:Mu,backendName:"webgpu",kernelFunc:Fpe},lS=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${tt()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5732,7 +5730,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},u8=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+    `}},uS=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -5747,7 +5745,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function Ppe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Ln({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new u8(d):(h=new l8(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var _pe={kernelName:Gs,backendName:"webgpu",kernelFunc:Ppe};function zpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return ab({a,b:s,transposeA:i,transposeB:o,backend:r})}var Ope={kernelName:js,backendName:"webgpu",kernelFunc:zpe},Dpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${mr(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=mr(this.rank),t=Lpe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${Dy[a]} = uniforms.start[${a}] + coords.${Dy[a]};`),`
+    `}};function Ppe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l);if(d.filterWidth===1&&d.filterHeight===1&&w.arraysEqual(d.inShape,d.outShape))return Ln({inputs:{x:a},backend:r});let h,p=[{type:"int32",data:[d.strideHeight,d.strideWidth]}];return d.filterHeight===1&&d.filterWidth===1?h=new uS(d):(h=new lS(d,"avg"),p.push({type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]})),r.runWebGPUProgram(h,[a],a.dtype,p)}var _pe={kernelName:Gs,backendName:"webgpu",kernelFunc:Ppe};function zpe(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;return nb({a,b:s,transposeA:i,transposeB:o,backend:r})}var Ope={kernelName:js,backendName:"webgpu",kernelFunc:zpe},Dpe=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${mr(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=mr(this.rank),t=Lpe(this.rank),r;return this.start.length===1?r=this.outputShape.map((n,a)=>"sourceLoc = uniforms.start + coords;"):r=this.outputShape.map((n,a)=>`sourceLoc.${Oy[a]} = uniforms.start[${a}] + coords.${Oy[a]};`),`
       ${tt()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5757,13 +5755,13 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},Dy=["x","y","z","w","u","v"];function Lpe(e){if(e===1)return"sourceLoc";if(e<=6)return Dy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function wd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=hpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Dpe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var Bpe={kernelName:il,backendName:"webgpu",kernelFunc:wd},Wpe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=wd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},Vpe={kernelName:Fo,backendName:"webgpu",kernelFunc:Wpe},d8=Gr({opSnippet:10,dtype:"bool",cpuKernelImpl:ope}),Upe={kernelName:Ko,backendName:"webgpu",kernelFunc:d8};function Ph(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Ln({inputs:{x:a.complexTensorInfos.real},backend:r})}var Gpe={kernelName:Kp,backendName:"webgpu",kernelFunc:Ph};function jpe(e,t){let r=new $h(e.shape,23),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Ly(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Ln({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Ly({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=vd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=Ph({inputs:{input:a},backend:r}),o=Ly({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=Ln({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return jpe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=d8({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Hpe={kernelName:Hs,backendName:"webgpu",kernelFunc:Ly},qpe=xr({opType:1,cpuKernelImpl:Ude}),Kpe={kernelName:qs,backendName:"webgpu",kernelFunc:qpe},Xpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},Oy=["x","y","z","w","u","v"];function Lpe(e){if(e===1)return"sourceLoc";if(e<=6)return Oy.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function vd(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,size:i}=n,[o,l]=_t.parseSliceParams(a,s,i);if(_t.assertParamsValid(a,o,l),r.shouldExecuteOnCPU([a])||a.dtype==="string"){let h=r.tensorMap.get(a.dataId),p=hpe(h.values,o,l,a.shape,a.dtype);return r.makeTensorInfo(l,a.dtype,p)}if(w.sizeFromShape(l)===0)return r.makeTensorInfo(l,a.dtype,[]);let u=new Dpe(o,l),d=[{type:"int32",data:o}];return r.runWebGPUProgram(u,[a],a.dtype,d)}var Bpe={kernelName:il,backendName:"webgpu",kernelFunc:vd},Wpe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n;w.assert(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((A,x)=>A*x),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=[],f=qe({inputs:{x:a},backend:r,attrs:{shape:l}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:u}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:d}}),y=vd({inputs:{x:g},backend:r,attrs:{begin:h,size:p}});return c.push(f),c.push(m),c.push(g),c.forEach(A=>r.disposeData(A.dataId)),y},Vpe={kernelName:Fo,backendName:"webgpu",kernelFunc:Wpe},dS=Gr({opSnippet:10,dtype:"bool",cpuKernelImpl:ope}),Upe={kernelName:Ko,backendName:"webgpu",kernelFunc:dS};function $h(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Ln({inputs:{x:a.complexTensorInfos.real},backend:r})}var Gpe={kernelName:qp,backendName:"webgpu",kernelFunc:$h};function jpe(e,t){let r=new Fh(e.shape,23),n=t.runWebGPUProgram(r,[e],"int32");return{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}function Dy(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dtype:s}=n;if(s==="complex64"){if(a.dtype==="complex64")return Ln({inputs:{x:a},backend:r});let i=Wt(a.shape),o=Dy({inputs:{x:a},backend:r,attrs:{dtype:"float32"}}),l=bd({inputs:{real:o,imag:i},backend:r});return i.dispose(),r.disposeData(o.dataId),l}if(a.dtype==="complex64"){let i=$h({inputs:{input:a},backend:r}),o=Dy({inputs:{x:i},backend:r,attrs:{dtype:s}});return r.disposeData(i.dataId),o}if(!w.hasEncodingLoss(a.dtype,s)){let i=Ln({inputs:{x:a},backend:r});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return jpe(a,r);if(s==="bool"){let i=r.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),o=dS({inputs:{a,b:i},backend:r});return r.disposeData(i.dataId),o}throw new Error(`Error in Cast: failed to cast ${a.dtype} to ${s}`)}var Hpe={kernelName:Hs,backendName:"webgpu",kernelFunc:Dy},qpe=xr({opType:1,cpuKernelImpl:Ude}),Kpe={kernelName:qs,backendName:"webgpu",kernelFunc:qpe},Xpe=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${tt()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
           var clampedValue : vec4<f32>;
           for (var i = 0; i < 4; i = i + 1) {
-            if (isNanCustom(value[i])) {
+            if (isnan(value[i])) {
               clampedValue[i] = value[i];
             } else {
               clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
@@ -5777,7 +5775,7 @@ return a / b;`,Eie=`
       ${tt()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
-          if (isNanCustom(value)) {
+          if (isnan(value)) {
             setOutputAtIndex(index, value);
             return;
           }
@@ -5798,7 +5796,7 @@ return a / b;`,Eie=`
           }
         }
       }
-    `}};function h0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Ln({inputs:{x:a.complexTensorInfos.imag},backend:r})}var ehe={kernelName:Gp,backendName:"webgpu",kernelFunc:h0};function By(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>Ph({inputs:{input:A},backend:r})),f=e.map(A=>h0({inputs:{input:A},backend:r})),m=By(c,t,r),g=By(f,t,r),y=vd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Gde(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=the(e,t,r),o=s.map(c=>c.shape),l=new Qpe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function the(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function p8(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return Ln({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),By(o,s,r)}var rhe={kernelName:$o,backendName:"webgpu",kernelFunc:p8},nhe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
+    `}};function p0(e){let{inputs:t,backend:r}=e,{input:n}=t,a=r.tensorMap.get(n.dataId);return Ln({inputs:{x:a.complexTensorInfos.imag},backend:r})}var ehe={kernelName:Up,backendName:"webgpu",kernelFunc:p0};function Ly(e,t,r){let n=e[0].dtype;if(n==="complex64"){let c=e.map(A=>$h({inputs:{input:A},backend:r})),f=e.map(A=>p0({inputs:{input:A},backend:r})),m=Ly(c,t,r),g=Ly(f,t,r),y=bd({inputs:{real:m,imag:g},backend:r});return c.forEach(A=>r.disposeData(A.dataId)),f.forEach(A=>r.disposeData(A.dataId)),r.disposeData(m.dataId),r.disposeData(g.dataId),y}let a=r.shouldExecuteOnCPU(e);if(n==="string"&&(a=!0),a){let c=e.map(b=>{let v=w.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:r,attrs:{shape:[-1,v]}})}),f=c.map(b=>({vals:r.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(c.map(b=>b.shape),1),g=c[0].shape[0]===1,y=Gde(f,m,n,g),A=N.computeOutShape(e.map(b=>b.shape),t),x=r.makeTensorInfo(A,n,y);return c.forEach(b=>r.disposeData(b.dataId)),x}let{tensors2D:s,outShape:i}=the(e,t,r),o=s.map(c=>c.shape),l=new Qpe(o),u=[],d=new Array(o.length-1);if(d.length>0){d[0]=o[0][1],u.push({type:"int32",data:[d[0]]});for(let c=1;c<d.length;c++)d[c]=d[c-1]+o[c][1],u.push({type:"int32",data:[d[c]]})}let h=r.runWebGPUProgram(l,s,s[0].dtype,u);s.forEach(c=>r.disposeData(c.dataId));let p=qe({inputs:{x:h},backend:r,attrs:{shape:i}});return r.disposeData(h.dataId),p}function the(e,t,r){let n=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:r,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:n}}function pS(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n,s=w.parseAxisParam(a,t[0].shape)[0],i=N.computeOutShape(t.map(u=>u.shape),s);if(w.sizeFromShape(i)===0)return r.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(u=>w.sizeFromShape(u.shape)>0);if(o.length===1)return Ln({inputs:{x:o[0]},backend:r});let l=o.map(u=>u.shape);return N.assertParamsConsistent(l,s),Ly(o,s,r)}var rhe={kernelName:$o,backendName:"webgpu",kernelFunc:pS},nhe=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
       inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
     ${tt()}
 
@@ -5848,7 +5846,7 @@ return a / b;`,Eie=`
         temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
       }
     }
-    `}getUserCode(){let e=a8(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
+    `}getUserCode(){let e=aS(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner),t=`let outRow = r / uniforms.outShape[2];
         let outCol = r % uniforms.outShape[2];
         let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
         let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
@@ -5927,7 +5925,7 @@ return a / b;`,Eie=`
           }
         }
         ${e}
-      `}},she=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=eb(this.dispatchLayout,this.outputShape),this.elementsPerThread=tb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ja(n,[s,o]),ja(a,[o,i])]}getUserCode(){let e=nb(this.elementsPerThread,this.workGroupSize),t=`
+      `}},she=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Qx(this.dispatchLayout,this.outputShape),this.elementsPerThread=eb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivationWeights=n,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],r=e>t?e:t;w.assert(r%this.workGroupSize[0]===0&&r%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let n=[e,r],a=[r,t],s=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ja(n,[s,o]),ja(a,[o,i])]}getUserCode(){let e=rb(this.elementsPerThread,this.workGroupSize),t=`
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -6037,7 +6035,7 @@ return a / b;`,Eie=`
 
         writeResult(batch, coords[1], coords[2], outChannel, acc);
       }
-    `}};function ohe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=ab({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function lhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=m*f,C=[v,b],S=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new nhe(C,x),O=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],z=n.runWebGPUProgram(I,[_],_.dtype,O),j=qe({inputs:{x:z},backend:n,attrs:{shape:[1,C[0],C[1]]}});R.push(z),R.push(j);let X=[1,C[0],C[1]],D=new s8(X,[1,v,r.outChannels],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,Y=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s);let Z=n.runWebGPUProgram(D,se,j.dtype,Y),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function h8({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return ohe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(J().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return lhe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=J().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&r.outChannels>=32,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new ihe(r,l,o,u);else{p?d=new ahe(r,l,o,u):d=new she(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),n.runWebGPUProgram(d,m,e.dtype,f)}function uhe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return h8({x:a,filter:s,convInfo:p,backend:n})}var dhe={kernelName:Ks,backendName:"webgpu",kernelFunc:uhe},phe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=eb(this.dispatchLayout,this.outputShape),this.elementsPerThread=tb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+    `}};function ohe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=e.shape,u=r.dataFormat==="channelsLast",d=!1,h=!1,p=r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID",c,f;if(p){let y=r.inHeight*r.inWidth*r.inChannels;c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,r.batchSize,y]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,y,r.outChannels]}})}else{let y=u?l[0]*l[1]*l[2]:l[0]*l[2]*l[3];c=qe({inputs:{x:e},backend:n,attrs:{shape:[1,y,r.inChannels]}}),f=qe({inputs:{x:t},backend:n,attrs:{shape:[1,r.inChannels,r.outChannels]}})}let m=nb({a:c,b:f,transposeA:d,transposeB:h,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),g=qe({inputs:{x:m},backend:n,attrs:{shape:r.outShape}});return n.disposeData(c.dataId),n.disposeData(f.dataId),n.disposeData(m.dataId),g}function lhe({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:l,filterHeight:u,inChannels:d,strideWidth:h,strideHeight:p,padInfo:c,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:A}=r,x=A==="channelsLast",b=l*u*d,v=m*f,C=[v,b],S=!1,E=!1,R=[],_=qe({inputs:{x:e},backend:n,attrs:{shape:e.shape.slice(1)}}),M=qe({inputs:{x:t},backend:n,attrs:{shape:[1,b,-1]}});R.push(_),R.push(M);let I=new nhe(C,x),O=[{type:"int32",data:[c.left,c.top]},{type:"int32",data:[h,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[d*l]},{type:"int32",data:[d]}],z=n.runWebGPUProgram(I,[_],_.dtype,O),j=qe({inputs:{x:z},backend:n,attrs:{shape:[1,C[0],C[1]]}});R.push(z),R.push(j);let X=[1,C[0],C[1]],D=new sS(X,[1,v,r.outChannels],J().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E,a,o,s),Q=X[1],V=X[2],ee=r.outChannels,Y=[{type:"int32",data:[Q]},{type:"int32",data:[ee]},{type:"int32",data:[V]}],se=[j,M];a&&se.push(a),s&&se.push(s);let Z=n.runWebGPUProgram(D,se,j.dtype,Y),ae=x?[1,m,f,r.outChannels]:[1,r.outChannels,m,f],de=qe({inputs:{x:Z},backend:n,attrs:{shape:ae}});R.push(Z);for(let Ae of R)n.disposeData(Ae.dataId);return de}function hS({x:e,filter:t,convInfo:r,backend:n,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let l=a!=null,u=s!=null,d;if(r.filterHeight===r.inHeight&&r.filterWidth===r.inWidth&&r.padInfo.type==="VALID"||r.filterHeight===1&&r.filterWidth===1&&r.dilationHeight===1&&r.dilationWidth===1&&r.strideHeight===1&&r.strideWidth===1&&(r.padInfo.type==="SAME"||r.padInfo.type==="VALID"))return ohe({x:e,filter:t,convInfo:r,backend:n,bias:a,activation:o,preluActivationWeights:s,leakyreluAlpha:i});if(J().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return lhe({x:e,filter:t,convInfo:r,backend:n,bias:a,preluActivationWeights:s,leakyreluAlpha:i,activation:o});let h=J().getBool("WEBGPU_USE_NAIVE_CONV2D"),p=(r.inChannels%4===0||r.inChannels===3&&r.padInfo.type==="VALID")&&r.outChannels%4===0&&r.outChannels>=32,c=[r.padInfo.top,r.padInfo.left],f=[{type:"int32",data:[r.filterHeight,r.filterWidth]},{type:"int32",data:[...c]},{type:"int32",data:[r.strideHeight,r.strideWidth]},{type:"int32",data:[r.dilationHeight,r.dilationWidth]}];if(h)d=new ihe(r,l,o,u);else{p?d=new ahe(r,l,o,u):d=new she(r,l,o,u);let g=r.outShape[1]*r.outShape[2],y=r.outShape[3],A=r.filterHeight*r.filterWidth*r.inShape[3];f.push({type:"int32",data:[g]},{type:"int32",data:[y]},{type:"int32",data:[A]})}let m=[e,t];return l&&m.push(a),u&&m.push(s),n.runWebGPUProgram(d,m,e.dtype,f)}function uhe(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dataFormat:l,dilations:u,dimRoundingMode:d}=r,h=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(a.shape,s.shape,i,u,o,d,!1,h);return hS({x:a,filter:s,convInfo:p,backend:n})}var dhe={kernelName:Ks,backendName:"webgpu",kernelFunc:uhe},phe=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Qx(this.dispatchLayout,this.outputShape),this.elementsPerThread=eb(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6090,7 +6088,7 @@ return a / b;`,Eie=`
       result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${nb(this.elementsPerThread,this.workGroupSize)}
+    ${rb(this.elementsPerThread,this.workGroupSize)}
   `}},hhe=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,r=this.isChannelsLast?3:1;return`
     ${tt()} {
       if(index < uniforms.size) {
@@ -6216,7 +6214,7 @@ return a / b;`,Eie=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function khe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new whe(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var Ihe={kernelName:zo,backendName:"webgpu",kernelFunc:khe},c8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=es(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function khe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockSize:s,dataFormat:i}=n,o=a.shape[0],l=i==="NHWC"?a.shape[1]:a.shape[2],u=i==="NHWC"?a.shape[2]:a.shape[3],d=i==="NHWC"?a.shape[3]:a.shape[1],h=l*s,p=u*s,c=d/(s*s),f=i==="NHWC"?[o,h,p,c]:[o,c,h,p],m=[{type:"int32",data:[s]}],g=new whe(f,i);return r.runWebGPUProgram(g,[a],a.dtype,m)}var Ihe={kernelName:zo,backendName:"webgpu",kernelFunc:khe},cS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise3x3_${r}`}getUserCode(){let e="",t="";if(this.activation){let n=es(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${n}
         }`:e=`
@@ -6226,7 +6224,7 @@ return a / b;`,Eie=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let r=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${Qx()}
+      ${Jx()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6288,7 +6286,7 @@ return a / b;`,Eie=`
           }
         }
       }
-    `}},f8=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
+    `}},fS=class{constructor(e,t=!1,r=null,n=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
       inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
       channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=r,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let n=es(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
@@ -6370,10 +6368,10 @@ return a / b;`,Eie=`
         ${t}
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
-    `}};function She(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.filterHeight===3&&h.inChannels%4===0?c=new c8(h):(c=new f8(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var Che={kernelName:Js,backendName:"webgpu",kernelFunc:She},m8=Gr({opSnippet:0,cpuKernelImpl:spe,supportsComplex:!0}),The={kernelName:gi,backendName:"webgpu",kernelFunc:m8},Nhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
-         if (isNanCustom(candidate)) {
+    `}};function She(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s}=t,{strides:i,pad:o,dilations:l,dimRoundingMode:u}=n,d=l;d==null&&(d=[1,1]);let h=N.computeConv2DInfo(a.shape,s.shape,i,d,o,u,!0),p=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]},{type:"int32",data:[h.inHeight,h.inWidth]}],c;return h.batchSize===1&&h.inHeight===h.outHeight&&h.inWidth===h.outWidth&&h.strideHeight===1&&h.strideWidth===1&&h.filterHeight===h.filterWidth&&h.inChannels===h.outChannels&&h.filterHeight===3&&h.inChannels%4===0?c=new cS(h):(c=new fS(h),p.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.outChannels/h.inChannels]})),r.runWebGPUProgram(c,[a,s],a.dtype,p)}var Che={kernelName:Js,backendName:"webgpu",kernelFunc:She},mS=Gr({opSnippet:0,cpuKernelImpl:spe,supportsComplex:!0}),The={kernelName:gi,backendName:"webgpu",kernelFunc:mS},Nhe=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[r]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+         if (isnan(candidate)) {
           bestValue = uniforms.NAN;
-         } else if (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
+         } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
            {  bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let r=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
         return ((a - 1u) / b + 1u);
@@ -6418,13 +6416,13 @@ return a / b;`,Eie=`
           ${r}
         }
        }
-     `}};function _h(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Nl({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=rpe(m,w.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=lpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":nh(e.dtype),x=[{type:"int32",data:[m]}],b=new Nhe(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),f=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function sb(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"sum",r)}var Ehe={kernelName:Ti,backendName:"webgpu",kernelFunc:sb};function Rhe(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Nl({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=m8({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=sb({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var Mhe={kernelName:Up,backendName:"webgpu",kernelFunc:Rhe},Fhe=xr({opType:4}),$he={kernelName:ei,backendName:"webgpu",kernelFunc:Fhe},Phe=Gr({opSnippet:4,dtype:"bool",cpuKernelImpl:jde}),_he={kernelName:Oo,backendName:"webgpu",kernelFunc:Phe},g8=xr({opType:5,cpuKernelImpl:Hde,dtype:"float32"}),zhe={kernelName:ti,backendName:"webgpu",kernelFunc:g8};function Wy(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Ohe={kernelName:Do,backendName:"webgpu",kernelFunc:Wy},Dhe=xr({opType:6,cpuKernelImpl:qde}),Lhe={kernelName:Lo,backendName:"webgpu",kernelFunc:Dhe},Bhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function Ph(e,t,r,n,a){let s=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),l=o,u=N.getAxesPermutation(l,s),d=e;u!=null&&(d=Nl({inputs:{x:e},attrs:{perm:u},backend:a}),l=N.getInnerMostAxes(l.length,s),i.push(d)),N.assertAxesAreInnerMostDims(n,l,s);let[h,p]=N.computeOutAndReduceShapes(d.shape,l),c=h;r&&(c=N.expandShapeToKeepDim(h,o));let f;if((n==="max"||n==="prod")&&a.shouldExecuteOnCPU([d])){let m=a.tensorMap.get(d.dataId).values;switch(n){case"max":let g=rpe(m,w.sizeFromShape(p),c,e.dtype);f=a.makeTensorInfo(c,e.dtype,g);break;case"prod":let{outVals:y,outShape:A,outDtype:x}=lpe(d.shape,d.dtype,m,l);f=a.makeTensorInfo(A,x,y);break;default:throw new Error(`${n} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(p),g=w.sizeFromShape(d.shape)/m,y={windowSize:m,inSize:m,batchSize:g,outSize:1},A=n==="mean"?"float32":rh(e.dtype),x=[{type:"int32",data:[m]}],b=new Nhe(y,n),v=a.runWebGPUProgram(b,[d],A,x);i.push(v),f=qe({inputs:{x:v},attrs:{shape:c},backend:a})}return i.forEach(m=>a.disposeData(m.dataId)),f}function ab(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ph(a,s,i,"sum",r)}var Ehe={kernelName:Ti,backendName:"webgpu",kernelFunc:ab};function Rhe(e){let{inputs:t,backend:r,attrs:n}=e,{equation:a}=n,s=t,{allDims:i,summedDims:o,idDims:l}=N.decodeEinsumEquation(a,s.length);N.checkEinsumDimSizes(i.length,l,s);let{path:u,steps:d}=N.getEinsumComputePath(o,l),h=d.length,p=null,c=i.length,f=[];for(let m=0;m<h;++m){for(let g of d[m]){let{permutationIndices:y,expandDims:A}=N.getEinsumPermutation(c,l[g]),x;N.isIdentityPermutation(y)?x=s[g]:(x=Nl({inputs:{x:s[g]},backend:r,attrs:{perm:y}}),f.push(x));let b=x.shape.slice();for(let v=0;v<A.length;++v)b.splice(A[v],0,1);w.arraysEqual(x.shape,b)||(x=qe({inputs:{x},backend:r,attrs:{shape:b}}),f.push(x)),p===null?p=x:(p=mS({inputs:{a:x,b:p},backend:r}),f.push(p))}m<h-1&&(u[m]>=0&&(p=ab({inputs:{x:p},backend:r,attrs:{axis:u[m]-(i.length-c),keepDims:!1}}),f.push(p)),c--)}for(let m of f)m!==p&&r.disposeData(m.dataId);return p}var Mhe={kernelName:Vp,backendName:"webgpu",kernelFunc:Rhe},Fhe=xr({opType:4}),$he={kernelName:ei,backendName:"webgpu",kernelFunc:Fhe},Phe=Gr({opSnippet:4,dtype:"bool",cpuKernelImpl:jde}),_he={kernelName:Oo,backendName:"webgpu",kernelFunc:Phe},gS=xr({opType:5,cpuKernelImpl:Hde,dtype:"float32"}),zhe={kernelName:ti,backendName:"webgpu",kernelFunc:gS};function By(e){let{inputs:t,attrs:r,backend:n}=e,{dim:a}=r,{input:s}=t,i=s.shape.length,o=s.shape.slice(),l=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),l=i+a+1),o.splice(l,0,1),qe({inputs:{x:s},backend:n,attrs:{shape:o}})}var Ohe={kernelName:Do,backendName:"webgpu",kernelFunc:By},Dhe=xr({opType:6,cpuKernelImpl:qde}),Lhe={kernelName:Lo,backendName:"webgpu",kernelFunc:Dhe},Bhe=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${tt()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function kd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Bhe(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var Whe={kernelName:Lu,backendName:"webgpu",kernelFunc:kd},Vhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function wd(e){let{backend:t,attrs:r}=e,{shape:n,value:a}=r,{dtype:s}=r;if(s=s||w.inferDtype(a),s==="string"){let i=w.getArrayFromDType(s,w.sizeFromShape(n));return i.fill(a),t.makeTensorInfo(n,s,i)}else{let i=new Bhe(n),o=[{type:"float32",data:[a]}];return t.runWebGPUProgram(i,[],s,o)}}var Whe={kernelName:Du,backendName:"webgpu",kernelFunc:wd},Vhe=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6433,7 +6431,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},Uhe={kernelName:Bo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Vhe(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},Ghe=xr({opType:7,cpuKernelImpl:Kde}),jhe={kernelName:ri,backendName:"webgpu",kernelFunc:Ghe},Hhe=Gr({opSnippet:12,dtype:"int32"}),qhe={kernelName:ni,backendName:"webgpu",kernelFunc:Hhe},Khe=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},y8=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=vue(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function A8(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function Dv(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=A8(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>y8(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var Xhe={kernelName:Ip,backendName:"webgpu",kernelFunc:Zhe},tu;function Zhe(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(J().getBool("WEBGPU_USE_IMPORT")&&i)return Dv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(tu==null&&(tu=document.createElement("canvas").getContext("2d")),tu.canvas.width=d,tu.canvas.height=h,tu.drawImage(a,0,0,d,h),a=tu.canvas),u||l||i||o)return Dv({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var Yhe=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+    `}},Uhe={kernelName:Bo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:r}=e,n=t,a=new Vhe(r.shape);return n.runWebGPUProgram(a,[r],r.dtype)}},Ghe=xr({opType:7,cpuKernelImpl:Kde}),jhe={kernelName:ri,backendName:"webgpu",kernelFunc:Ghe},Hhe=Gr({opSnippet:12,dtype:"int32"}),qhe={kernelName:ni,backendName:"webgpu",kernelFunc:Hhe},Khe=(e,t,r,n,a)=>{let s=[n,...r];return a&&s.push(a),e.createBindGroup({layout:t,entries:s.map((i,o)=>({binding:o,resource:i}))})},yS=(e,t,r,n,a,s=!1)=>{let i={dtype:a.dtype,shape:a.shape},o=vue(n,i,t,s),l=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:r,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function AS(e,t,r,n="",a=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(s=>s.length).join(",")+r.join(",")+e.variableNames.join(",")+n+a}function Ov(e){let{externalImage:t,backend:r,attrs:n,outShape:a,useImport:s}=e,{numChannels:i}=n,o=w.sizeFromShape(a),l=w.computeStrides(a),u=r.makeTensorInfo(a,"int32"),d=r.getFromPixelsProgram(s?"import":"copyExternal");d.updateOutputShape(a);let h=[u.shape],p=[u.dtype,s?"import":"copyExternal"],c=AS(d,h,p),f=d.getLayout(r.device),m=r.getAndSavePipeline(c,()=>yS(r.device,d,f.pipelineLayout,[],u,!0));d.setPipeline(m),s||r.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:d.makeInputTexture(r.device,a[1],a[0])},[a[1],a[0]]);let g=r.tensorMap.get(u.dataId);g.bufferInfo.buffer=r.acquireBuffer(g.bufferInfo.byteSize);let y=[o,i,...l,...d.dispatch];d.setUniform(r.device,y);let A;if(s){let x={source:t};A=r.device.importExternalTexture(x)}else A=d.inputTexture.createView();return r.runFromPixelsProgram(d,g.bufferInfo.buffer,f,A,u.dataId),u}var Xhe={kernelName:kp,backendName:"webgpu",kernelFunc:Zhe},eu;function Zhe(e){let{inputs:t,backend:r,attrs:n}=e,{pixels:a}=t,{numChannels:s}=n;if(a==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&a instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&a instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&a instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&a instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&a instanceof ImageBitmap,[d,h]=i?[a.videoWidth,a.videoHeight]:[a.width,a.height],p=[h,d,s];if(J().getBool("WEBGPU_USE_IMPORT")&&i)return Ov({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!0});if((i||o)&&(eu==null&&(eu=document.createElement("canvas").getContext("2d")),eu.canvas.width=d,eu.canvas.height=h,eu.drawImage(a,0,0,d,h),a=eu.canvas),u||l||i||o)return Ov({externalImage:a,backend:r,attrs:n,outShape:p,useImport:!1});let c=a.data,f=c;if(s!=null&&s!==4){f=new Uint8Array(a.width*a.height*s);let y=c.length,A=0;for(let x=0;x<y;x++)x%4<s&&(f[A++]=c[x])}let m=r.makeTensorInfo(p,"int32"),g=r.tensorMap.get(m.dataId);return g.values=new Int32Array(f),r.maybeReleaseBuffer(m.dataId),r.uploadToGPU(m.dataId),m}var Yhe=class{constructor(e,t,r,n,a){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,r),this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset")),a!=null&&(N.assertAndGetBroadcastShape(e,a),this.variableNames.push("scale")),this.offsetShape=n,this.scaleShape=a,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${tt()}
         if (index < uniforms.size)
         {
@@ -6446,7 +6444,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},Jhe={kernelName:ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Yhe(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function Qhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return h8({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var ece={kernelName:Ns,backendName:"webgpu",kernelFunc:Qhe};function tce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p}=n,c=d;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${c}'`);let f=N.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!0),m=[a,s],g=i!=null,y=o!=null;g&&m.push(i),y&&m.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],x;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4===0?x=new c8(f,g,p,y):(x=new f8(f,g,p,y),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),r.runWebGPUProgram(x,m,"float32",A)}var rce={kernelName:Es,backendName:"webgpu",kernelFunc:tce},nce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${mr(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+  `}},Jhe={kernelName:ai,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n,scale:a,offset:s,mean:i,variance:o}=e,{varianceEpsilon:l}=t,u=r,d=[n,i,o],h=null;s!=null&&(h=s.shape,d.push(s));let p=null;a!=null&&(p=a.shape,d.push(a));let c=new Yhe(n.shape,i.shape,o.shape,h,p),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(c,d,n.dtype,f)}};function Qhe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dataFormat:d,dilations:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=n,m=N.convertConv2DDataFormat(d),g=N.computeConv2DInfo(a.shape,s.shape,l,h,u,p,!1,m);return hS({x:a,filter:s,convInfo:g,backend:r,bias:i,preluActivationWeights:o,leakyreluAlpha:f,activation:c})}var ece={kernelName:Ns,backendName:"webgpu",kernelFunc:Qhe};function tce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dimRoundingMode:h,activation:p}=n,c=d;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(l,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${c}'`);let f=N.computeConv2DInfo(a.shape,s.shape,l,c,u,h,!0),m=[a,s],g=i!=null,y=o!=null;g&&m.push(i),y&&m.push(o);let A=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],x;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4===0?x=new cS(f,g,p,y):(x=new fS(f,g,p,y),A.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),r.runWebGPUProgram(x,m,"float32",A)}var rce={kernelName:Es,backendName:"webgpu",kernelFunc:tce},nce=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${mr(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
         ${tt()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6467,7 +6465,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function oce(e,t="int"){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push(`${t}(getIndices(resRC.x, resRC.z))`):n.push(`${r[a]}`);return n.join()}function x8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),C=Zde(v,x,f);return h.forEach(S=>r.disposeData(S.dataId)),r.makeTensorInfo(u.outputShape,C.dtype,C.values)}let m=new ice(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var lce={kernelName:Wo,backendName:"webgpu",kernelFunc:x8},uce=Gr({opSnippet:5,cpuKernelImpl:Jde,dtype:"bool"}),dce={kernelName:Uo,backendName:"webgpu",kernelFunc:uce},pce=Gr({opSnippet:6,dtype:"bool",cpuKernelImpl:Yde}),hce={kernelName:si,backendName:"webgpu",kernelFunc:pce};function cce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new $h(a.shape,15);return o.uniforms="alpha : f32;",r.runWebGPUProgram(o,[a],"float32",i)}var fce={kernelName:oi,backendName:"webgpu",kernelFunc:cce},mce=Gr({opSnippet:7,dtype:"bool",cpuKernelImpl:epe}),gce={kernelName:Go,backendName:"webgpu",kernelFunc:mce},yce=Gr({opSnippet:8,dtype:"bool",cpuKernelImpl:Qde}),Ace={kernelName:jo,backendName:"webgpu",kernelFunc:yce},xce=xr({opType:9,cpuKernelImpl:tpe}),bce={kernelName:li,backendName:"webgpu",kernelFunc:xce},vce=Gr({opSnippet:9,dtype:"bool"}),wce={kernelName:Ho,backendName:"webgpu",kernelFunc:vce},kce=xr({opType:10}),Ice={kernelName:Gu,backendName:"webgpu",kernelFunc:kce};function b8(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return _h(a,s,i,"max",r)}var Sce={kernelName:ui,backendName:"webgpu",kernelFunc:b8},Cce=Gr({opSnippet:15,cpuKernelImpl:npe}),Tce={kernelName:di,backendName:"webgpu",kernelFunc:Cce};function Nce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return Ln({inputs:{x:a},backend:r});h=new u8(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new l8(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var Ece={kernelName:pi,backendName:"webgpu",kernelFunc:Nce};function Rce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return _h(a,i,s,"mean",r)}var Mce={kernelName:hi,backendName:"webgpu",kernelFunc:Rce};function Fce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"min",r)}var $ce={kernelName:ci,backendName:"webgpu",kernelFunc:Fce},Pce=Gr({opSnippet:16,cpuKernelImpl:ape}),_ce={kernelName:fi,backendName:"webgpu",kernelFunc:Pce},zce=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>;`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=mr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function oce(e,t="int"){let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let a=0;a<e.length;a++)a===2?n.push(`${t}(getIndices(resRC.x, resRC.z))`):n.push(`${r[a]}`);return n.join()}function xS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a,indices:s}=t,{axis:i,batchDims:o}=n,l=w.parseAxisParam(i,a.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(a,s,l,o),d=w.sizeFromShape(s.shape),h=[],p=qe({inputs:{x:a},backend:r,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),c=qe({inputs:{x:s},backend:r,attrs:{shape:[u.batchSize,d/u.batchSize]}});h.push(p),h.push(c);let f=[u.batchSize,u.outerSize,d/u.batchSize,u.sliceSize];if(r.shouldExecuteOnCPU([a,s])){let A=r.tensorMap.get(c.dataId).values,x=We(c.shape,c.dtype,A),b=r.tensorMap.get(p.dataId).values,v=We(p.shape,p.dtype,b),C=Zde(v,x,f);return h.forEach(S=>r.disposeData(S.dataId)),r.makeTensorInfo(u.outputShape,C.dtype,C.values)}let m=new ice(p.shape,f),g=r.runWebGPUProgram(m,[p,c],p.dtype);h.push(g);let y=qe({inputs:{x:g},backend:r,attrs:{shape:u.outputShape}});return h.forEach(A=>r.disposeData(A.dataId)),y}var lce={kernelName:Wo,backendName:"webgpu",kernelFunc:xS},uce=Gr({opSnippet:5,cpuKernelImpl:Jde,dtype:"bool"}),dce={kernelName:Uo,backendName:"webgpu",kernelFunc:uce},pce=Gr({opSnippet:6,dtype:"bool",cpuKernelImpl:Yde}),hce={kernelName:si,backendName:"webgpu",kernelFunc:pce};function cce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{alpha:s}=n,i=[{type:"float32",data:[s]}],o=new Fh(a.shape,15);return o.uniforms="alpha : f32;",r.runWebGPUProgram(o,[a],"float32",i)}var fce={kernelName:oi,backendName:"webgpu",kernelFunc:cce},mce=Gr({opSnippet:7,dtype:"bool",cpuKernelImpl:epe}),gce={kernelName:Go,backendName:"webgpu",kernelFunc:mce},yce=Gr({opSnippet:8,dtype:"bool",cpuKernelImpl:Qde}),Ace={kernelName:jo,backendName:"webgpu",kernelFunc:yce},xce=xr({opType:9,cpuKernelImpl:tpe}),bce={kernelName:li,backendName:"webgpu",kernelFunc:xce},vce=Gr({opSnippet:9,dtype:"bool"}),wce={kernelName:Ho,backendName:"webgpu",kernelFunc:vce},kce=xr({opType:10}),Ice={kernelName:Uu,backendName:"webgpu",kernelFunc:kce};function bS(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{reductionIndices:s,keepDims:i}=n;return Ph(a,s,i,"max",r)}var Sce={kernelName:ui,backendName:"webgpu",kernelFunc:bS},Cce=Gr({opSnippet:15,cpuKernelImpl:npe}),Tce={kernelName:di,backendName:"webgpu",kernelFunc:Cce};function Nce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:l}=n,u=1,d=N.computePool2DInfo(a.shape,s,i,u,o,l),h,p=[];if(d.filterHeight===1&&d.filterWidth===1){if(w.arraysEqual(d.inShape,d.outShape))return Ln({inputs:{x:a},backend:r});h=new uS(d),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]})}else h=new lS(d,"max"),p.push({type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]},{type:"int32",data:[d.effectiveFilterHeight,d.effectiveFilterWidth]});return r.runWebGPUProgram(h,[a],a.dtype,p)}var Ece={kernelName:pi,backendName:"webgpu",kernelFunc:Nce};function Rce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{keepDims:s,axis:i}=n;return Ph(a,i,s,"mean",r)}var Mce={kernelName:hi,backendName:"webgpu",kernelFunc:Rce};function Fce(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ph(a,s,i,"min",r)}var $ce={kernelName:ci,backendName:"webgpu",kernelFunc:Fce},Pce=Gr({opSnippet:16,cpuKernelImpl:ape}),_ce={kernelName:fi,backendName:"webgpu",kernelFunc:Pce},zce=class{constructor(e,t,r){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,a)=>n[0]+e[a]+n[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((n,a)=>{this.uniforms+=` pad${a} : vec2<i32>;`}),this.offset=r==="reflect"?0:1,this.shaderKey=`mirrorPad_${r}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),r=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),n=e===1?"start":"start[i]",a=e===1?"end":"end[i]",s=e===1?"outC":"outC[i]",i=mr(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${tt()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6484,7 +6482,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},Oce={kernelName:mi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new zce(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function Dce(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=ipe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new $h(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var Lce={kernelName:qo,backendName:"webgpu",kernelFunc:Dce};function Bce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Un.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Wce={kernelName:Xo,backendName:"webgpu",kernelFunc:Bce};function Vce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Un.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Uce={kernelName:Zo,backendName:"webgpu",kernelFunc:Vce};function Ef(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=Ph({inputs:{input:n},backend:r}),s=Ef({inputs:{x:a},backend:r}),i=h0({inputs:{input:n},backend:r}),o=Ef({inputs:{x:i},backend:r}),l=vd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return kd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Gce={kernelName:ml,backendName:"webgpu",kernelFunc:Ef};function v8(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=Ph({inputs:{input:n},backend:r}),s=v8({inputs:{x:a},backend:r}),i=h0({inputs:{input:n},backend:r}),o=Ef({inputs:{x:i},backend:r}),l=vd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return kd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var jce={kernelName:Yo,backendName:"webgpu",kernelFunc:v8};function Hce(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Wy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Wy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=p8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var qce={kernelName:Qo,backendName:"webgpu",kernelFunc:Hce},Kce=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=mr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},Oce={kernelName:mi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:r})=>{let{x:n}=e,{paddings:a,mode:s}=t,i=r,o=a.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new zce(n.shape,a,s);return i.runWebGPUProgram(l,[n],n.dtype,o)}};function Dce(e){let{inputs:t,backend:r}=e,{x:n}=t;if(r.shouldExecuteOnCPU([n])){let s=r.tensorMap.get(n.dataId),[i,o]=ipe(s.values,n.shape,n.dtype);return r.makeTensorInfo(o,n.dtype,i)}let a=new Fh(n.shape,11);return r.runWebGPUProgram(a,[n],n.dtype)}var Lce={kernelName:qo,backendName:"webgpu",kernelFunc:Dce};function Bce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l}=n,u=r.readSync(a.dataId),d=r.readSync(s.dataId),{selectedIndices:h}=Un.nonMaxSuppressionV3Impl(u,d,i,o,l);return r.makeTensorInfo([h.length],"int32",new Int32Array(h))}var Wce={kernelName:Xo,backendName:"webgpu",kernelFunc:Bce};function Vce(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:r,attrs:n}=e,{boxes:a,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:l,softNmsSigma:u}=n,d=r.readSync(a.dataId),h=r.readSync(s.dataId),p=i,c=o,f=l,m=u,{selectedIndices:g,selectedScores:y}=Un.nonMaxSuppressionV5Impl(d,h,p,c,f,m);return[r.makeTensorInfo([g.length],"int32",new Int32Array(g)),r.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Uce={kernelName:Zo,backendName:"webgpu",kernelFunc:Vce};function Nf(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="complex64"){let a=$h({inputs:{input:n},backend:r}),s=Nf({inputs:{x:a},backend:r}),i=p0({inputs:{input:n},backend:r}),o=Nf({inputs:{x:i},backend:r}),l=bd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return wd({attrs:{shape:n.shape,dtype:n.dtype,value:n.dtype==="string"?"":0},backend:r})}var Gce={kernelName:ml,backendName:"webgpu",kernelFunc:Nf};function vS(e){let{inputs:t,backend:r}=e,{x:n}=t;if(n.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(n.dtype==="complex64"){let a=$h({inputs:{input:n},backend:r}),s=vS({inputs:{x:a},backend:r}),i=p0({inputs:{input:n},backend:r}),o=Nf({inputs:{x:i},backend:r}),l=bd({inputs:{real:s,imag:o},backend:r});return r.disposeData(a.dataId),r.disposeData(s.dataId),r.disposeData(i.dataId),r.disposeData(o.dataId),l}else return wd({attrs:{shape:n.shape,dtype:n.dtype,value:1},backend:r})}var jce={kernelName:Yo,backendName:"webgpu",kernelFunc:vS};function Hce(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return By({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=By({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=pS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var qce={kernelName:Qo,backendName:"webgpu",kernelFunc:Hce},Kce=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((r,n)=>r[0]+e[n]+r[1]),this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((r,n)=>{this.uniforms+=` pad${n} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=mr(e),r=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),a=e>1?`${t}(${r})`:`${r}`,s=e>1?`${t}(${n})`:`${n}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${tt()}
         if (index < uniforms.size) {
           let start = ${a};
@@ -6499,7 +6497,7 @@ return a / b;`,Eie=`
           }
         }
       }
-    `}},w8=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return Ln({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return kd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Kce(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},Xce={kernelName:yi,backendName:"webgpu",kernelFunc:w8},Zce=Gr({opSnippet:13}),Yce={kernelName:Ai,backendName:"webgpu",kernelFunc:Zce};function Jce(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new i8(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var Qce={kernelName:xi,backendName:"webgpu",kernelFunc:Jce};function efe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return _h(a,s,i,"prod",r)}var tfe={kernelName:el,backendName:"webgpu",kernelFunc:efe},rfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=upe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},nfe={kernelName:qu,backendName:"webgpu",kernelFunc:rfe},k8=Gr({opSnippet:3}),afe={kernelName:Qs,backendName:"webgpu",kernelFunc:k8},sfe=xr({opType:13}),ife={kernelName:bi,backendName:"webgpu",kernelFunc:sfe},ofe=xr({opType:14}),lfe={kernelName:wi,backendName:"webgpu",kernelFunc:ofe},ufe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},wS=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{paddings:s,constantValue:i}=n;if(s.every(u=>w.arraysEqual(u,[0,0])))return Ln({inputs:{x:a},backend:r});if(w.sizeFromShape(a.shape)===0){let u=s.map((d,h)=>d[0]+a.shape[h]+d[1]);return wd({backend:r,attrs:{shape:u,value:i,dtype:a.dtype}})}let o=[{type:"float32",data:[i]}];s.map(u=>o.push({type:"int32",data:[u[0],u[1]]}));let l=new Kce(a.shape,s);return r.runWebGPUProgram(l,[a],a.dtype,o)},Xce={kernelName:yi,backendName:"webgpu",kernelFunc:wS},Zce=Gr({opSnippet:13}),Yce={kernelName:Ai,backendName:"webgpu",kernelFunc:Zce};function Jce(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=new iS(14,n.shape,a.shape);return r.runWebGPUProgram(s,[n,a],"float32")}var Qce={kernelName:xi,backendName:"webgpu",kernelFunc:Jce};function efe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{axis:s,keepDims:i}=n;return Ph(a,s,i,"prod",r)}var tfe={kernelName:el,backendName:"webgpu",kernelFunc:efe},rfe=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=upe(n,a,s,i);return t.makeTensorInfo([o.length],i,o)},nfe={kernelName:Hu,backendName:"webgpu",kernelFunc:rfe},kS=Gr({opSnippet:3}),afe={kernelName:Qs,backendName:"webgpu",kernelFunc:kS},sfe=xr({opType:13}),ife={kernelName:bi,backendName:"webgpu",kernelFunc:sfe},ofe=xr({opType:14}),lfe={kernelName:wi,backendName:"webgpu",kernelFunc:ofe},ufe=class{constructor(e,t,r){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,r,e[3]],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${tt()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6573,7 +6571,7 @@ return a / b;`,Eie=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function cfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new hfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var ffe={kernelName:Xu,backendName:"webgpu",kernelFunc:cfe},mfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
+    `}};function cfe(e){let{inputs:t,backend:r,attrs:n}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,d=s&&l>1?1:0,h=s&&u>1?1:0,p=[{type:"float32",data:[d,h]},{type:"float32",data:[s?.5:0]}],c=new hfe(a.shape,l,u,i);return r.runWebGPUProgram(c,[a],a.dtype,p)}var ffe={kernelName:Ku,backendName:"webgpu",kernelFunc:cfe},mfe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
           cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${tt()}
 
@@ -6632,7 +6630,7 @@ return a / b;`,Eie=`
 
          ${o}
         }
-      }`}};function bfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=kd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new xfe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var vfe={kernelName:al,backendName:"webgpu",kernelFunc:bfe},wfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
+      }`}};function bfe(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a,updates:s}=t,{shape:i}=n,{sliceRank:o,numUpdates:l,sliceSize:u,strides:d,outputSize:h}=N.calculateShapes(s,a,i),p=[h/u,u];if(h===0)return r.makeTensorInfo(i,a.dtype);let c=qe({inputs:{x:a},backend:r,attrs:{shape:[l,o]}}),f=qe({inputs:{x:s},backend:r,attrs:{shape:[l,u]}}),m=f.dtype,g=wd({backend:r,attrs:{shape:p,value:0,dtype:m}}),y=w.sizeFromShape(f.shape),A=[{type:"int32",data:[o]},{type:"int32",data:d},{type:"int32",data:[y]}],x=new xfe(f.shape,o,c.shape.length,f.shape.length,d,p,m),b=r.runWebGPUProgram(x,[f,c],m,A,g),v=qe({inputs:{x:b},backend:r,attrs:{shape:i}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(b.dataId),v}var vfe={kernelName:al,backendName:"webgpu",kernelFunc:bfe},wfe=class{constructor(e,t,r){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=r,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let r=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[],a=[];for(let s=0;s<this.outputShape.length;s++)a.push(`${r[s]}`),s<this.cRank&&n.push(`${r[s]}`);e=n.join(),t=a.join()}return`
       ${tt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6644,7 +6642,7 @@ return a / b;`,Eie=`
           }
         }
       }
-    `}};function kfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new wfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Dr(a.dtype,s.dtype))}var Ife={kernelName:sl,backendName:"webgpu",kernelFunc:kfe},Sfe=xr({opType:19}),Cfe={kernelName:Si,backendName:"webgpu",kernelFunc:Sfe},Tfe=xr({opType:17}),Nfe={kernelName:Ii,backendName:"webgpu",kernelFunc:Tfe},Efe=xr({opType:18}),Rfe={kernelName:ol,backendName:"webgpu",kernelFunc:Efe},I8=Gr({opSnippet:2,cpuKernelImpl:mpe,supportsComplex:!0}),Mfe={kernelName:Ri,backendName:"webgpu",kernelFunc:I8};function Ffe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=b8({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=I8({inputs:{a,b:u},backend:r}),h=g8({inputs:{x:d},backend:r}),p=sb({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=k8({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var $fe={kernelName:Ni,backendName:"webgpu",kernelFunc:Ffe},Pfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=w8({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},_fe={kernelName:ll,backendName:"webgpu",kernelFunc:Pfe},zfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=mr(a.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function kfe(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=new wfe(n.shape.length,a.shape,a.shape.length);return r.runWebGPUProgram(i,[n,a,s],Dr(a.dtype,s.dtype))}var Ife={kernelName:sl,backendName:"webgpu",kernelFunc:kfe},Sfe=xr({opType:19}),Cfe={kernelName:Si,backendName:"webgpu",kernelFunc:Sfe},Tfe=xr({opType:17}),Nfe={kernelName:Ii,backendName:"webgpu",kernelFunc:Tfe},Efe=xr({opType:18}),Rfe={kernelName:ol,backendName:"webgpu",kernelFunc:Efe},IS=Gr({opSnippet:2,cpuKernelImpl:mpe,supportsComplex:!0}),Mfe={kernelName:Ri,backendName:"webgpu",kernelFunc:IS};function Ffe(e){let{inputs:t,backend:r,attrs:n}=e,{logits:a}=t,{dim:s}=n,i=w.parseAxisParam([s],a.shape),o=bS({inputs:{x:a},backend:r,attrs:{reductionIndices:i,keepDims:!1}}),l=N.expandShapeToKeepDim(o.shape,i),u=qe({inputs:{x:o},backend:r,attrs:{shape:l}}),d=IS({inputs:{a,b:u},backend:r}),h=gS({inputs:{x:d},backend:r}),p=ab({inputs:{x:h},backend:r,attrs:{axis:i,keepDims:!1}}),c=qe({inputs:{x:p},backend:r,attrs:{shape:l}}),f=kS({inputs:{a:h,b:c},backend:r});return r.disposeData(o.dataId),r.disposeData(u.dataId),r.disposeData(d.dataId),r.disposeData(h.dataId),r.disposeData(p.dataId),r.disposeData(c.dataId),f}var $fe={kernelName:Ni,backendName:"webgpu",kernelFunc:Ffe},Pfe=e=>{let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n;w.assert(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=s.reduce((y,A)=>y*A),l=[[0,0]];l.push(...i);for(let y=1+s.length;y<a.shape.length;++y)l.push([0,0]);let u=[],d=wS({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),h=N.getReshaped(d.shape,s,o,!1),p=N.getPermuted(h.length,s.length,!1),c=N.getReshapedPermuted(d.shape,s,o,!1),f=qe({inputs:{x:d},backend:r,attrs:{shape:h}}),m=Nl({inputs:{x:f},backend:r,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:r,attrs:{shape:c}});return u.push(d),u.push(f),u.push(m),u.forEach(y=>r.disposeData(y.dataId)),g},_fe={kernelName:ll,backendName:"webgpu",kernelFunc:Pfe},zfe=class{constructor(e,t,r,n,a,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=s,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${r}_${n}_${o}`;let l=mr(a.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let u="";r===1?u="i":r===2&&(u="i, j"),this.indicesSnippet=`getIndices(${u})`;let d="";n===1?d="i":n===2&&(d="i, coords[1]"),this.updatesSnippet=`getUpdates(${d})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${tt()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6674,14 +6672,14 @@ return a / b;`,Eie=`
             }
           }
         }
-      }`}};function Ofe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new zfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Dfe={kernelName:Jp,backendName:"webgpu",kernelFunc:Ofe};function Lfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=wd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Bfe={kernelName:ul,backendName:"webgpu",kernelFunc:Lfe},Wfe=xr({opType:20}),Vfe={kernelName:Ci,backendName:"webgpu",kernelFunc:Wfe},Ufe={kernelName:ed,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new $h(r.shape,21);return n.runWebGPUProgram(a,[r],r.dtype)}},Gfe=Gr({opSnippet:11}),jfe={kernelName:Ei,backendName:"webgpu",kernelFunc:Gfe},Hfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=mr(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function Ofe(e){let{inputs:t,backend:r,attrs:n}=e,{sparseIndices:a,sparseValues:s,defaultValue:i}=t,{outputShape:o}=n,{sliceRank:l,numUpdates:u,strides:d,outputSize:h}=N.calculateShapes(s,a,o),p=!1,c=[{type:"int32",data:[u]},{type:"int32",data:[l]},{type:"int32",data:d}],f=new zfe(u,l,a.shape.length,s.shape.length,d,[h,1],p),m=r.runWebGPUProgram(f,[s,a,i],s.dtype,c),g=qe({inputs:{x:m},backend:r,attrs:{shape:o}});return r.disposeData(m.dataId),g}var Dfe={kernelName:Yp,backendName:"webgpu",kernelFunc:Ofe};function Lfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{numOrSizeSplits:s,axis:i}=n,o=w.parseAxisParam(i,a.shape)[0],l=N.prepareSplitSize(a,s,o),u=a.shape.length,d=new Array(u).fill(0),h=a.shape.slice();return l.map(p=>{let c=[...h];c[o]=p;let f=vd({inputs:{x:a},backend:r,attrs:{begin:d,size:c}});return d[o]+=p,f})}var Bfe={kernelName:ul,backendName:"webgpu",kernelFunc:Lfe},Wfe=xr({opType:20}),Vfe={kernelName:Ci,backendName:"webgpu",kernelFunc:Wfe},Ufe={kernelName:Qu,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:r}=e,n=t,a=new Fh(r.shape,21);return n.runWebGPUProgram(a,[r],r.dtype)}},Gfe=Gr({opSnippet:11}),jfe={kernelName:Ei,backendName:"webgpu",kernelFunc:Gfe},Hfe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=mr(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let r=0;t=this.outputShape.map((n,a)=>(r++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${r-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${tt()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function qfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let C=_t.computeOutShape(A,x,b),S=wd({inputs:{x:a},backend:r,attrs:{begin:A,size:C}});v=qe({inputs:{x:S},backend:r,attrs:{shape:f}}),r.disposeData(S.dataId)}else if(r.shouldExecuteOnCPU([a])){let C=r.readSync(a.dataId),S=We(a.shape,a.dtype,C),E=cpe(c,S,b,A);v=r.makeTensorInfo(f,a.dtype,E.values)}else{let C=new Hfe(c),S=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(C,[a],a.dtype,S);v=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var Kfe={kernelName:dl,backendName:"webgpu",kernelFunc:qfe};function Xfe(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=fpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var Zfe={kernelName:Qp,backendName:"webgpu",kernelFunc:Xfe},Yfe=xr({opType:22}),Jfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Yfe},Qfe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=eme(this.rank,"uniforms.");return`
+     `}};function qfe(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{begin:s,end:i,strides:o,beginMask:l,endMask:u,ellipsisMask:d,newAxisMask:h,shrinkAxisMask:p}=n,{finalShapeSparse:c,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:A,end:x,strides:b}=_t.sliceInfo(a.shape,s,i,o,l,u,d,h,p),v;if(m)v=qe({inputs:{x:a},backend:r,attrs:{shape:f}});else if(g||y){w.assert(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);let C=_t.computeOutShape(A,x,b),S=vd({inputs:{x:a},backend:r,attrs:{begin:A,size:C}});v=qe({inputs:{x:S},backend:r,attrs:{shape:f}}),r.disposeData(S.dataId)}else if(r.shouldExecuteOnCPU([a])){let C=r.readSync(a.dataId),S=We(a.shape,a.dtype,C),E=cpe(c,S,b,A);v=r.makeTensorInfo(f,a.dtype,E.values)}else{let C=new Hfe(c),S=[{type:"int32",data:A},{type:"int32",data:b}],E=r.runWebGPUProgram(C,[a],a.dtype,S);v=qe({inputs:{x:E},backend:r,attrs:{shape:f}}),r.disposeData(E.dataId)}return v}var Kfe={kernelName:dl,backendName:"webgpu",kernelFunc:qfe};function Xfe(e){let{inputs:t,backend:r,attrs:n}=e,{separator:a,nGramWidths:s,leftPad:i,rightPad:o,padWidth:l,preserveShortSequences:u}=n,{data:d,dataSplits:h}=t,p=r.readSync(d.dataId),c=r.readSync(h.dataId),[f,m]=fpe(p,c,a,s,i,o,l,u);return[r.makeTensorInfo([f.length],"string",f),r.makeTensorInfo(h.shape,"int32",m)]}var Zfe={kernelName:Jp,backendName:"webgpu",kernelFunc:Xfe},Yfe=xr({opType:22}),Jfe={kernelName:Mi,backendName:"webgpu",kernelFunc:Yfe},Qfe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[n]*t[n];this.outputShape=r,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=eme(this.rank,"uniforms.");return`
       ${tt()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6815,7 +6813,7 @@ return a / b;`,Eie=`
             }
           }
         }
-      `}};function ru(e,t){t!==null&&e.disposeData(t.dataId)}function Lv(e){let t=1;for(;t<e;)t*=2;return t}function sme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,C]=ype(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(C.shape,C.dtype,C.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,kd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=Lv(s),p=Lv(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,v,C)=>{let S=f(),E=new nme(C),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],_=c;c=r.runWebGPUProgram(E,S,"int32",R),ru(r,_)};for(let b=1;b<h;b*=2){let v=b*2;for(let C=b;C>=1;C/=2)m(v,C,[u,p])}for(let b=p;b>h;b/=2){let v=f(),C=new ame([u,b/2]),S=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(C,v,"int32",S),ru(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=wd({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),ru(r,g);let y=x8({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});ru(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),ru(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),ru(r,x),[y,c]}var ime={kernelName:hl,backendName:"webgpu",kernelFunc:sme},ome=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function tu(e,t){t!==null&&e.disposeData(t.dataId)}function Dv(e){let t=1;for(;t<e;)t*=2;return t}function sme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{k:s,sorted:i}=n,o=a.shape,l=o[o.length-1];if(r.shouldExecuteOnCPU([a])){let b=r.readSync(a.dataId),[v,C]=ype(b,o,a.dtype,s,i);return[r.makeTensorInfo(v.shape,v.dtype,v.values),r.makeTensorInfo(C.shape,C.dtype,C.values)]}if(s===0)return o[o.length-1]=0,[r.makeTensorInfo(o,a.dtype,[]),r.makeTensorInfo(o,"int32",[])];if(l===1)return[a,wd({attrs:{shape:o,dtype:"int32",value:0},backend:r})];let u=w.sizeFromShape(o)/l,d=qe({inputs:{x:a},attrs:{shape:[u,l]},backend:r}),h=Dv(s),p=Dv(l),c=null,f=()=>c===null?[d,d]:[d,c],m=(b,v,C)=>{let S=f(),E=new nme(C),R=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[b]},{type:"int32",data:[v]}],_=c;c=r.runWebGPUProgram(E,S,"int32",R),tu(r,_)};for(let b=1;b<h;b*=2){let v=b*2;for(let C=b;C>=1;C/=2)m(v,C,[u,p])}for(let b=p;b>h;b/=2){let v=f(),C=new ame([u,b/2]),S=[{type:"int32",data:[l]},{type:"int32",data:[c===null?1:0]},{type:"int32",data:[h]}],E=c;c=r.runWebGPUProgram(C,v,"int32",S),tu(r,E);let R=h/2,_=R*2;for(let M=R;M>=1;M/=2)m(_,M,c.shape)}let g=c;c=vd({inputs:{x:c},backend:r,attrs:{begin:0,size:[u,s]}}),tu(r,g);let y=xS({inputs:{x:d,indices:c},backend:r,attrs:{axis:1,batchDims:1}});tu(r,d);let A=o.slice(0,-1);A.push(s),g=c,c=qe({inputs:{x:c},attrs:{shape:A},backend:r}),tu(r,g);let x=y;return y=qe({inputs:{x:y},attrs:{shape:A},backend:r}),tu(r,x),[y,c]}var ime={kernelName:hl,backendName:"webgpu",kernelFunc:sme},ome=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6931,7 +6929,7 @@ return a / b;`,Eie=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function lme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new ome(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var ume={kernelName:cl,backendName:"webgpu",kernelFunc:lme};function dme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=wd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var pme={kernelName:fl,backendName:"webgpu",kernelFunc:dme},hme=[Ode,bpe,wpe,Spe,Mpe,$pe,_pe,Ope,Vpe,Hpe,Kpe,Jpe,Wde,rhe,dhe,fhe,ghe,Ahe,vhe,Ihe,Che,Mhe,$he,_he,zhe,Ohe,Lhe,Whe,Uhe,Xhe,jhe,qhe,Jhe,ece,rce,sce,lce,dce,hce,Bde,ehe,fce,gce,Ace,bce,wce,Ice,Sce,Tce,Ece,Mce,$ce,_ce,Oce,The,Lce,Wce,Uce,Upe,jce,qce,Xce,Yce,Qce,tfe,nfe,Gpe,afe,ife,lfe,_de,pfe,ffe,gfe,Afe,vfe,Ife,Cfe,Nfe,Rfe,Bpe,Kfe,Zfe,$fe,_fe,Dfe,Bfe,Vfe,Ufe,jfe,Mfe,Ehe,Jfe,rme,ime,ume,Epe,pme,Gce];for(let e of hme)Wn(e);var cme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=Bv(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=Bv(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Bv(e,t){return`${e}_${t}`}var S8=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+        `}};function lme(e){let{inputs:t,backend:r,attrs:n}=e,{image:a,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:l,outputShape:u}=n,[d,h,p,c]=a.shape,[f,m]=u!=null?u:[h,p],g=[d,f,m,c],y=new ome(g),A=i==="nearest"?1:2,x;switch(o){case"constant":x=1;break;case"reflect":x=2;break;case"wrap":x=3;break;case"nearest":x=4;break;default:x=1;break}let b=[{type:"int32",data:[A]},{type:"int32",data:[x]},{type:"float32",data:[l]}];return r.runWebGPUProgram(y,[a,s],"float32",b)}var ume={kernelName:cl,backendName:"webgpu",kernelFunc:lme};function dme(e){let{inputs:t,backend:r,attrs:n}=e,{value:a}=t,{axis:s}=n;s<0&&(s+=a.shape.length);let i=a,o=i.shape.length,l=a.shape[s],u=new Array(o-1),d=0;for(let m=0;m<o;m++)m!==s&&(u[d++]=i.shape[m]);let h=[],p=new Array(o).fill(0),c=i.shape.slice();c[s]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[s]=m;let g=vd({inputs:{x:i},backend:r,attrs:{begin:p,size:c}}),y=qe({inputs:{x:g},backend:r,attrs:{shape:u}});f[m]=y,h.push(g)}return h.forEach(m=>r.disposeData(m.dataId)),f}var pme={kernelName:fl,backendName:"webgpu",kernelFunc:dme},hme=[Ode,bpe,wpe,Spe,Mpe,$pe,_pe,Ope,Vpe,Hpe,Kpe,Jpe,Wde,rhe,dhe,fhe,ghe,Ahe,vhe,Ihe,Che,Mhe,$he,_he,zhe,Ohe,Lhe,Whe,Uhe,Xhe,jhe,qhe,Jhe,ece,rce,sce,lce,dce,hce,Bde,ehe,fce,gce,Ace,bce,wce,Ice,Sce,Tce,Ece,Mce,$ce,_ce,Oce,The,Lce,Wce,Uce,Upe,jce,qce,Xce,Yce,Qce,tfe,nfe,Gpe,afe,ife,lfe,_de,pfe,ffe,gfe,Afe,vfe,Ife,Cfe,Nfe,Rfe,Bpe,Kfe,Zfe,$fe,_fe,Dfe,Bfe,Vfe,Ufe,jfe,Mfe,Ehe,Jfe,rme,ime,ume,Epe,pme,Gce];for(let e of hme)Wn(e);var cme=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,r=!1){let n=Lv(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let s=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(s),s}this.numBytesAllocated+=e;let a=this.device.createBuffer({mappedAtCreation:r,size:e,usage:t});return this.usedBuffers.get(n).push(a),a}releaseBuffer(e,t,r){if(this.freeBuffers.size===0)return;let n=Lv(t,r);this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.freeBuffers.get(n).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let a=this.usedBuffers.get(n),s=a.indexOf(e);if(s<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");a.splice(s,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,r){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,r)},n=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(r=>{r.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Lv(e,t){return`${e}_${t}`}var SS=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Xe(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${tt()}
@@ -6945,10 +6943,10 @@ return a / b;`,Eie=`
           }
         }
       }
-  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},fme=class extends S8{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},mme=J().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),C8=class extends Cu{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!rb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new cme(this.device),this.tensorMap=new Op(this,kr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),J().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return C8.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*Oy(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*Oy(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new S8),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new fme),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),J().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=r8(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let S=this.tensorMap.get(a.dataId);return S.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(S=>S.shape),o="int32";i.map(S=>{s.push({type:o,data:S})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let S=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?S/4:S]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((S,E)=>{if(S.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(S.dataId),{dtype:this.tensorMap.get(S.dataId).dtype,shape:S.shape,name:e.variableNames[E]}}),h=d.map(S=>S.dtype).concat(a.dtype),p=d.map(S=>N.getBroadcastDims(S.shape,a.shape)),c=d.map(S=>w.arraysEqual(S.shape,a.shape)).join("_"),f=p.map(S=>S.join("_")).join(";"),m=A8(e,i,h,f,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(m,()=>y8(this.device,e,y,d,a)),x=this.activeTimers!=null,b=Khe(this.device,g,t.map(S=>this.tensorToBinding(S)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let v=this.getComputePass();x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(A),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(S=>{this.commandQueueOwnedIds.add(S.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let C={byteSize:u.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};return this.uniformDisposalQueue.push(C),J().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}runFromPixelsProgram(e,t,r,n,a){let s=this.device.createBindGroup({layout:r.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:n},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,s),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=mme){return J().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&w.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},ib=C8;ib.nextDataId=0;var T8={};Le(T8,{WebGPUBackend:()=>ib,webgpu_util:()=>e8});rb()&&Al("webgpu",async()=>{J().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:J().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r={},n=t.features.has("timestamp-query");n?r={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(r);return new ib(a,n)},3);var Ut=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ut||{}),c0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(c0||{}),N8;function gme(e){N8=e.wasm.cwrap(Ts,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function yme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=c0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,C=new Uint8Array(new Int32Array(a.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return N8(p,C,a.shape.length,c,S,s.shape.length,l,u,g,f,m,h||0,v),b}var Ame={kernelName:Ts,backendName:"wasm",setupFunc:gme,kernelFunc:yme};function br(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var xme=br(Mo);function jr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var bme=!0,vme=jr(Ha,bme),E8;function wme(e){E8=e.wasm.cwrap(Vs,null,["array","number","number","number"])}function kme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return E8(s,a.length,Ut[n.dtype],i),n}var Ime={kernelName:Vs,backendName:"wasm",setupFunc:wme,kernelFunc:kme};function f0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Sme={kernelName:ii,backendName:"wasm",kernelFunc:f0},R8;function Cme(e){R8=e.wasm.cwrap(Fi,null,["number","array","number","number","number","array","number"])}function Su(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Nme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=Tme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=f0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return R8(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function Tme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Nme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Eme={kernelName:Fi,backendName:"wasm",kernelFunc:Su,setupFunc:Cme};function Li(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Su({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var M8;function Rme(e){M8=e.wasm.cwrap(Ru,null,["number, number, number"])}function Mme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;M8(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Fme={kernelName:Ru,backendName:"wasm",setupFunc:Rme,kernelFunc:Mme},F8;function $me(e){F8=e.wasm.cwrap(Mu,null,["number, number, number"])}function Pme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;F8(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var _me={kernelName:Mu,backendName:"wasm",setupFunc:$me,kernelFunc:Pme},$8;function zme(e){$8=e.wasm.cwrap(Us,null,["number","number","number","number","number"])}function Ome(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Li(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[d[0]];return $8(o,Ut[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var Dme={kernelName:Us,backendName:"wasm",kernelFunc:Ome,setupFunc:zme},P8;function Lme(e){P8=e.wasm.cwrap(Gs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),v=n.dataIdMap.get(b.dataId).id;return P8(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,v),b}var Wme={kernelName:Gs,backendName:"wasm",setupFunc:Lme,kernelFunc:Bme};function Qr(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Vme={kernelName:tl,backendName:"wasm",kernelFunc:Qr},_8;function Ume(e){_8=e.wasm.cwrap(js,null,["number","array","number","number","array","number","number","number","number"])}function Gme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=Qr({inputs:{x:a},backend:r,attrs:{shape:x}}),C=Qr({inputs:{x:s},backend:r,attrs:{shape:b}}),S=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(C.dataId).id,R=i?v.shape[2]:v.shape[1],_=o?C.shape[1]:C.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],v.dtype),O=r.dataIdMap.get(I.dataId).id,z=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return _8(S,z,v.shape.length,E,j,C.shape.length,i,o,O),r.disposeData(v.dataId),r.disposeData(C.dataId),I.shape=A,I}var jme={kernelName:js,backendName:"wasm",setupFunc:Ume,kernelFunc:Gme};function No(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(i))),u}if(t.dtype==="string"){let f=If(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)Hme(l,d[0],p,s,i);else if(c===3)qme(l,d[0],d[1],p,s,i);else if(c===4)Kme(l,d[0],d[1],d[2],p,s,i);else{let f=If(l,s,i,t.shape,t.dtype);p.set(f)}return u}function Hme(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function qme(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function Kme(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Xme={kernelName:il,backendName:"wasm",kernelFunc:No};function Zme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Qr({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Su({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:d}}),g=No({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var Yme={kernelName:Fo,backendName:"wasm",kernelFunc:Zme};function zh(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var Jme={kernelName:Hs,backendName:"wasm",kernelFunc:zh},Qme=br(qs),z8;function e0e(e){z8=e.wasm.cwrap(qa,null,["number","number","number","number"])}function 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Ige={kernelName:ci,backendName:"wasm",setupFunc:wge,kernelFunc:kge},Sge=!1,Cge=jr(fi,Sge),nC=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(nC||{}),aC;function Tge(e){aC=e.wasm.cwrap(mi,null,["number","array","number","number","array","array","number","number"])}function Nge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return aC(i,u,t.shape.length,Ut[t.dtype],p,c,nC[a],l),o}var Ege={kernelName:mi,backendName:"wasm",kernelFunc:Nge,setupFunc:Tge},Rge=!0,Mge=jr(gi,Rge),Fge=br(qo);function ob(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var sC;function $ge(e){sC=e.wasm.cwrap(Xo,"number",["number","number","number","number","number"])}function Pge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=sC(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=ob(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var _ge={kernelName:Xo,backendName:"wasm",setupFunc:$ge,kernelFunc:Pge},iC;function zge(e){iC=e.wasm.cwrap(Hu,"number",["number","number","number","number","number","bool"])}function Oge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=iC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=ob(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var Dge={kernelName:Hu,backendName:"wasm",setupFunc:zge,kernelFunc:Oge},oC;function Lge(e){oC=e.wasm.cwrap(Zo,"number",["number","number","number","number","number","number"])}function Bge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=oC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=ob(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var Wge={kernelName:Zo,backendName:"wasm",setupFunc:Lge,kernelFunc:Bge},Vge=!1,Uge=jr(Ko,Vge,"bool"),lC;function Gge(e){lC=e.wasm.cwrap(Jo,null,["number","number","number","number","number"])}function jge(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return lC(d,s,i,o,u),l}var Hge={kernelName:Jo,backendName:"wasm",setupFunc:Gge,kernelFunc:jge};function qge(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var Kge={kernelName:Yo,backendName:"wasm",kernelFunc:qge};function Xge(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Vy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Vy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=O8({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var Zge={kernelName:Qo,backendName:"wasm",kernelFunc:Xge},uC;function Yge(e){uC=e.wasm.cwrap(yi,null,["number","array","number","number","array","array","number","number"])}function Jge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(w.sizeFromShape(t.shape)===0)return j8({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return uC(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var dC={kernelName:yi,backendName:"wasm",kernelFunc:Jge,setupFunc:Yge},Qge=!1,e1e=jr(Ai,Qge),pC;function t1e(e){pC=e.wasm.cwrap(xi,null,["number","number","number"])}function r1e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=zh({backend:r,inputs:{x:n},attrs:{dtype:"float32"}}),o=r.dataIdMap.get(u.dataId).id);let d=r.makeOutput(n.shape,"float32"),h=r.dataIdMap.get(d.dataId).id;return pC(o,i,h),l.dtype!=="float32"&&r.disposeData(u.dataId),d}var n1e={kernelName:xi,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e},hC;function a1e(e){hC=e.wasm.cwrap(el,null,["number","number","number","number"])}function s1e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t),f=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;hC(l,y,Ut[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var i1e={kernelName:el,backendName:"wasm",setupFunc:a1e,kernelFunc:s1e},o1e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=Px(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},l1e={kernelName:qu,backendName:"wasm",kernelFunc:o1e},u1e=!0,d1e=jr(Qs,u1e),p1e=br(bi),h1e=br(wi),cC;function c1e(e){cC=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number"])}function f1e(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,f=[d,l,u,c],m=t.dataIdMap.get(a.dataId),g;m.dtype!=="float32"&&(g=zh({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(w.sizeFromShape(a.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return cC(y,d,h,p,c,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var m1e={kernelName:vi,backendName:"wasm",setupFunc:c1e,kernelFunc:f1e},fC;function g1e(e){fC=e.wasm.cwrap(rl,null,["number","array","number","array","number","number"])}function y1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=w.parseAxisParam(s,a.shape);if(a.shape.length===0)return f0({inputs:{x:a},backend:r});let o=r.makeOutput(a.shape,a.dtype),l=r.dataIdMap.get(a.dataId).id,u=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);fC(l,d,i.length,h,a.shape.length,u);let p=Qr({inputs:{x:o},attrs:{shape:a.shape},backend:r});return r.disposeData(o.dataId),p}var A1e={kernelName:rl,backendName:"wasm",kernelFunc:y1e,setupFunc:g1e},mC;function x1e(e){mC=e.wasm.cwrap(gl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function b1e(e){let{inputs:t,backend:r,attrs:n}=e,{image:a}=t,{radians:s,fillValue:i,center:o}=n,l=r.makeOutput(a.shape,a.dtype),u=r.dataIdMap.get(a.dataId).id,d=r.dataIdMap.get(l.dataId).id,[h,p,c,f]=a.shape,[m,g]=N.getImageCenter(o,p,c),y=i===0,A=255,x=typeof i=="number"?[i,i,i,y?0:A]:[...i,A],b=new Uint8Array(new Int32Array(x).buffer);return mC(u,h,p,c,f,s,m,g,b,x.length,d),l}var v1e={kernelName:gl,backendName:"wasm",kernelFunc:b1e,setupFunc:x1e},w1e=br(nl),k1e=br(ki),gC;function I1e(e){gC=e.wasm.cwrap(al,null,["number","number","number","number","number","number","array","number","number"])}function S1e(e){let{backend:t,inputs:r,attrs:n}=e,{indices:a,updates:s}=r,{shape:i}=n,o=t.makeOutput(i,s.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:l,numUpdates:u,sliceSize:d,strides:h,outputSize:p}=s2.calculateShapes(s,a,i),c=t.dataIdMap.get(a.dataId).id,f=t.dataIdMap.get(s.dataId).id,m=new Uint8Array(new Int32Array(h).buffer),g=t.dataIdMap.get(o.dataId).id;return gC(c,f,Ut[s.dtype],l,u,d,m,p,g),o}var C1e={kernelName:al,backendName:"wasm",setupFunc:I1e,kernelFunc:S1e},yC;function T1e(e){yC=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function N1e(e){let{inputs:t,backend:r}=e,{condition:n,t:a,e:s}=t,i=r.dataIdMap.get(n.dataId).id,o=r.dataIdMap.get(a.dataId).id,l=r.dataIdMap.get(s.dataId).id,u=r.makeOutput(a.shape,a.dtype),d=r.dataIdMap.get(u.dataId).id,h=n.shape.length,p=a.shape.length,c=h===0||h>1||p===1?1:w.sizeFromShape(a.shape.slice(1));return yC(i,o,l,c,d),u}var E1e={kernelName:sl,backendName:"wasm",kernelFunc:N1e,setupFunc:T1e},AC;function R1e(e){AC=e.wasm.cwrap(Si,null,["number","number"])}function M1e(e){let{backend:t,inputs:{x:r}}=e,n=t.dataIdMap.get(r.dataId).id,a=t.makeOutput(r.shape,r.dtype),s=t.dataIdMap.get(a.dataId).id;return w.sizeFromShape(a.shape)===0||AC(n,s),a}var F1e={kernelName:"Sigmoid",backendName:"wasm",setupFunc:R1e,kernelFunc:M1e},$1e=br(Ii),xC;function P1e(e){xC=e.wasm.cwrap(Ni,null,["number","number","number","number"])}function _1e(e){let{backend:t,inputs:{logits:r},attrs:{dim:n}}=e,a=t.dataIdMap.get(r.dataId).id,s=t.makeOutput(r.shape,r.dtype),i=t.dataIdMap.get(s.dataId).id,o=r.shape[n],l=w.sizeFromShape(r.shape)/o;return w.sizeFromShape(s.shape)===0||xC(a,i,o,l),s}var z1e={kernelName:Ni,backendName:"wasm",setupFunc:P1e,kernelFunc:_1e};function O1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,paddings:i}=n,o=w.sizeFromShape(s),l=[[0,0]];l.push(...i);for(let g=1+s.length;g<a.shape.length;++g)l.push([0,0]);let u=dC.kernelFunc({inputs:{x:a},backend:r,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,s,o,!1),h=N.getPermuted(d.length,s.length,!1),p=N.getReshapedPermuted(u.shape,s,o,!1),c=Qr({inputs:{x:u},backend:r,attrs:{shape:d}}),f=Su({inputs:{x:c},backend:r,attrs:{perm:h}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:p}});return r.disposeData(u.dataId),r.disposeData(c.dataId),r.disposeData(f.dataId),m}var D1e={kernelName:ll,backendName:"wasm",kernelFunc:O1e},bC;function L1e(e){bC=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function B1e(e){let{backend:t,inputs:r}=e,{indices:n,values:a,denseShape:s,defaultValue:i}=r,o=n.shape[0],l=n.shape[1],u=t.readSync(s.dataId)[0],d=[o+u,l],h=t.dataIdMap.get(n.dataId).id,p=t.dataIdMap.get(a.dataId).id,c=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(d,n.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(d.slice(0,1),a.dtype),y=t.dataIdMap.get(g.dataId).id,A=t.makeOutput([u],"bool"),x=t.dataIdMap.get(A.dataId).id,b=t.makeOutput([o],n.dtype),v=t.dataIdMap.get(b.dataId).id,C=t.makeOutput([4],"int32"),S=t.dataIdMap.get(C.dataId).id,E=bC(h,p,Ut[a.dtype],o,u,l,c,m,y,x,v,S),R=t.readSync(C.dataId),_;switch(R[0]){case 1:{_=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{_=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:_=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:_=""}if(t.disposeData(C.dataId),_)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(A.dataId),t.disposeData(b.dataId),new Error(_);let M=f,I=g;return E!==d[0]&&(M=No({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),I=No({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[M,I,A,b]}var W1e={kernelName:Xp,backendName:"wasm",setupFunc:L1e,kernelFunc:B1e},vC;function V1e(e){vC=e.wasm.cwrap(Qu,null,["number","number","number","number","number","number","number"])}function U1e(e){let{backend:t,inputs:r}=e,{inputIndices:n,inputShape:a,newShape:s}=r;if(n.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
+  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let r=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=r}!t||t.length===this.lastUniformData.length&&t.every((r,n)=>r===this.lastUniformData[n])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,r){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==r)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,r],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=r),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},fme=class extends SS{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let r=e.createBindGroupLayout({entries:t}),n=e.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}},mme=J().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Bv=(e,t)=>{let r=e.limits.maxComputeWorkgroupsPerDimension,n=t.dispatchLayout,a=t.dispatch;if(a.every(i=>i<=r))return a;w.assert(a[0]>r&&n.y===void 0&&n.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let s=Math.ceil(Math.sqrt(a[0]));return s>r?(s=Math.ceil(Math.cbrt(a[0])),w.assert(s<=r,()=>"Total dispatch size exceeds WebGPU maximum."),[s,s,s]):[s,s,1]},CS=class extends Su{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!tb())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new cme(this.device),this.tensorMap=new zp(this,kr()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),J().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return CS.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let r=this.tensorMap.get(e);if(r.refCount--,!t&&r.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:n}=this.tensorMap.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,r){if(r==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()},a=w.sizeFromShape(t)*zy(r);return this.tensorMap.set(n,{dtype:r,values:e,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:1}),n}move(e,t,r,n,a){if(n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s=w.sizeFromShape(r)*zy(n);this.tensorMap.set(e,{dtype:n,values:t,bufferInfo:{byteSize:s,usage:this.defaultGpuBufferUsage()},refCount:a})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new SS),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new fme),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let r=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),J().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),r}convertAndCacheOnCPU(e,t){let r=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),r.values=t,r.values}readSync(e){let t=this.tensorMap.get(e),{values:r}=t;if(r==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return r}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:r}=t;if(r!=null)return this.convertAndCacheOnCPU(e,r);let n;if(t.dtype==="complex64"){let a=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),s=a[0],i=a[1];n=N.mergeRealAndImagArrays(s,i)}else{let a=await this.getBufferData(t);n=rS(a,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}bufferSync(e){let t=this.readSync(e.dataId),r=t;if(e.dtype==="string")try{r=t.map(n=>w.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return We(e.shape,e.dtype,r)}async time(e){let t=this.activeTimers,r=[],n=!1;this.programTimersStack==null?(this.programTimersStack=r,n=!0):this.activeTimers.push(r),this.activeTimers=r,e();let a=w.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),s=w.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(a);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((l,u)=>({name:s[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,r){let n;if(t==="string"&&r!=null&&r.length>0&&w.isString(r[0])){let a=r.map(s=>w.encodeString(s));n=this.write(a,e,t)}else n=this.write(r,e,t);return{dataId:n,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let r=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),n=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(n).set(t.values):new Float32Array(n).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let a={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingDisposalQueue.push(a)}}makeUniforms(e){let t=0,r=[];e.forEach(s=>{s.data.length===0&&(s.data=[1]);let i;switch(s.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${s.data.length}D shape`)}t=Math.ceil(t/i)*i,r.push(t),t+=s.data.length*4});let n=new ArrayBuffer(t);e.forEach((s,i)=>{let o=r[i];s.type==="int32"?new Int32Array(n,o,s.data.length).set(s.data):s.type==="uint32"?new Uint32Array(n,o,s.data.length).set(s.data):new Float32Array(n,o,s.data.length).set(s.data)});let a=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(a,0,n,0,t),{offset:0,size:t,buffer:a}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let a=0;a<e;a++)t.push({binding:a+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let r=this.device.createBindGroupLayout({entries:t}),n=this.device.createPipelineLayout({bindGroupLayouts:[r]});return{bindGroupLayout:r,pipelineLayout:n}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,r,n,a){if(!a){if(a=this.makeTensorInfo(e.outputShape,r),w.sizeFromShape(a.shape)===0){let S=this.tensorMap.get(a.dataId);return S.values=w.getTypedArrayFromDType(a.dtype,0),a}this.uploadToGPU(a.dataId)}e.dispatch=Bv(this.device,e);let s=[{type:"float32",data:[NaN]}],i=t.concat(a).map(S=>S.shape),o="int32";i.map(S=>{s.push({type:o,data:S})});let l=w.computeStrides(a.shape);if(s.push({type:o,data:l}),e.size){let S=w.sizeFromShape(e.outputShape);s.push({type:o,data:[e.isVec4?S/4:S]})}n&&(s=[...s,...n]);let u=this.makeUniforms(s),d=t.map((S,E)=>{if(S.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(S.dataId),{dtype:this.tensorMap.get(S.dataId).dtype,shape:S.shape,name:e.variableNames[E]}}),h=d.map(S=>S.dtype).concat(a.dtype),p=d.map(S=>N.getBroadcastDims(S.shape,a.shape)),c=d.map(S=>w.arraysEqual(S.shape,a.shape)).join("_"),f=p.map(S=>S.join("_")).join(";"),m=AS(e,i,h,f,c),{bindGroupLayout:g,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),A=this.getAndSavePipeline(m,()=>yS(this.device,e,y,d,a)),x=this.activeTimers!=null,b=Khe(this.device,g,t.map(S=>this.tensorToBinding(S)),this.tensorToBinding(a),u);this.ensureCommandEncoderReady();let v=this.getComputePass();x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,0),v.setPipeline(A),v.setBindGroup(0,b),v.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),x&&this.supportTimeQuery&&v.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(S=>{this.commandQueueOwnedIds.add(S.dataId)}),this.commandQueueOwnedIds.add(a.dataId);let C={byteSize:u.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:u.buffer};return this.uniformDisposalQueue.push(C),J().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),x&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),a}runFromPixelsProgram(e,t,r,n,a){e.dispatch=Bv(this.device,e);let s=this.device.createBindGroup({layout:r.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:n},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,s),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(a),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,r,0,16),this.submitQueue(),await r.mapAsync(GPUMapMode.READ);let n=new BigUint64Array(r.getMappedRange()),a=Number(n[1]-n[0]);return r.unmap(),this.bufferManager.releaseBuffer(r,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),a/1e6}shouldExecuteOnCPU(e,t=mme){return J().getBool("WEBGPU_CPU_FORWARD")&&e.every(r=>this.tensorMap.get(r.dataId).bufferInfo.buffer==null&&w.sizeFromShape(r.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},sb=CS;sb.nextDataId=0;var TS={};Le(TS,{WebGPUBackend:()=>sb,webgpu_util:()=>eS});tb()&&Al("webgpu",async()=>{J().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:J().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),r=t.limits,n={},a=t.features.has("timestamp-query");n.requiredLimits={maxComputeWorkgroupStorageSize:r.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:r.maxComputeWorkgroupsPerDimension},a?n.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let s=await t.requestDevice(n);return new sb(s,a)},3);var Ut=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ut||{}),h0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(h0||{}),NS;function gme(e){NS=e.wasm.cwrap(Ts,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function yme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s,bias:i,preluActivationWeights:o}=t;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:d,leakyreluAlpha:h}=n,p=r.dataIdMap.get(a.dataId).id,c=r.dataIdMap.get(s.dataId).id,f=0;if(i!=null){let E=r.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:r.dataIdMap.get(o.dataId).id,g=h0[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?a.shape[2]:a.shape[1],A=u?s.shape[1]:s.shape[2],x=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)),b=r.makeOutput([...x,y,A],a.dtype),v=r.dataIdMap.get(b.dataId).id,C=new Uint8Array(new Int32Array(a.shape).buffer),S=new Uint8Array(new Int32Array(s.shape).buffer);return NS(p,C,a.shape.length,c,S,s.shape.length,l,u,g,f,m,h||0,v),b}var Ame={kernelName:Ts,backendName:"wasm",setupFunc:gme,kernelFunc:yme};function br(e,t){let r;function n(s){r=s.wasm.cwrap(e,null,["number","number","number"])}function a(s){let{backend:i,inputs:{x:o}}=s,l=i.dataIdMap.get(o.dataId).id,u=i.makeOutput(o.shape,t||o.dtype),d=i.dataIdMap.get(u.dataId).id;return w.sizeFromShape(u.shape)===0||r(l,Ut[o.dtype],d),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var xme=br(Mo);function jr(e,t,r){let n;function a(i){n=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:l}=i,{a:u,b:d}=l,h=o.dataIdMap.get(u.dataId).id,p=o.dataIdMap.get(d.dataId).id,c=r!=null?r:u.dtype,f=N.assertAndGetBroadcastShape(u.shape,d.shape),m=o.makeOutput(f,c);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),A=o.dataIdMap.get(m.dataId).id;return n(h,g,u.shape.length,p,y,d.shape.length,Ut[u.dtype],A),m}return{kernelName:e,backendName:"wasm",setupFunc:a,kernelFunc:s}}var bme=!0,vme=jr(Ha,bme),ES;function wme(e){ES=e.wasm.cwrap(Vs,null,["array","number","number","number"])}function kme(e){let{inputs:t,backend:r}=e,n=r.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(n.shape)===0)return n;let a=t.map(o=>r.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(a).buffer),i=r.dataIdMap.get(n.dataId).id;return ES(s,a.length,Ut[n.dtype],i),n}var Ime={kernelName:Vs,backendName:"wasm",setupFunc:wme,kernelFunc:kme};function c0(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype),a=r.typedArrayFromHeap(t);return r.typedArrayFromHeap(n).set(a),n}var Sme={kernelName:ii,backendName:"wasm",kernelFunc:c0},RS;function Cme(e){RS=e.wasm.cwrap(Fi,null,["number","array","number","number","number","array","number"])}function Iu(e){let{inputs:t,backend:r,attrs:n}=e,[a,s]=Nme(t.x.shape,n.perm),i=!0;for(let f=0;f<s.length;f++)s[f]!==f&&(i=!1);let o=Tme(t.x.shape,n.perm),l={dataId:t.x.dataId,shape:a,dtype:t.x.dtype};if(i){let f=c0({inputs:t,backend:r});return f.shape=o,f}let u=r.makeOutput(o,l.dtype),d=r.dataIdMap.get(l.dataId).id,h=r.dataIdMap.get(u.dataId).id,p=new Uint8Array(new Int32Array(s).buffer),c=new Uint8Array(new Int32Array(l.shape).buffer);return RS(d,c,l.shape.length,Ut[l.dtype],h,p,s.length),u}function Tme(e,t){let r=new Array(e.length);for(let n=0;n<r.length;n++)r[n]=e[t[n]];return r}function Nme(e,t){let r=[],n=[];for(let a=0;a<e.length;++a)e[a]!==1&&r.push(e[a]),e[t[a]]!==1&&n.push(t[a]);for(let a=0;a<n.length;++a){let s=-1;for(let i=0;i<n.length;++i)n[i]>=a&&(s===-1||n[s]>n[i])&&(s=i);n[s]=a}return[r,n]}var Eme={kernelName:Fi,backendName:"wasm",kernelFunc:Iu,setupFunc:Cme};function Li(e,t,r){let n=e.shape,a=e.shape.length,s=w.parseAxisParam(t,n),i=s,o=N.getAxesPermutation(i,a),l=null,u=!1;if(o!=null){let d=new Array(a);for(let p=0;p<d.length;p++)d[p]=n[o[p]];i=N.getInnerMostAxes(i.length,a),l=Iu({inputs:{x:e},attrs:{perm:o},backend:r});let h=r.dataIdMap.get(e.dataId).id;r.dataIdMap.get(l.dataId).id!==h&&(u=!0)}return{transposed:l,originalAxes:s,axes:i,inputWasTransposed:u}}var MS;function Rme(e){MS=e.wasm.cwrap(Eu,null,["number, number, number"])}function Mme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("all",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;MS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var Fme={kernelName:Eu,backendName:"wasm",setupFunc:Rme,kernelFunc:Mme},FS;function $me(e){FS=e.wasm.cwrap(Ru,null,["number, number, number"])}function Pme(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=i,{transposed:u,axes:d,originalAxes:h,inputWasTransposed:p}=Li(i,a,t);if(p){let A=t.dataIdMap.get(u.dataId).id;l=u,o=A}let c=l.shape.length;N.assertAxesAreInnerMostDims("any",d,c);let[f,m]=N.computeOutAndReduceShapes(l.shape,d),g=w.sizeFromShape(m),y=t.makeOutput(f,i.dtype);if(w.sizeFromShape(l.shape)!==0){let A=t.dataIdMap.get(y.dataId).id;FS(o,g,A)}if(p&&t.disposeData(u.dataId),s){let A=N.expandShapeToKeepDim(y.shape,h);y.shape=A}return y}var _me={kernelName:Ru,backendName:"wasm",setupFunc:$me,kernelFunc:Pme},$S;function zme(e){$S=e.wasm.cwrap(Us,null,["number","number","number","number","number"])}function Ome(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a}=n,{x:s}=r,i=t.dataIdMap.get(s.dataId).id,o=i,l=s,{transposed:u,axes:d,inputWasTransposed:h}=Li(s,a,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(l=u,o=y)}let p=l.shape.slice(0,-1),c=t.makeOutput(p,"int32"),f=t.dataIdMap.get(c.dataId).id,m=w.sizeFromShape(c.shape),g=l.shape[d[0]];return $S(o,Ut[l.dtype],m,g,f),h&&t.disposeData(u.dataId),c}var Dme={kernelName:Us,backendName:"wasm",kernelFunc:Ome,setupFunc:zme},PS;function Lme(e){PS=e.wasm.cwrap(Gs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bme(e){let{inputs:t,attrs:r,backend:n}=e,a=t.x,s=n.dataIdMap.get(a.dataId).id,{filterSize:i,strides:o,pad:l,dimRoundingMode:u}=r,d=N.computePool2DInfo(a.shape,i,o,1,l,u),h=d.filterHeight,p=d.filterWidth,c=d.padInfo.top,f=d.padInfo.right,m=d.padInfo.bottom,g=d.padInfo.left,y=d.strideHeight,A=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let b=n.makeOutput(d.outShape,"float32"),v=n.dataIdMap.get(b.dataId).id;return PS(s,a.shape[0],a.shape[1],a.shape[2],h,p,c,f,m,g,y,A,x,v),b}var Wme={kernelName:Gs,backendName:"wasm",setupFunc:Lme,kernelFunc:Bme};function Qr(e){let{inputs:t,attrs:r}=e,{x:n}=t,{shape:a}=r,s=w.sizeFromShape(n.shape),i=w.inferFromImplicitShape(a,s);return w.assert(s===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${n.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(n.dataId),{dataId:n.dataId,shape:i,dtype:n.dtype}}var Vme={kernelName:tl,backendName:"wasm",kernelFunc:Qr},_S;function Ume(e){_S=e.wasm.cwrap(js,null,["number","array","number","number","array","number","number","number","number"])}function Gme(e){let{inputs:t,backend:r,attrs:n}=e,{a,b:s}=t,{transposeA:i,transposeB:o}=n;if(a.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=a.shape.length,u=s.shape.length,d=i?a.shape[l-2]:a.shape[l-1],h=o?s.shape[u-1]:s.shape[u-2],p=i?a.shape[l-1]:a.shape[l-2],c=o?s.shape[u-2]:s.shape[u-1],f=a.shape.slice(0,-2),m=s.shape.slice(0,-2),g=w.sizeFromShape(f),y=w.sizeFromShape(m),A=yl.assertAndGetBroadcastShape(a.shape.slice(0,-2),s.shape.slice(0,-2)).concat([p,c]);w.assert(d===h,()=>`Error in matMul: inner shapes (${d}) and (${h}) of Tensors with shapes ${a.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,d,p]:[g,p,d],b=o?[y,c,h]:[y,h,c],v=Qr({inputs:{x:a},backend:r,attrs:{shape:x}}),C=Qr({inputs:{x:s},backend:r,attrs:{shape:b}}),S=r.dataIdMap.get(v.dataId).id,E=r.dataIdMap.get(C.dataId).id,R=i?v.shape[2]:v.shape[1],_=o?C.shape[1]:C.shape[2],M=Math.max(g,y),I=r.makeOutput([M,R,_],v.dtype),O=r.dataIdMap.get(I.dataId).id,z=new Uint8Array(new Int32Array(v.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return _S(S,z,v.shape.length,E,j,C.shape.length,i,o,O),r.disposeData(v.dataId),r.disposeData(C.dataId),I.shape=A,I}var jme={kernelName:js,backendName:"wasm",setupFunc:Ume,kernelFunc:Gme};function No(e){let{inputs:{x:t},attrs:{begin:r,size:n},backend:a}=e,[s,i]=_t.parseSliceParams(t,r,n),o=_t.isSliceContinous(t.shape,s,i),l=a.readSync(t.dataId),u=a.makeOutput(i,t.dtype),d=w.computeStrides(t.shape),h=a.dataIdMap.get(u.dataId);if(o){let f=_t.computeFlatOffset(s,d);return t.dtype==="string"?h.stringBytes=l.slice(f,f+w.sizeFromShape(i)):a.typedArrayFromHeap(u).set(l.subarray(f,f+w.sizeFromShape(i))),u}if(t.dtype==="string"){let f=kf(l,s,i,t.shape,t.dtype);return h.stringBytes=f,u}let p=a.typedArrayFromHeap(u),c=t.shape.length;if(c===2)Hme(l,d[0],p,s,i);else if(c===3)qme(l,d[0],d[1],p,s,i);else if(c===4)Kme(l,d[0],d[1],d[2],p,s,i);else{let f=kf(l,s,i,t.shape,t.dtype);p.set(f)}return u}function Hme(e,t,r,n,a){let s=0,i=n[0],o=n[1],l=i+a[0];for(let u=i;u<l;u++){let d=u*t+o;r.set(e.subarray(d,d+a[1]),s),s+=a[1]}}function qme(e,t,r,n,a,s){let i=0,o=a[0],l=a[1],u=a[2],d=o+s[0],h=l+s[1];for(let p=o;p<d;p++)for(let c=l;c<h;c++){let f=p*t+c*r+u;n.set(e.subarray(f,f+s[2]),i),i+=s[2]}}function Kme(e,t,r,n,a,s,i){let o=0,l=s[0],u=s[1],d=s[2],h=l+i[0],p=u+i[1],c=d+i[2],f=s[3];for(let m=l;m<h;m++)for(let g=u;g<p;g++)for(let y=d;y<c;y++){let A=m*t+g*r+y*n+f;a.set(e.subarray(A,A+i[3]),o),o+=i[3]}}var Xme={kernelName:il,backendName:"wasm",kernelFunc:No};function Zme(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{blockShape:s,crops:i}=n,o=s.reduce((y,A)=>y*A),l=N.getReshaped(a.shape,s,o),u=N.getPermuted(l.length,s.length),d=N.getReshapedPermuted(a.shape,s,o),h=N.getSliceBeginCoords(i,s.length),p=N.getSliceSize(d,i,s.length),c=Qr({inputs:{x:a},backend:r,attrs:{shape:l}}),f=Iu({inputs:{x:c},backend:r,attrs:{perm:u}}),m=Qr({inputs:{x:f},backend:r,attrs:{shape:d}}),g=No({inputs:{x:m},backend:r,attrs:{begin:h,size:p}});return r.disposeData(c.dataId),r.disposeData(f.dataId),r.disposeData(c.dataId),g}var Yme={kernelName:Fo,backendName:"wasm",kernelFunc:Zme};function _h(e){let{inputs:{x:t},attrs:{dtype:r},backend:n}=e,a=n.makeOutput(t.shape,r),s=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(s),a}var Jme={kernelName:Hs,backendName:"wasm",kernelFunc:_h},Qme=br(qs),zS;function e0e(e){zS=e.wasm.cwrap(qa,null,["number","number","number","number"])}function 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Please use 'NHWC'.`);let ee=n.makeOutput(m.outShape,"float32"),Y=n.dataIdMap.get(ee.dataId).id,se=o==null?0:n.dataIdMap.get(o.dataId).id;return KS(y,D,Q,V,A,v,C,b,S,E,R,_,X,M,I,O,z,j,x,g,se,f||0,Y),ee}var W0e={kernelName:Ns,backendName:"wasm",setupFunc:L0e,kernelFunc:B0e},XS;function V0e(e){XS=e.wasm.cwrap(Es,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function U0e(e){let{inputs:t,attrs:r,backend:n}=e,{x:a,filter:s,bias:i,preluActivationWeights:o}=t,{strides:l,pad:u,dilations:d,dataFormat:h,dimRoundingMode:p,activation:c,leakyreluAlpha:f}=r,m=N.computeConv2DInfo(a.shape,s.shape,l,d,u,p,!0),g=h0[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=n.dataIdMap.get(a.dataId).id,A=n.dataIdMap.get(s.dataId).id,x=m.outChannels,b=0;if(i!=null){let Z=n.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);b=Z.id}let v=m.filterHeight,C=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,_=m.padInfo.left,M=m.dilationHeight,I=m.dilationWidth,O=m.strideHeight,z=m.strideWidth,j=m.inChannels,X=m.padInfo.type==="SAME"?1:0,D=m.batchSize,Q=m.inHeight,V=m.inWidth;if(h!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${h}'. 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b=N.expandShapeToKeepDim(x.shape,p);x.shape=b}return u.dtype!=="float32"&&t.disposeData(A.dataId),x}var vge={kernelName:hi,backendName:"wasm",setupFunc:xge,kernelFunc:bge},rC;function wge(e){rC=e.wasm.cwrap(ci,null,["number","number","number","number"])}function kge(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t);if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x)}let f=u.shape.length;N.assertAxesAreInnerMostDims("min",h,f);let[m,g]=N.computeOutAndReduceShapes(u.shape,h),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;rC(l,Ut[i.dtype],y,x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var Ige={kernelName:ci,backendName:"wasm",setupFunc:wge,kernelFunc:kge},Sge=!1,Cge=jr(fi,Sge),nC=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(nC||{}),aC;function Tge(e){aC=e.wasm.cwrap(mi,null,["number","array","number","number","array","array","number","number"])}function Nge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,mode:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]),i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return aC(i,u,t.shape.length,Ut[t.dtype],p,c,nC[a],l),o}var Ege={kernelName:mi,backendName:"wasm",kernelFunc:Nge,setupFunc:Tge},Rge=!0,Mge=jr(gi,Rge),Fge=br(qo);function ib(e,t){let r=new Int32Array(e.wasm.HEAPU8.buffer,t,4),n=r[0],a=r[1],s=r[2],i=r[3];return e.wasm._free(t),{pSelectedIndices:n,selectedSize:a,pSelectedScores:s,pValidOutputs:i}}var sC;function $ge(e){sC=e.wasm.cwrap(Xo,"number",["number","number","number","number","number"])}function Pge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i}=n,{boxes:o,scores:l}=r,u=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(l.dataId).id,h=sC(u,d,s,a,i),{pSelectedIndices:p,selectedSize:c,pSelectedScores:f,pValidOutputs:m}=ib(t,h);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([c],"int32",p)}var _ge={kernelName:Xo,backendName:"wasm",setupFunc:$ge,kernelFunc:Pge},iC;function zge(e){iC=e.wasm.cwrap(ju,"number",["number","number","number","number","number","bool"])}function Oge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=iC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=ib(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([],"int32",g);return[y,A]}var Dge={kernelName:ju,backendName:"wasm",setupFunc:zge,kernelFunc:Oge},oC;function Lge(e){oC=e.wasm.cwrap(Zo,"number",["number","number","number","number","number","number"])}function Bge(e){let{backend:t,inputs:r,attrs:n}=e,{iouThreshold:a,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=n,{boxes:l,scores:u}=r,d=t.dataIdMap.get(l.dataId).id,h=t.dataIdMap.get(u.dataId).id,p=oC(d,h,s,a,i,o),{pSelectedIndices:c,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=ib(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",c),A=t.makeOutput([f],"float32",m);return[y,A]}var Wge={kernelName:Zo,backendName:"wasm",setupFunc:Lge,kernelFunc:Bge},Vge=!1,Uge=jr(Ko,Vge,"bool"),lC;function Gge(e){lC=e.wasm.cwrap(Jo,null,["number","number","number","number","number"])}function jge(e){let{inputs:t,backend:r,attrs:n}=e,{indices:a}=t,{depth:s,onValue:i,offValue:o}=n,l=r.makeOutput([...a.shape,s],"int32"),u=r.dataIdMap.get(l.dataId).id,d=r.dataIdMap.get(a.dataId).id;return lC(d,s,i,o,u),l}var Hge={kernelName:Jo,backendName:"wasm",setupFunc:Gge,kernelFunc:jge};function qge(e){let{inputs:{x:t},backend:r}=e,n=r.makeOutput(t.shape,t.dtype);return r.typedArrayFromHeap(n).fill(1),n}var Kge={kernelName:Yo,backendName:"wasm",kernelFunc:qge};function Xge(e){let{inputs:t,backend:r,attrs:n}=e,{axis:a}=n;if(t.length===1)return Wy({inputs:{input:t[0]},backend:r,attrs:{dim:a}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{w.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],l=t.map(d=>{let h=Wy({inputs:{input:d},backend:r,attrs:{dim:a}});return o.push(h),h}),u=OS({inputs:l,backend:r,attrs:{axis:a}});return o.forEach(d=>r.disposeData(d.dataId)),u}var Zge={kernelName:Qo,backendName:"wasm",kernelFunc:Xge},uC;function Yge(e){uC=e.wasm.cwrap(yi,null,["number","array","number","number","array","array","number","number"])}function Jge(e){let{inputs:{x:t},backend:r,attrs:{paddings:n,constantValue:a}}=e,s=n.map((f,m)=>f[0]+t.shape[m]+f[1]);if(w.sizeFromShape(t.shape)===0)return jS({backend:r,attrs:{shape:s,value:a,dtype:t.dtype}});let i=r.dataIdMap.get(t.dataId).id,o=r.makeOutput(s,t.dtype),l=r.dataIdMap.get(o.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=n.map(f=>f[0]),h=n.map(f=>f[1]),p=new Uint8Array(new Int32Array(d).buffer),c=new Uint8Array(new Int32Array(h).buffer);return uC(i,u,t.shape.length,Ut[t.dtype],p,c,a,l),o}var dC={kernelName:yi,backendName:"wasm",kernelFunc:Jge,setupFunc:Yge},Qge=!1,e1e=jr(Ai,Qge),pC;function t1e(e){pC=e.wasm.cwrap(xi,null,["number","number","number"])}function r1e(e){let{inputs:t,backend:r}=e,{x:n,alpha:a}=t,s=r.dataIdMap.get(n.dataId).id,i=r.dataIdMap.get(a.dataId).id,o=s,l=n,u=l;l.dtype!=="float32"&&(u=_h({backend:r,inputs:{x:n},attrs:{dtype:"float32"}}),o=r.dataIdMap.get(u.dataId).id);let d=r.makeOutput(n.shape,"float32"),h=r.dataIdMap.get(d.dataId).id;return pC(o,i,h),l.dtype!=="float32"&&r.disposeData(u.dataId),d}var n1e={kernelName:xi,backendName:"wasm",setupFunc:t1e,kernelFunc:r1e},hC;function a1e(e){hC=e.wasm.cwrap(el,null,["number","number","number","number"])}function s1e(e){let{backend:t,inputs:r,attrs:n}=e,{axis:a,keepDims:s}=n,{x:i}=r,o=t.dataIdMap.get(i.dataId).id,l=o,u=i,{transposed:d,axes:h,originalAxes:p,inputWasTransposed:c}=Li(i,a,t),f=h;if(c){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(u=d,l=x,f=N.getInnerMostAxes(f.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",f,u.shape.length);let[m,g]=N.computeOutAndReduceShapes(u.shape,f),y=w.sizeFromShape(g),A=t.makeOutput(m,u.dtype);if(w.sizeFromShape(u.shape)!==0){let x=t.dataIdMap.get(A.dataId).id;hC(l,y,Ut[A.dtype],x)}if(c&&t.disposeData(d.dataId),s){let x=N.expandShapeToKeepDim(A.shape,p);A.shape=x}return A}var i1e={kernelName:el,backendName:"wasm",setupFunc:a1e,kernelFunc:s1e},o1e=e=>{let{backend:t,attrs:r}=e,{start:n,stop:a,step:s,dtype:i}=r,o=$x(n,a,s,i),l=t.makeOutput([o.length],i);return t.typedArrayFromHeap(l).set(o),l},l1e={kernelName:Hu,backendName:"wasm",kernelFunc:o1e},u1e=!0,d1e=jr(Qs,u1e),p1e=br(bi),h1e=br(wi),cC;function c1e(e){cC=e.wasm.cwrap(vi,null,["number","number","number","number","number","number","number","number","number","number"])}function f1e(e){let{backend:t,inputs:r,attrs:n}=e,{images:a}=r,{alignCorners:s,halfPixelCenters:i,size:o}=n,[l,u]=o,[d,h,p,c]=a.shape,f=[d,l,u,c],m=t.dataIdMap.get(a.dataId),g;m.dtype!=="float32"&&(g=_h({backend:t,inputs:{x:a},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let y=m.id,A=t.makeOutput(f,"float32");if(w.sizeFromShape(a.shape)===0)return A;let x=t.dataIdMap.get(A.dataId).id;return cC(y,d,h,p,c,l,u,s?1:0,i?1:0,x),g!=null&&t.disposeData(g.dataId),A}var m1e={kernelName:vi,backendName:"wasm",setupFunc:c1e,kernelFunc:f1e},fC;function g1e(e){fC=e.wasm.cwrap(rl,null,["number","array","number","array","number","number"])}function y1e(e){let{inputs:t,backend:r,attrs:n}=e,{x:a}=t,{dims:s}=n,i=w.parseAxisParam(s,a.shape);if(a.shape.length===0)return c0({inputs:{x:a},backend:r});let o=r.makeOutput(a.shape,a.dtype),l=r.dataIdMap.get(a.dataId).id,u=r.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),h=new Uint8Array(new Int32Array(a.shape).buffer);fC(l,d,i.length,h,a.shape.length,u);let p=Qr({inputs:{x:o},attrs:{shape:a.shape},backend:r});return r.disposeData(o.dataId),p}var A1e={kernelName:rl,backendName:"wasm",kernelFunc:y1e,setupFunc:g1e},mC;function x1e(e){mC=e.wasm.cwrap(gl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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pb(e,t){let r=t||Hr(e.width,e.height);return r.getContext("2d").drawImage(e,0,0),r}async function Sd(e,t,r=!0){if(!e)return t.debug&&ie("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof rt)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ce.Canvas!="undefined"&&e instanceof ce.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type is not recognized");if(e instanceof rt){let n=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)n=Ht(e,0);else if(e.shape[2]===4){let a=vl(e,[0,0,0],[-1,-1,3]);n=Ht(a,0),re(a)}}else e.shape.length===4&&(e.shape[3]===3?n=Or(e):e.shape[3]===4&&(n=wo(e,[0,0,0,0],[-1,-1,-1,3])));if(n==null||n.shape.length!==4||n.shape[0]!==1||n.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape}`);if(n.dtype==="int32"){let a=me(n,"float32");re(n),n=a}return{tensor:n,canvas:t.filter.return?or:null}}else{if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&ie("input stream is not ready"),{tensor:null,canvas:ut};let n=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,a=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!n||!a)return t.debug&&ie("cannot determine input dimensions"),{tensor:null,canvas:ut};let s=n,i=a;if(s>g0&&(s=g0,i=Math.trunc(s*a/n)),i>g0&&(i=g0,s=Math.trunc(i*n/a)),(t.filter.width||0)>0?s=t.filter.width:(t.filter.height||0)>0&&(s=n*((t.filter.height||0)/a)),(t.filter.height||0)>0?i=t.filter.height:(t.filter.width||0)>0&&(i=a*((t.filter.width||0)/n)),!s||!i)throw new Error("input error: cannot determine dimension");(!ut||(ut==null?void 0:ut.width)!==s||(ut==null?void 0:ut.height)!==i)&&(ut=Hr(s,i));let o=ut.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?o.putImageData(e,0,0):t.filter.flip&&typeof o.translate!="undefined"?(o.translate(n,0),o.scale(-1,1),o.drawImage(e,0,0,n,a,0,0,ut==null?void 0:ut.width,ut==null?void 0:ut.height),o.setTransform(1,0,0,1,0,0)):o.drawImage(e,0,0,n,a,0,0,ut==null?void 0:ut.width,ut==null?void 0:ut.height),(!or||ut.width!==or.width||(ut==null?void 0:ut.height)!==(or==null?void 0:or.height))&&(or=Hr(ut.width,ut.height)),t.filter.enabled&&ce.webgl.supported){if(Tt||(Tt=ce.browser?new BC:null),ce.filter=!!Tt,!Tt||!Tt.add)return t.debug&&ie("input process error: cannot initialize filters"),{tensor:null,canvas:ut};Tt.reset(),t.filter.brightness!==0&&Tt.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Tt.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Tt.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Tt.add("blur",t.filter.blur),t.filter.saturation!==0&&Tt.add("saturation",t.filter.saturation),t.filter.hue!==0&&Tt.add("hue",t.filter.hue),t.filter.negative&&Tt.add("negative"),t.filter.sepia&&Tt.add("sepia"),t.filter.vintage&&Tt.add("brownie"),t.filter.sepia&&Tt.add("sepia"),t.filter.kodachrome&&Tt.add("kodachrome"),t.filter.technicolor&&Tt.add("technicolor"),t.filter.polaroid&&Tt.add("polaroid"),t.filter.pixelate!==0&&Tt.add("pixelate",t.filter.pixelate),Tt.get()>0?or=Tt.apply(ut):or=Tt.draw(ut)}else pb(ut,or),Tt&&(Tt=null),ce.filter=!!Tt;if(!r)return{tensor:null,canvas:or};if(!or)throw new Error("canvas error: cannot create output");let l,u=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(ce.browser&&$n)l=$n?$n.fromPixels(e):null;else{u=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=ct(p,[e.height,e.width,u],"int32")}else if((!Id||or.width!==Id.width||or.height!==Id.height)&&(Id=Hr(or.width,or.height)),$n&&ce.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=$n.fromPixels(or):(Id=pb(or),l=$n.fromPixels(Id));else{let f=pb(or).getContext("2d").getImageData(0,0,s,i);u=f.data.length/s/i;let m=new Uint8Array(f.data.buffer);l=ct(m,[s,i,u])}if(u===4){let p=vl(l,[0,0,0],[-1,-1,3]);re(l),l=p}if(!l)throw new Error("input error: cannot create tensor");let d=me(l,"float32"),h=t.filter.equalization?await m0(d):Ht(d,0);return re([l,d]),{tensor:h,canvas:t.filter.return?or:null}}}async function WC(e,t){let r=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return r;if(!ts.inputTensor)ts.inputTensor=Or(t);else if(ts.inputTensor.shape[1]!==t.shape[1]||ts.inputTensor.shape[2]!==t.shape[2])re(ts.inputTensor),ts.inputTensor=Or(t);else{let n={};n.diff=he(t,ts.inputTensor),n.squared=L(n.diff,n.diff),n.sum=ke(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;re([ts.inputTensor,n.diff,n.squared,n.sum]),ts.inputTensor=Or(t),r=s<=(e.cacheSensitivity||0)}return r}async function VC(e,t,r){let n={};if(!t||!r||t.shape.length!==4||t.shape.length!==r.shape.length)return e.debug||ie("invalid input tensor or tensor shapes do not match:",t.shape,r.shape),0;if(t.shape[0]!==1||r.shape[0]!==1||t.shape[3]!==3||r.shape[3]!==3)return e.debug||ie("input tensors must be of shape [1, height, width, 3]:",t.shape,r.shape),0;n.input1=Or(t),n.input2=t.shape[1]!==r.shape[1]||t.shape[2]!==r.shape[2]?Ie.resizeBilinear(r,[t.shape[1],t.shape[2]]):Or(r),n.diff=he(n.input1,n.input2),n.squared=L(n.diff,n.diff),n.sum=ke(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return re([n.input1,n.input2,n.diff,n.squared,n.sum]),s}var UC=class{constructor(){fe(this,"browser");fe(this,"node");fe(this,"worker");fe(this,"platform","");fe(this,"agent","");fe(this,"backends",[]);fe(this,"initial");fe(this,"filter");fe(this,"tfjs");fe(this,"offscreen");fe(this,"perfadd",!1);fe(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});fe(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});fe(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});fe(this,"cpu",{model:void 0,flags:[]});fe(this,"kernels",[]);fe(this,"Canvas");fe(this,"Image");fe(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:Oh["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let r=t[0].match(/\(([^()]+)\)/g);this.platform=r&&r[0]?r[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/  /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(kr().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&pn()==="wasm"&&(this.wasm.simd=await J().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Hr(100,100),r=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof r!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(pn()==="webgl"||pn()==="humangl")){let n=ga().gpgpu!=="undefined"?await ga().getGPGPUContext().gl:null;n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.renderer=n.getParameter(n.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name)}catch(n){this.webgpu.supported=!1}try{this.kernels=Na(pn()).map(n=>n.kernelName.toLowerCase())}catch(n){}}async updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},ce=new UC;var rs={cacheModels:!1,verbose:!0,debug:!1,modelBasePath:""};async function Vye(e,t){return rs.debug&&ie("load model fetch:",e,t),fetch(e,t)}function GC(e){rs.cacheModels=e.cacheModels,rs.verbose=e.debug,rs.modelBasePath=e.modelBasePath}async function je(e){let t=l3(rs.modelBasePath,e||""),r=t.split("/"),n="indexeddb://"+r[r.length-1].replace(".json",""),a=await Ir.listModels(),s=rs.cacheModels&&Object.keys(a).includes(n),i=typeof fetch=="undefined"?{}:{fetchFunc:(l,u)=>Vye(l,u)},o=new r0(s?n:t,i);try{o.findIOHandler(),rs.debug&&ie("model load handler:",o.handler);let l=await o.handler.load();o.loadSync(l),rs.verbose&&ie("load model:",o.modelUrl)}catch(l){ie("error loading model:",t,l)}if(rs.cacheModels&&!s)try{let l=await o.save(n);ie("model saved:",n,l)}catch(l){ie("error saving model:",t,l)}return o}var hb="2.6.3";var Hn,cb=[],jye=["white","black","asian","indian","other"],Hye=[15,23,28,35.5,45.5,55.5,65],jC=0,HC=0,fb=Number.MAX_SAFE_INTEGER;async function qC(e){return ce.initial&&(Hn=null),Hn?e.debug&&ie("cached model:",Hn.modelUrl):Hn=await je(e.face.gear),Hn}async function mb(e,t,r,n){var i,o;if(!Hn)return{age:0,gender:"unknown",genderScore:0,race:[]};let a=fb<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>oe()-HC;return t.skipAllowed&&s&&a&&jC===n&&cb[r]?(fb++,cb[r]):(fb=0,new Promise(async l=>{var y,A;if(!(Hn==null?void 0:Hn.inputs[0].shape))return;let u={},d=[[0,.1,.9,.9]];u.resize=Ie.cropAndResize(e,d,[0],[Hn.inputs[0].shape[2],Hn.inputs[0].shape[1]]);let h={age:0,gender:"unknown",genderScore:0,race:[]};((y=t.face.gear)==null?void 0:y.enabled)&&([u.age,u.gender,u.race]=Hn.execute(u.resize,["age_output","gender_output","race_output"]));let p=await u.gender.data();h.gender=p[0]>p[1]?"male":"female",h.genderScore=Math.round(100*(p[0]>p[1]?p[0]:p[1]))/100;let c=await u.race.data();for(let x=0;x<c.length;x++)c[x]>(((A=t.face.gear)==null?void 0:A.minConfidence)||.2)&&h.race.push({score:Math.round(100*c[x])/100,race:jye[x]});h.race.sort((x,b)=>b.score-x.score);let m=Array.from(await u.age.data()).map((x,b)=>[Hye[b],x]).sort((x,b)=>b[1]-x[1]),g=m[0][0];for(let x=1;x<m.length;x++)g+=m[x][1]*(m[x][0]-g);h.age=Math.round(10*g)/10,Object.keys(u).forEach(x=>re(u[x])),cb[r]=h,jC=n,HC=oe(),l(h)}))}var Qe={tf255:255,tf1:1,tf2:2,tf05:.5,tf127:127.5,rgb:[.2989,.587,.114]};function XC(){Qe.tf255=Se(255,"float32"),Qe.tf1=Se(1,"float32"),Qe.tf2=Se(2,"float32"),Qe.tf05=Se(.5,"float32"),Qe.tf127=Se(127.5,"float32"),Qe.rgb=St([.2989,.587,.114],"float32")}var cn,y0=[],ZC=0,YC=0,gb=Number.MAX_SAFE_INTEGER;async function JC(e){return ce.initial&&(cn=null),cn?e.debug&&ie("cached model:",cn.modelUrl):cn=await je(e.face.ssrnet.modelPathAge),cn}async function yb(e,t,r,n){var i,o,l,u;if(!cn)return{age:0};let a=gb<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>oe()-YC;return t.skipAllowed&&a&&s&&ZC===n&&((l=y0[r])==null?void 0:l.age)&&((u=y0[r])==null?void 0:u.age)>0?(gb++,y0[r]):(gb=0,new Promise(async d=>{if(!(cn==null?void 0:cn.inputs)||!cn.inputs[0]||!cn.inputs[0].shape)return;let h={};h.resize=Ie.resizeBilinear(e,[cn.inputs[0].shape[2],cn.inputs[0].shape[1]],!1),h.enhance=L(h.resize,Qe.tf255);let p={age:0};if(t.face.ssrnet.enabled&&(h.age=cn.execute(h.enhance)),h.age){let c=await h.age.data();p.age=Math.trunc(10*c[0])/10}Object.keys(h).forEach(c=>re(h[c])),y0[r]=p,ZC=n,YC=oe(),d(p)}))}var qn,A0=[],eT=0,tT=0,Ab=Number.MAX_SAFE_INTEGER,xb=[.2989,.587,.114];async function rT(e){return ce.initial&&(qn=null),qn?e.debug&&ie("cached model:",qn.modelUrl):qn=await je(e.face.ssrnet.modelPathGender),qn}async function bb(e,t,r,n){var i,o,l,u;if(!qn)return{gender:"unknown",genderScore:0};let a=Ab<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>oe()-tT;return t.skipAllowed&&a&&s&&eT===n&&((l=A0[r])==null?void 0:l.gender)&&((u=A0[r])==null?void 0:u.genderScore)>0?(Ab++,A0[r]):(Ab=0,new Promise(async d=>{if(!(qn==null?void 0:qn.inputs[0].shape))return;let h={};h.resize=Ie.resizeBilinear(e,[qn.inputs[0].shape[2],qn.inputs[0].shape[1]],!1),h.enhance=K(()=>{let[f,m,g]=Kt(h.resize,3,3),y=L(f,xb[0]),A=L(m,xb[1]),x=L(g,xb[2]),b=om([y,A,x]);return L(he(b,Qe.tf05),2)});let p={gender:"unknown",genderScore:0};t.face.ssrnet.enabled&&(h.gender=qn.execute(h.enhance));let c=await h.gender.data();p.gender=c[0]>c[1]?"female":"male",p.genderScore=c[0]>c[1]?Math.trunc(100*c[0])/100:Math.trunc(100*c[1])/100,Object.keys(h).forEach(f=>re(h[f])),A0[r]=p,eT=n,tT=oe(),d(p)}))}var Tr,x0=[],vb=Number.MAX_SAFE_INTEGER,aT=0,sT=0;async function iT(e){var t;return ce.initial&&(Tr=null),Tr?e.debug&&ie("cached model:",Tr.modelUrl):Tr=await je((t=e.face.antispoof)==null?void 0:t.modelPath),Tr}async function wb(e,t,r,n){var i,o;if(!Tr)return 0;let a=(((i=t.face.antispoof)==null?void 0:i.skipTime)||0)>oe()-sT,s=vb<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&aT===n&&x0[r]?(vb++,x0[r]):(vb=0,new Promise(async l=>{let 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Kye=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Xye=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Zye=[33,133,362,263,1,78,308],VAe=Kye.map(e=>Bh[e]),UAe=Xye.map(e=>Bh[e]),GAe=Zye.map(e=>Bh[e]);var Cd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],b0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],Nb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Eb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],dT=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},Cb=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Qe.tf255);return re(i),o},v0=(e,t)=>{let r=b0(e),n=Cd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},w0=e=>{let t=b0(e),r=Cd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},pT=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Tb=[[1,0,0],[0,1,0],[0,0,1]],Yye=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Jye=(e,t)=>Yye(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var lT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Rl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},Qye=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},uT=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Rl(e[a],Qye(t,s)))}return r},hT=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=lT(t[0],t[1]),i=uT(s,a),o=lT(-t[0],-t[1]);return uT(i,o)},e2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Rl(t[0],r),-Rl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},t2e=(e,t)=>[Rl(e,t[0]),Rl(e,t[1])];function cT(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function fT(e,t,r,n,a){let s=Cd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?hT(r,[0,0]):Tb,u=o?i.map(c=>[...t2e(c,l),c[2]]):i,d=o?e2e(n):Tb,h=b0(t),p=[Rl(h,d[0]),Rl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function mT(e,t,r,n){let a=t.landmarks.length>=kb.count?kb.symmetryLine:Lh.symmetryLine,s=0,i=Tb,o;if(e&&ce.kernels.includes("rotatewithoffset"))if(s=Jye(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=b0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=hT(-s,u),o=Cb(t,h,[n,n]),re(h)}else o=Cb(t,r,[n,n]);else o=Cb(t,r,[n,n]);return[s,i,o]}var r2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},gT=(e,t)=>{let r=r2e(e),n=Cd(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var yT=6,n2e=1.2,_a,AT=null,Bi=0,Wh=null,k0=()=>Bi;async function xT(e){var t;return ce.initial&&(_a=null),_a?e.debug&&ie("cached model:",_a.modelUrl):_a=await je((t=e.face.detector)==null?void 0:t.modelPath),Bi=_a.inputs[0].shape?_a.inputs[0].shape[2]:0,Wh=Se(Bi,"int32"),AT=oa(cT(Bi)),_a}function a2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,AT),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Wh),t.centersNormalized=pe(t.centers,Wh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Wh),t.endNormalized=L(t.ends,Wh);let r=rd([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function bT(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Bi,Bi]),r.div=pe(r.resized,Qe.tf127),r.normalized=he(r.div,Qe.tf05);let n=_a==null?void 0:_a.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else r.batch=et(n);re(n),r.boxes=a2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Sr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],yT-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=G(c.squeeze,[yT,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=dT(m,[(e.shape[2]||0)/Bi,(e.shape[1]||0)/Bi]),y=v0(g,t.face.scale||n2e),A=w0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var I0={};ep(I0,{connected:()=>Fb,kpt:()=>Mb});var Mb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Fb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var wT=224,s2e,i2e=5,S0=[8,16,32,32,32];async function kT(){let e=[],t=0;for(;t<i2e;){let r=0,n=t;for(;n<S0.length&&S0[n]===S0[t];)r+=2,n++;let a=S0[t],s=Math.ceil(wT/a),i=Math.ceil(wT/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}s2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function ns(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function IT(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function C0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var TT={initial:!0},fn={detector:null,landmarks:null},Td={detector:[224,224],landmarks:[256,256]},$b=Number.MAX_SAFE_INTEGER,l2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},N0=null,Vh,Wi=[[0,0],[0,0],[0,0],[0,0]],ST=0,CT=e=>1-1/(1+Math.exp(e));async function NT(e){if(TT.initial&&(fn.detector=null),!fn.detector&&e.body.detector&&e.body.detector.modelPath){fn.detector=await je(e.body.detector.modelPath);let t=Object.values(fn.detector.modelSignature.inputs);Td.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Td.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&fn.detector&&ie("cached model:",fn.detector.modelUrl);return await kT(),fn.detector}async function ET(e){if(TT.initial&&(fn.landmarks=null),fn.landmarks)e.debug&&ie("cached model:",fn.landmarks.modelUrl);else{fn.landmarks=await je(e.body.modelPath);let t=Object.values(fn.landmarks.modelSignature.inputs);Td.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Td.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return fn.landmarks}async function u2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Vh&&(r.cropped=Ie.cropAndResize(e,[Vh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Wi=[[0,0],a,s,[0,0]],r.pad=Vn(r.cropped||e,Wi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function d2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Wi[2][0]+Wi[2][1])/t[0]-Wi[2][0]),Math.trunc(r.position[1]*(t[1]+Wi[1][0]+Wi[1][1])/t[1]-Wi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Vh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Vh[1],r.positionRaw[1]+Vh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function p2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function h2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=fn.landmarks)==null?void 0:f.execute(e,l2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=CT(s[l*m+3]),y=CT(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Td.landmarks[0],s[l*m+1]/Td.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:Mb[m],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;p2e(o);let u=d2e(o,r),d=u.map(m=>m.position),h=ns(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Fb)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function Pb(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-ST,a=$b<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&N0!==null)$b++;else{let s={};s.landmarks=await u2e(e,256),N0=await h2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),ST=oe(),$b=0}return N0?[N0]:[]}var Nd=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var as,Ml=0,_b=[],MT=0,zb=Number.MAX_SAFE_INTEGER;async function FT(e){if(ce.initial&&(as=null),as)e.debug&&ie("cached model:",as.modelUrl);else{as=await je(e.object.modelPath);let t=Object.values(as.modelSignature.inputs);Ml=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return as}async function c2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Kt(n.squeeze,6,1);n.stack=sr([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Nd[h].label,[c,f]=[s[0][u][0]/Ml,s[0][u][1]/Ml],m=[c,f,s[0][u][2]/Ml-c,s[0][u][3]/Ml-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function Ob(e,t){let r=(t.object.skipTime||0)>oe()-MT,n=zb<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&_b.length>0?(zb++,_b):(zb=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ml,Ml]),o=t.object.enabled?as==null?void 0:as.execute(i,["tower_0/detections"]):null;MT=oe(),re(i);let l=await c2e(o,s,t);_b=l,a(l)}))}var E0={};ep(E0,{connected:()=>Lb,kpt:()=>Db});var Db=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Lb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Nr,PT=0,qr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Bb=Number.MAX_SAFE_INTEGER;async function _T(e){return ce.initial&&(Nr=null),Nr?e.debug&&ie("cached model:",Nr.modelUrl):Nr=await je(e.body.modelPath),Nr}async function f2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=fr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Cn(a,0),l=sd(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function Wb(e,t){let r=(t.body.skipTime||0)>oe()-PT,n=Bb<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(qr.keypoints).length>0?(Bb++,[qr]):(Bb=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Nr==null?void 0:Nr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Nr.inputs[0].shape[2],Nr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return he(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Nr==null?void 0:Nr.execute(s)),PT=oe(),re(s),i){qr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await f2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&qr.keypoints.push({score:Math.round(100*y)/100,part:Db[f],positionRaw:[m/Nr.inputs[0].shape[2],g/Nr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Nr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Nr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}qr.score=qr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=qr.keypoints.map(p=>p.position[0]),l=qr.keypoints.map(p=>p.position[1]);qr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=qr.keypoints.map(p=>p.positionRaw[0]),d=qr.keypoints.map(p=>p.positionRaw[1]);qr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(Lb)){let f=[];for(let m=0;m<c.length-1;m++){let g=qr.keypoints.find(A=>A.part===c[m]),y=qr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}qr.annotations[p]=f}a([qr])}))}var m2e=["angry","disgust","fear","happy","sad","surprise","neutral"],_n,R0=[],OT=0,DT=0,Vb=Number.MAX_SAFE_INTEGER;async function LT(e){var t;return ce.initial&&(_n=null),_n?e.debug&&ie("cached model:",_n.modelUrl):_n=await je((t=e.face.emotion)==null?void 0:t.modelPath),_n}async function Ub(e,t,r,n){var i,o;if(!_n)return[];let a=Vb<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-DT;return t.skipAllowed&&s&&a&&OT===n&&R0[r]&&R0[r].length>0?(Vb++,R0[r]):(Vb=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)==null?void 0:d.enabled){let p={},c=(_n==null?void 0:_n.inputs[0].shape)?_n.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=_n==null?void 0:_n.execute(p.grayscaleMul),DT=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:m2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}R0[r]=u,OT=n,l(u)}))}var mn,Gb=[],WT=0,VT=0,UT=Number.MAX_SAFE_INTEGER;async function GT(e){return ce.initial&&(mn=null),mn?e.debug&&ie("cached model:",mn.modelUrl):mn=await je(e.face.mobilefacenet.modelPath),mn}async function jb(e,t,r,n){var i,o;if(!mn)return[];let a=UT<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-VT;return t.skipAllowed&&s&&a&&WT===n&&Gb[r]?(UT++,Gb[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(mn==null?void 0:mn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[mn.inputs[0].shape[2],mn.inputs[0].shape[1]],!1),h.data=mn==null?void 0:mn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}Gb[r]=u,WT=n,VT=oe(),l(u)})}var ss,Vi=0,g2e=2.3,Hb=Kn.leftEyeLower0,qb=Kn.rightEyeLower0,Ed={leftBounds:[Hb[0],Hb[Hb.length-1]],rightBounds:[qb[0],qb[qb.length-1]]},Rd={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function XT(e){var t;return ce.initial&&(ss=null),ss?e.debug&&ie("cached model:",ss.modelUrl):ss=await je((t=e.face.iris)==null?void 0:t.modelPath),Vi=ss.inputs[0].shape?ss.inputs[0].shape[2]:0,Vi===-1&&(Vi=64),ss}function M0(e,t,r,n){for(let a=0;a<Ib.length;a++){let{key:s,indices:i}=Ib[a],o=Kn[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var y2e=e=>{let t=e[Ed.leftBounds[0]][2],r=e[Ed.rightBounds[0]][2];return t-r},HT=(e,t,r,n,a,s=!1)=>{let i=w0(v0(pT([e[r],e[n]]),g2e)),o=Cd(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[Vi,Vi]);if(s&&ce.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},qT=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Rd.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/Vi:i/Vi)*r[0]+t.startPoint[0],o/Vi*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Rd.index)}},KT=(e,t,r)=>{let n=e[Kn[`${r}EyeUpper0`][Rd.upperCenter]][2],a=e[Kn[`${r}EyeLower0`][Rd.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function ZT(e,t,r,n){if(!ss)return r.debug&&ie("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=HT(e,t,Ed.leftBounds[0],Ed.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=HT(e,t,Ed.rightBounds[0],Ed.rightBounds[1],n,!0),d=kt([i,u]);re(i),re(u);let h=ss.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Rd.numCoordinates*3),{rawCoords:f,iris:m}=qT(c,a,s,!0),g=p.slice(Rd.numCoordinates*3),{rawCoords:y,iris:A}=qT(g,o,l),x=y2e(e);Math.abs(x)<30?(M0(e,f,"left",null),M0(e,y,"right",null)):x<1?M0(e,f,"left",["EyeUpper0","EyeLower0"]):M0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=KT(e,m,"left"),v=KT(e,A,"right");return e.concat(b).concat(v)}var za={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},is=null,Md=0;async function JT(e,t){var o,l,u,d,h,p,c,f,m;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-za.timestamp,n=za.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||za.boxes.length===0?(za.boxes=await bT(e,t),za.timestamp=oe(),za.skipped=0):za.skipped++;let a=[],s=[],i=0;for(let g=0;g<za.boxes.length;g++){let y=za.boxes[g],A=0,x,b={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,b.tensor]=mT((u=t.face.detector)==null?void 0:u.rotation,y,e,((d=t.face.mesh)==null?void 0:d.enabled)?Md:k0()),(h=t==null?void 0:t.filter)==null?void 0:h.equalization){let v=await m0(b.tensor);re(b.tensor),b.tensor=v}if(b.boxScore=Math.round(100*y.confidence)/100,(p=t.face.mesh)==null?void 0:p.enabled)if(!is)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let[v,C,S]=is.execute(b.tensor),E=await C.data();b.faceScore=Math.round(100*E[0])/100;let R=G(S,[-1,3]),_=await R.array();if(re([S,R,C,v]),b.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))y.confidence=b.faceScore;else{((f=t.face.iris)==null?void 0:f.enabled)&&(_=await ZT(_,b.tensor,t,Md)),b.mesh=fT(_,y,A,x,Md),b.meshRaw=b.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Md]);for(let I of Object.keys(Kn))b.annotations[I]=Kn[I].map(O=>b.mesh[O]);b.score=b.faceScore;let M={...gT(b.mesh,y),confidence:y.confidence,landmarks:y.landmarks};b.box=Nb(M,e),b.boxRaw=Eb(M,e),s.push(M)}}else{b.box=Nb(y,e),b.boxRaw=Eb(y,e),b.score=b.boxScore,b.mesh=y.landmarks.map(v=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*v[0]/k0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*v[1]/k0()]),b.meshRaw=b.mesh.map(v=>[v[0]/(e.shape[2]||0),v[1]/(e.shape[1]||0),(v[2]||0)/Md]);for(let v of Object.keys(Lh))b.annotations[v]=[b.mesh[Lh[v]]]}b.score>(((m=t.face.detector)==null?void 0:m.minConfidence)||1)?a.push(b):re(b.tensor)}return za.boxes=s,a}async function QT(e){var t;return ce.initial&&(is=null),is?e.debug&&ie("cached model:",is.modelUrl):is=await je((t=e.face.mesh)==null?void 0:t.modelPath),Md=is.inputs[0].shape?is.inputs[0].shape[2]:0,is}var e9=El,t9=Bh;var gn,F0=[],r9=0,n9=0,Xb=Number.MAX_SAFE_INTEGER;async function a9(e){var t;return ce.initial&&(gn=null),gn?e.debug&&ie("cached model:",gn.modelUrl):gn=await je((t=e.face.description)==null?void 0:t.modelPath),gn}function Zb(e){let t=e.image||e.tensor||e;if(!(gn==null?void 0:gn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[gn.inputs[0].shape[2],gn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function Yb(e,t,r,n){var i,o,l,u;if(!gn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=Xb<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-r9;return t.skipAllowed&&a&&s&&n9===n&&((l=F0[r])==null?void 0:l.age)&&((u=F0[r])==null?void 0:u.age)>0?(Xb++,F0[r]):(Xb=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)==null?void 0:p.enabled){let f=Zb(e),m=gn==null?void 0:gn.execute(f);r9=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Cn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let C=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(C[b-1]>C[b+1]?10*b-100*C[b-1]:10*b+100*C[b+1])/10;let S=m.find(R=>R.shape[1]===1024),E=S?await S.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}F0[r]=h,n9=n,d(h)}))}function $0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Uh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function o9(e,t,r){let n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function l9(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function P0(e,t=1.5){let r=Uh(e),n=$0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function _0(e){let t=Uh(e),r=$0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function A2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function u9(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return A2e(r)}var s9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ui(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function x2e(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function i9(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ui(e[a],x2e(t,s)))}return r}function Qb(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=s9(t[0],t[1]),i=i9(s,a),o=s9(-t[0],-t[1]);return i9(i,o)}function d9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ui(t[0],r),-Ui(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function e5(e,t){return[Ui(e,t[0]),Ui(e,t[1])]}var 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=he(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Sr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=l9(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var w2e=5,c9=1.65,f9=[0,5,9,13,17,1,2],k2e=0,I2e=2,m9=0,r5=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>e5([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return P0(_0(a),w2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=P0(_0(r),c9);n.palmLandmarks=[];for(let a=0;a<f9.length;a++)n.palmLandmarks.push(t[f9[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=$0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=Qb(n,[0,0]),u=o.map(c=>[...e5(c,l),c[2]]),d=d9(a),h=[...Uh(r),1],p=[Ui(h,d[0]),Ui(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-m9,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?u9(u.palmLandmarks[k2e],u.palmLandmarks[I2e]):0,h=Uh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&ce.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=Qb(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=o9(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);m9=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),C=await v.array();re(x),re(v);let S=this.transformRawCoords(C,m,d,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=P0(_0(u),c9),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Kr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Kr.nameMapping[e],getPoints:e=>Kr.pointsMapping[e]},Gi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Gi.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},Fl=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:Aa,index:os,middle:ls,ring:$l,pinky:Pl}=Kr,{none:xa,half:C2e,full:ba}=Gi,{verticalUp:Fd,verticalDown:$xe,horizontalLeft:n5,horizontalRight:T2e,diagonalUpRight:N2e,diagonalUpLeft:$d,diagonalDownRight:Pxe,diagonalDownLeft:_xe}=Dt,ji=new Fl("thumbs up");ji.curl(Aa,xa,1);ji.direction(Aa,Fd,1);ji.direction(Aa,$d,.25);ji.direction(Aa,N2e,.25);for(let e of[Kr.index,Kr.middle,Kr.ring,Kr.pinky])ji.curl(e,ba,1),ji.direction(e,n5,1),ji.direction(e,T2e,1);var Yt=new Fl("victory");Yt.curl(Aa,C2e,.5);Yt.curl(Aa,xa,.5);Yt.direction(Aa,Fd,1);Yt.direction(Aa,$d,1);Yt.curl(os,xa,1);Yt.direction(os,Fd,.75);Yt.direction(os,$d,1);Yt.curl(ls,xa,1);Yt.direction(ls,Fd,1);Yt.direction(ls,$d,.75);Yt.curl($l,ba,1);Yt.direction($l,Fd,.2);Yt.direction($l,$d,1);Yt.direction($l,n5,.2);Yt.curl(Pl,ba,1);Yt.direction(Pl,Fd,.2);Yt.direction(Pl,$d,1);Yt.direction(Pl,n5,.2);Yt.weight(os,2);Yt.weight(ls,2);var Hi=new Fl("point");Hi.curl(Aa,ba,1);Hi.curl(os,xa,.5);Hi.curl(ls,ba,.5);Hi.curl($l,ba,.5);Hi.curl(Pl,ba,.5);Hi.weight(os,2);Hi.weight(ls,2);var qi=new Fl("middle finger");qi.curl(Aa,xa,1);qi.curl(os,ba,.5);qi.curl(ls,ba,.5);qi.curl($l,ba,.5);qi.curl(Pl,ba,.5);qi.weight(os,2);qi.weight(ls,2);var Pd=new Fl("open palm");Pd.curl(Aa,xa,.75);Pd.curl(os,xa,.75);Pd.curl(ls,xa,.75);Pd.curl($l,xa,.75);Pd.curl(Pl,xa,.75);var g9=[ji,Yt,Hi,qi,Pd];var E2e=.7,_l={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function y9(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function x9(e,t){if(!e||!t)return[0,0];let r=y9(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=y9(e[1],e[2],t[1],t[2]);return[r,n]}function A9(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function R2e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>_l.NO_CURL_START_LIMIT?y=Gi.none:g>_l.HALF_CURL_START_LIMIT?y=Gi.half:y=Gi.full,y}function b9(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function v9(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function M2e(e,t,r,n,a,s,i,o){let l,u=v9(e,t,r,n),d=b9(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function F2e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=_l.DISTANCE_VOTE_POWER:m>.66?c+=_l.DISTANCE_VOTE_POWER:f+=_l.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],C=r[0],S=r[1];x===g?(C=r[0],S=r[1]):x===A&&(b=t[0],v=t[1]);let _=x9([b,v],[C,S]),M=A9(_,_l.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let O of n){let z=A9(O,_l.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],f+=z[2]}let I;return p===Math.max(p,c,f)?I=v9(l,o,u,h):f===Math.max(c,f)?I=b9(s,a,i,d):I=M2e(l,o,u,h,s,a,i,d),I}function w9(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Kr.all){let i=Kr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=x9(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Kr.all){let i=s===Kr.thumb?1:0,o=Kr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=R2e(l,u,d),p=F2e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function z0(e){if(!e||e.length===0)return null;let t=w9(e),r={};for(let n of Kr.all)r[Kr.getName(n)]={curl:Gi.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function k9(e){let t=[];if(!e||e.length===0)return t;let r=w9(e);for(let n of g9){let a=n.matchAgainst(r.curls,r.directions);a>=E2e&&t.push({name:n.name,confidence:a})}return t}var I9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},_d,zd,S9;async function s5(e,t){let r=await S9.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(I9))s[d]=I9[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=z0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function i5(e){var r,n;ce.initial&&(_d=null,zd=null),!_d||!zd?[_d,zd]=await Promise.all([e.hand.enabled?je((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?je((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",_d.modelUrl),e.debug&&ie("cached model:",zd.modelUrl));let t=new t5(_d);return S9=new r5(t,zd),[_d,zd]}var lr=[null,null],$2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ki=[[0,0],[0,0]],P2e=["hand","fist","pinch","point","face","tip","pinchtip"],T9=4,N9=1.6,_2e=512,z2e=1.4,O0=Number.MAX_SAFE_INTEGER,o5=0,us=[0,0],Gt={boxes:[],hands:[]},E9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function R9(e){var t;if(ce.initial&&(lr[0]=null),lr[0])e.debug&&ie("cached model:",lr[0].modelUrl);else{D0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),lr[0]=await je((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(lr[0].modelSignature.inputs);Ki[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ki[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[0]}async function M9(e){var t;if(ce.initial&&(lr[1]=null),lr[1])e.debug&&ie("cached model:",lr[1].modelUrl);else{lr[1]=await je((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(lr[1].modelSignature.inputs);Ki[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ki[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[1]}async function O2e(e,t){let r=[];if(!e||!lr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,_2e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await lr[0].executeAsync(n.cast,$2e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=en(n.scores,1);re(o[T9]),o.splice(T9,1),n.filtered=sr(o,1),re(o),n.max=fr(n.filtered,1),n.argmax=Cn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=C0(m,z2e),y=[Math.trunc(m[0]*us[0]),Math.trunc(m[1]*us[1]),Math.trunc(m[2]*us[0]),Math.trunc(m[3]*us[1])],A=d[p],x=P2e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function l5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&lr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[Ki[1][0],Ki[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=lr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/Ki[1][1],h[1]/Ki[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[us[0]*(h[0]+t.boxRaw[0]),us[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=z0(n.keypoints);for(let h of Object.keys(E9))n.annotations[h]=E9[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function u5(e,t){var a,s;if(!lr[0]||!lr[1]||!((a=lr[0])==null?void 0:a.inputs[0].shape)||!((s=lr[1])==null?void 0:s.inputs[0].shape))return[];us=[e.shape[2]||0,e.shape[1]||0],O0++;let r=(t.hand.skipTime||0)>oe()-o5,n=O0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Gt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-o5,l=O0<3*(t.hand.skipFrames||0);t.skipAllowed&&Gt.hands.length===t.hand.maxDetected?Gt.hands=await Promise.all(Gt.boxes.map(d=>l5(e,d,t))):t.skipAllowed&&o&&l&&Gt.hands.length>0?Gt.hands=await Promise.all(Gt.boxes.map(d=>l5(e,d,t))):(Gt.boxes=await O2e(e,t),o5=oe(),Gt.hands=await Promise.all(Gt.boxes.map(d=>l5(e,d,t))),O0=0);let u=[...Gt.boxes];if(Gt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Gt.hands.length;d++){let h=IT(Gt.hands[d].keypoints,us);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Gt.hands[d].fingerScore&&Gt.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=C0(h.box,N9),c=C0(h.boxRaw,N9);Gt.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Gt.hands.length;d++){let h=ns(Gt.hands[d].keypoints,us);Gt.hands[d].box=h.box,Gt.hands[d].boxRaw=h.boxRaw}i(Gt.hands)})}var Er,L0=[],d5=Number.MAX_SAFE_INTEGER,$9=0,P9=0;async function _9(e){var t;return ce.initial&&(Er=null),Er?e.debug&&ie("cached model:",Er.modelUrl):Er=await je((t=e.face.liveness)==null?void 0:t.modelPath),Er}async function p5(e,t,r,n){var i,o;if(!Er)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-P9,s=d5<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&$9===n&&L0[r]?(d5++,L0[r]):(d5=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[(Er==null?void 0:Er.inputs[0].shape)?Er.inputs[0].shape[2]:0,(Er==null?void 0:Er.inputs[0].shape)?Er.inputs[0].shape[1]:0],!1),d=Er==null?void 0:Er.execute(u),h=(await d.data())[0];L0[r]=Math.round(100*h)/100,$9=n,P9=oe(),re([u,d]),l(L0[r])}))}var Gh={};ep(Gh,{connected:()=>W0,horizontal:()=>h5,kpt:()=>B0,relative:()=>f5,vertical:()=>c5});var B0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],h5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],c5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],f5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],W0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var O9=.005,yn={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function m5(e){for(let t of h5){let r=e.keypoints.findIndex(a=>a.part===t[0]),n=e.keypoints.findIndex(a=>a.part===t[1]);if(e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[0]<e.keypoints[n].position[0]){let a=e.keypoints[r];e.keypoints[r]=e.keypoints[n],e.keypoints[n]=a}}for(let t of c5){let r=e.keypoints.findIndex(a=>a&&a.part===t[0]),n=e.keypoints.findIndex(a=>a&&a.part===t[1]);e.keypoints[r]&&e.keypoints[n]&&e.keypoints[r].position[1]<e.keypoints[n].position[1]&&e.keypoints.splice(r,1)}for(let[t,r]of f5){let n=e.keypoints.findIndex(u=>u&&u.part===t[0]),a=e.keypoints.findIndex(u=>u&&u.part===t[1]),s=e.keypoints.findIndex(u=>u&&u.part===r[0]),i=e.keypoints.findIndex(u=>u&&u.part===r[1]);if(!e.keypoints[s]||!e.keypoints[i])continue;let o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function D9(e){for(let t=0;t<e.length;t++)if(e[t]&&yn.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-yn.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-yn.keypoints[t].positionRaw[1])];r[0]<O9&&r[1]<O9?e[t]=yn.keypoints[t]:yn.keypoints[t]=e[t]}else yn.keypoints[t]=e[t];return e}function L9(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;yn.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=Vn(e,yn.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function B9(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+yn.padding[2][0]+yn.padding[2][1])/t[0]-yn.padding[2][0],n.position[1]*(t[1]+yn.padding[1][0]+yn.padding[1][1])/t[1]-yn.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=ns(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var An,V0=0,g5=Number.MAX_SAFE_INTEGER,zl={boxes:[],bodies:[],last:0};async function W9(e){return ce.initial&&(An=null),An?e.debug&&ie("cached model:",An.modelUrl):(D0(["size"],e),An=await je(e.body.modelPath)),V0=An.inputs[0].shape?An.inputs[0].shape[2]:0,V0<64&&(V0=256),An}async function L2e(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:B0[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=ns(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(W0)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return m5(u),i.push(u),i}async function B2e(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:B0[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ns(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(W0)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};m5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function y5(e,t){if(!An||!(An==null?void 0:An.inputs[0].shape))return[];t.skipAllowed||(zl.boxes.length=0),g5++;let r=(t.body.skipTime||0)>oe()-zl.last,n=g5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?zl.bodies:new Promise(async a=>{let s={};g5=0,s.input=L9(e,V0),s.res=An==null?void 0:An.execute(s.input),zl.last=oe();let i=await s.res.array();zl.bodies=s.res.shape[2]===17?await L2e(i,t,e):await B2e(i,t,e);for(let o of zl.bodies)B9(o,[e.shape[2]||1,e.shape[1]||1]),D9(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(zl.bodies)})}var Od,U0=[],U9=0,A5=Number.MAX_SAFE_INTEGER,j0=0,G0=2.5;async function G9(e){if(!Od||ce.initial){Od=await je(e.object.modelPath);let t=Object.values(Od.modelSignature.inputs);j0=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ie("cached model:",Od.modelUrl);return Od}async function W2e(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===Nd.length)),h=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<Nd.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/j0)),[v,C]=[A-G0/l*b[0],x-G0/l*b[1]],[S,E]=[A+G0/l*b[2]-v,x+G0/l*b[3]-C],R=[v,C,S,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:Nd[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function x5(e,t){let r=(t.object.skipTime||0)>oe()-U9,n=A5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&U0.length>0?(A5++,U0):(A5=0,!ce.kernels.includes("mod")||!ce.kernels.includes("sparsetodense")?U0:new Promise(async a=>{let 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Hh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],V2e=Hh.length,jh=Hh.reduce((e,t,r)=>(e[t]=r,e),{}),U2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],sbe=U2e.map(([e,t])=>[jh[e],jh[t]]),H9=[["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"]];function q9(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function K9(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var b5=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new 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l,u=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(ce.browser&&$n)l=$n?$n.fromPixels(e):null;else{u=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=ct(p,[e.height,e.width,u],"int32")}else if((!kd||or.width!==kd.width||or.height!==kd.height)&&(kd=Hr(or.width,or.height)),$n&&ce.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=$n.fromPixels(or):(kd=db(or),l=$n.fromPixels(kd));else{let f=db(or).getContext("2d").getImageData(0,0,s,i);u=f.data.length/s/i;let m=new Uint8Array(f.data.buffer);l=ct(m,[s,i,u])}if(u===4){let p=vl(l,[0,0,0],[-1,-1,3]);re(l),l=p}if(!l)throw new Error("input error: cannot create tensor");let d=me(l,"float32"),h=t.filter.equalization?await f0(d):Ht(d,0);return re([l,d]),{tensor:h,canvas:t.filter.return?or:null}}}async function WC(e,t){let r=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return r;if(!ts.inputTensor)ts.inputTensor=Or(t);else if(ts.inputTensor.shape[1]!==t.shape[1]||ts.inputTensor.shape[2]!==t.shape[2])re(ts.inputTensor),ts.inputTensor=Or(t);else{let n={};n.diff=he(t,ts.inputTensor),n.squared=L(n.diff,n.diff),n.sum=ke(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;re([ts.inputTensor,n.diff,n.squared,n.sum]),ts.inputTensor=Or(t),r=s<=(e.cacheSensitivity||0)}return r}async function VC(e,t,r){let n={};if(!t||!r||t.shape.length!==4||t.shape.length!==r.shape.length)return e.debug||ie("invalid input tensor or tensor shapes do not match:",t.shape,r.shape),0;if(t.shape[0]!==1||r.shape[0]!==1||t.shape[3]!==3||r.shape[3]!==3)return e.debug||ie("input tensors must be of shape [1, height, width, 3]:",t.shape,r.shape),0;n.input1=Or(t),n.input2=t.shape[1]!==r.shape[1]||t.shape[2]!==r.shape[2]?Ie.resizeBilinear(r,[t.shape[1],t.shape[2]]):Or(r),n.diff=he(n.input1,n.input2),n.squared=L(n.diff,n.diff),n.sum=ke(n.squared);let s=(await n.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return re([n.input1,n.input2,n.diff,n.squared,n.sum]),s}var UC=class{constructor(){fe(this,"browser");fe(this,"node");fe(this,"worker");fe(this,"platform","");fe(this,"agent","");fe(this,"backends",[]);fe(this,"initial");fe(this,"filter");fe(this,"tfjs");fe(this,"offscreen");fe(this,"perfadd",!1);fe(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});fe(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});fe(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});fe(this,"cpu",{model:void 0,flags:[]});fe(this,"kernels",[]);fe(this,"Canvas");fe(this,"Image");fe(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:zh["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let r=t[0].match(/\(([^()]+)\)/g);this.platform=r&&r[0]?r[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/  /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(kr().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&pn()==="wasm"&&(this.wasm.simd=await J().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await J().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Hr(100,100),r=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof r!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(pn()==="webgl"||pn()==="humangl")){let n=ga().gpgpu!=="undefined"?await ga().getGPGPUContext().gl:null;n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.renderer=n.getParameter(n.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name)}catch(n){this.webgpu.supported=!1}try{this.kernels=Na(pn()).map(n=>n.kernelName.toLowerCase())}catch(n){}}async updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},ce=new UC;var rs={cacheModels:!1,verbose:!0,debug:!1,modelBasePath:""};async function Vye(e,t){return rs.debug&&ie("load model fetch:",e,t),fetch(e,t)}function GC(e){rs.cacheModels=e.cacheModels,rs.verbose=e.debug,rs.modelBasePath=e.modelBasePath}async function je(e){let t=o3(rs.modelBasePath,e||""),r=t.split("/"),n="indexeddb://"+r[r.length-1].replace(".json",""),a=await Ir.listModels(),s=rs.cacheModels&&Object.keys(a).includes(n),i=typeof fetch=="undefined"?{}:{fetchFunc:(l,u)=>Vye(l,u)},o=new t0(s?n:t,i);try{o.findIOHandler(),rs.debug&&ie("model load handler:",o.handler);let l=await o.handler.load();o.loadSync(l),rs.verbose&&ie("load model:",o.modelUrl)}catch(l){ie("error loading model:",t,l)}if(rs.cacheModels&&!s)try{let l=await o.save(n);ie("model saved:",n,l)}catch(l){ie("error saving model:",t,l)}return o}var pb="2.6.4";var Hn,hb=[],jye=["white","black","asian","indian","other"],Hye=[15,23,28,35.5,45.5,55.5,65],jC=0,HC=0,cb=Number.MAX_SAFE_INTEGER;async function qC(e){return ce.initial&&(Hn=null),Hn?e.debug&&ie("cached model:",Hn.modelUrl):Hn=await je(e.face.gear),Hn}async function fb(e,t,r,n){var i,o;if(!Hn)return{age:0,gender:"unknown",genderScore:0,race:[]};let a=cb<(((i=t.face.gear)==null?void 0:i.skipFrames)||0),s=(((o=t.face.gear)==null?void 0:o.skipTime)||0)>oe()-HC;return t.skipAllowed&&s&&a&&jC===n&&hb[r]?(cb++,hb[r]):(cb=0,new Promise(async l=>{var y,A;if(!(Hn!=null&&Hn.inputs[0].shape))return;let u={},d=[[0,.1,.9,.9]];u.resize=Ie.cropAndResize(e,d,[0],[Hn.inputs[0].shape[2],Hn.inputs[0].shape[1]]);let h={age:0,gender:"unknown",genderScore:0,race:[]};(y=t.face.gear)!=null&&y.enabled&&([u.age,u.gender,u.race]=Hn.execute(u.resize,["age_output","gender_output","race_output"]));let p=await u.gender.data();h.gender=p[0]>p[1]?"male":"female",h.genderScore=Math.round(100*(p[0]>p[1]?p[0]:p[1]))/100;let c=await u.race.data();for(let x=0;x<c.length;x++)c[x]>(((A=t.face.gear)==null?void 0:A.minConfidence)||.2)&&h.race.push({score:Math.round(100*c[x])/100,race:jye[x]});h.race.sort((x,b)=>b.score-x.score);let m=Array.from(await u.age.data()).map((x,b)=>[Hye[b],x]).sort((x,b)=>b[1]-x[1]),g=m[0][0];for(let x=1;x<m.length;x++)g+=m[x][1]*(m[x][0]-g);h.age=Math.round(10*g)/10,Object.keys(u).forEach(x=>re(u[x])),hb[r]=h,jC=n,HC=oe(),l(h)}))}var Qe={tf255:255,tf1:1,tf2:2,tf05:.5,tf127:127.5,rgb:[.2989,.587,.114]};function XC(){Qe.tf255=Se(255,"float32"),Qe.tf1=Se(1,"float32"),Qe.tf2=Se(2,"float32"),Qe.tf05=Se(.5,"float32"),Qe.tf127=Se(127.5,"float32"),Qe.rgb=St([.2989,.587,.114],"float32")}var cn,g0=[],ZC=0,YC=0,mb=Number.MAX_SAFE_INTEGER;async function JC(e){return ce.initial&&(cn=null),cn?e.debug&&ie("cached model:",cn.modelUrl):cn=await je(e.face.ssrnet.modelPathAge),cn}async function gb(e,t,r,n){var i,o,l,u;if(!cn)return{age:0};let a=mb<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>oe()-YC;return t.skipAllowed&&a&&s&&ZC===n&&((l=g0[r])==null?void 0:l.age)&&((u=g0[r])==null?void 0:u.age)>0?(mb++,g0[r]):(mb=0,new Promise(async d=>{if(!(cn!=null&&cn.inputs)||!cn.inputs[0]||!cn.inputs[0].shape)return;let h={};h.resize=Ie.resizeBilinear(e,[cn.inputs[0].shape[2],cn.inputs[0].shape[1]],!1),h.enhance=L(h.resize,Qe.tf255);let p={age:0};if(t.face.ssrnet.enabled&&(h.age=cn.execute(h.enhance)),h.age){let c=await h.age.data();p.age=Math.trunc(10*c[0])/10}Object.keys(h).forEach(c=>re(h[c])),g0[r]=p,ZC=n,YC=oe(),d(p)}))}var qn,y0=[],eT=0,tT=0,yb=Number.MAX_SAFE_INTEGER,Ab=[.2989,.587,.114];async function rT(e){return ce.initial&&(qn=null),qn?e.debug&&ie("cached model:",qn.modelUrl):qn=await je(e.face.ssrnet.modelPathGender),qn}async function xb(e,t,r,n){var i,o,l,u;if(!qn)return{gender:"unknown",genderScore:0};let a=yb<(((i=t.face.ssrnet)==null?void 0:i.skipFrames)||0),s=(((o=t.face.ssrnet)==null?void 0:o.skipTime)||0)>oe()-tT;return t.skipAllowed&&a&&s&&eT===n&&((l=y0[r])==null?void 0:l.gender)&&((u=y0[r])==null?void 0:u.genderScore)>0?(yb++,y0[r]):(yb=0,new Promise(async d=>{if(!(qn!=null&&qn.inputs[0].shape))return;let h={};h.resize=Ie.resizeBilinear(e,[qn.inputs[0].shape[2],qn.inputs[0].shape[1]],!1),h.enhance=K(()=>{let[f,m,g]=Kt(h.resize,3,3),y=L(f,Ab[0]),A=L(m,Ab[1]),x=L(g,Ab[2]),b=im([y,A,x]);return L(he(b,Qe.tf05),2)});let p={gender:"unknown",genderScore:0};t.face.ssrnet.enabled&&(h.gender=qn.execute(h.enhance));let c=await h.gender.data();p.gender=c[0]>c[1]?"female":"male",p.genderScore=c[0]>c[1]?Math.trunc(100*c[0])/100:Math.trunc(100*c[1])/100,Object.keys(h).forEach(f=>re(h[f])),y0[r]=p,eT=n,tT=oe(),d(p)}))}var Tr,A0=[],bb=Number.MAX_SAFE_INTEGER,aT=0,sT=0;async function iT(e){var t;return ce.initial&&(Tr=null),Tr?e.debug&&ie("cached model:",Tr.modelUrl):Tr=await je((t=e.face.antispoof)==null?void 0:t.modelPath),Tr}async function vb(e,t,r,n){var i,o;if(!Tr)return 0;let a=(((i=t.face.antispoof)==null?void 0:i.skipTime)||0)>oe()-sT,s=bb<(((o=t.face.antispoof)==null?void 0:o.skipFrames)||0);return 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Kye=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Xye=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Zye=[33,133,362,263,1,78,308],VAe=Kye.map(e=>Lh[e]),UAe=Xye.map(e=>Lh[e]),GAe=Zye.map(e=>Lh[e]);var Sd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],x0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],Tb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Nb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],dT=(e,t)=>{let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:r,endPoint:n,landmarks:e.landmarks,confidence:e.confidence}},Sb=(e,t,r)=>{let n=t.shape[1],a=t.shape[2],s=[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a],i=Ie.cropAndResize(t,[s],[0],r),o=pe(i,Qe.tf255);return re(i),o},b0=(e,t)=>{let r=x0(e),n=Sd(e),a=[t*n[0]/2,t*n[1]/2];return{startPoint:[r[0]-a[0],r[1]-a[1]],endPoint:[r[0]+a[0],r[1]+a[1]],landmarks:e.landmarks,confidence:e.confidence}},v0=e=>{let t=x0(e),r=Sd(e),n=Math.max(...r)/2;return{startPoint:[Math.round(t[0]-n),Math.round(t[1]-n)],endPoint:[Math.round(t[0]+n),Math.round(t[1]+n)],landmarks:e.landmarks,confidence:e.confidence}},pT=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return{startPoint:[Math.min(...t),Math.min(...r)],endPoint:[Math.max(...t),Math.max(...r)],landmarks:e}},Cb=[[1,0,0],[0,1,0],[0,0,1]],Yye=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Jye=(e,t)=>Yye(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var lT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Rl=(e,t)=>{let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r},Qye=(e,t)=>{let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r},uT=(e,t)=>{let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Rl(e[a],Qye(t,s)))}return r},hT=(e,t)=>{let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=lT(t[0],t[1]),i=uT(s,a),o=lT(-t[0],-t[1]);return uT(i,o)},e2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Rl(t[0],r),-Rl(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]},t2e=(e,t)=>[Rl(e,t[0]),Rl(e,t[1])];function cT(e){let t={strides:[e/16,e/8],anchors:[2,6]},r=[];for(let n=0;n<t.strides.length;n++){let a=t.strides[n],s=Math.floor((e+a-1)/a),i=Math.floor((e+a-1)/a),o=t.anchors[n];for(let l=0;l<s;l++){let u=a*(l+.5);for(let d=0;d<i;d++){let h=a*(d+.5);for(let p=0;p<o;p++)r.push([h,u])}}}return r}function fT(e,t,r,n,a){let s=Sd(t),i=e.map(c=>[s[0]/a*(c[0]-a/2),s[1]/a*(c[1]-a/2),c[2]||0]),o=r&&r!==0&&Math.abs(r)>.2,l=o?hT(r,[0,0]):Cb,u=o?i.map(c=>[...t2e(c,l),c[2]]):i,d=o?e2e(n):Cb,h=x0(t),p=[Rl(h,d[0]),Rl(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2]||0)])}function mT(e,t,r,n){let a=t.landmarks.length>=wb.count?wb.symmetryLine:Dh.symmetryLine,s=0,i=Cb,o;if(e&&ce.kernels.includes("rotatewithoffset"))if(s=Jye(t.landmarks[a[0]],t.landmarks[a[1]]),s&&s!==0&&Math.abs(s)>.2){let u=x0(t),d=[u[0]/r.shape[2],u[1]/r.shape[1]],h=Ie.rotateWithOffset(r,s,0,d);i=hT(-s,u),o=Sb(t,h,[n,n]),re(h)}else o=Sb(t,r,[n,n]);else o=Sb(t,r,[n,n]);return[s,i,o]}var r2e=e=>{let t=e.map(n=>n[0]),r=e.map(n=>n[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...r)+(Math.max(...r)-Math.min(...r))/2]},gT=(e,t)=>{let r=r2e(e),n=Sd(t);return{startPoint:[r[0]-n[0]/2,r[1]-n[1]/2],endPoint:[r[0]+n[0]/2,r[1]+n[1]/2]}};var yT=6,n2e=1.2,_a,AT=null,Bi=0,Bh=null,w0=()=>Bi;async function xT(e){var t;return ce.initial&&(_a=null),_a?e.debug&&ie("cached model:",_a.modelUrl):_a=await je((t=e.face.detector)==null?void 0:t.modelPath),Bi=_a.inputs[0].shape?_a.inputs[0].shape[2]:0,Bh=Se(Bi,"int32"),AT=oa(cT(Bi)),_a}function a2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,AT),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=pe(t.boxSizes,Bh),t.centersNormalized=pe(t.centers,Bh),t.halfBoxSize=pe(t.boxSizesNormalized,Qe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Bh),t.endNormalized=L(t.ends,Bh);let r=td([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(n=>re(t[n])),r}async function bT(e,t){var o,l,u,d;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let r={};r.resized=Ie.resizeBilinear(e,[Bi,Bi]),r.div=pe(r.resized,Qe.tf127),r.normalized=he(r.div,Qe.tf05);let n=_a==null?void 0:_a.execute(r.normalized);if(Array.isArray(n)){let h=n.sort((p,c)=>p.size-c.size);r.concat384=kt([h[0],h[2]],2),r.concat512=kt([h[1],h[3]],2),r.concat=kt([r.concat512,r.concat384],1),r.batch=et(r.concat,0)}else r.batch=et(n);re(n),r.boxes=a2e(r.batch),r.logits=Pe(r.batch,[0,0],[-1,1]),r.sigmoid=Sr(r.logits),r.scores=et(r.sigmoid),r.nms=await Ie.nonMaxSuppressionAsync(r.boxes,r.scores,((o=t.face.detector)==null?void 0:o.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let a=await r.nms.array(),s=[],i=await r.scores.data();for(let h=0;h<a.length;h++){let p=i[a[h]];if(p>(((d=t.face.detector)==null?void 0:d.minConfidence)||0)){let c={};c.bbox=Pe(r.boxes,[a[h],0],[1,-1]),c.slice=Pe(r.batch,[a[h],yT-1],[1,-1]),c.squeeze=et(c.slice),c.landmarks=G(c.squeeze,[yT,-1]);let f=await c.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await c.landmarks.array(),confidence:p},g=dT(m,[(e.shape[2]||0)/Bi,(e.shape[1]||0)/Bi]),y=b0(g,t.face.scale||n2e),A=v0(y);s.push(A),Object.keys(c).forEach(x=>re(c[x]))}}return Object.keys(r).forEach(h=>re(r[h])),s}var k0={};Qd(k0,{connected:()=>Mb,kpt:()=>Rb});var Rb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Mb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var wT=224,s2e,i2e=5,I0=[8,16,32,32,32];async function kT(){let e=[],t=0;for(;t<i2e;){let r=0,n=t;for(;n<I0.length&&I0[n]===I0[t];)r+=2,n++;let a=I0[t],s=Math.ceil(wT/a),i=Math.ceil(wT/a);for(let o=0;o<s;++o)for(let l=0;l<i;++l)for(let u=0;u<r;++u)e.push({x:(l+.5)/i,y:(o+.5)/s});t=n}s2e={x:St(e.map(r=>r.x)),y:St(e.map(r=>r.y))}}function ns(e,t=[1,1]){let r=[e.map(o=>o[0]),e.map(o=>o[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[n[0],n[1],a[0]-n[0],a[1]-n[1]],i=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:i}}function IT(e,t=[1,1]){let r=[e.map(u=>u[0]),e.map(u=>u[1])],n=[Math.min(...r[0]),Math.min(...r[1])],a=[Math.max(...r[0]),Math.max(...r[1])],s=[(n[0]+a[0])/2,(n[1]+a[1])/2],i=Math.max(s[0]-n[0],s[1]-n[1],-s[0]+a[0],-s[1]+a[1]),o=[Math.trunc(s[0]-i),Math.trunc(s[1]-i),Math.trunc(2*i),Math.trunc(2*i)],l=[o[0]/t[0],o[1]/t[1],o[2]/t[0],o[3]/t[1]];return{box:o,boxRaw:l}}function S0(e,t){let r=[e[2]*t,e[3]*t];return[e[0]-(r[0]-e[2])/2,e[1]-(r[1]-e[3])/2,r[0],r[1]]}var TT={initial:!0},fn={detector:null,landmarks:null},Cd={detector:[224,224],landmarks:[256,256]},Fb=Number.MAX_SAFE_INTEGER,l2e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},T0=null,Wh,Wi=[[0,0],[0,0],[0,0],[0,0]],ST=0,CT=e=>1-1/(1+Math.exp(e));async function NT(e){if(TT.initial&&(fn.detector=null),!fn.detector&&e.body.detector&&e.body.detector.modelPath){fn.detector=await je(e.body.detector.modelPath);let t=Object.values(fn.detector.modelSignature.inputs);Cd.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Cd.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&fn.detector&&ie("cached model:",fn.detector.modelUrl);return await kT(),fn.detector}async function ET(e){if(TT.initial&&(fn.landmarks=null),fn.landmarks)e.debug&&ie("cached model:",fn.landmarks.modelUrl);else{fn.landmarks=await je(e.body.modelPath);let t=Object.values(fn.landmarks.modelSignature.inputs);Cd.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Cd.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return fn.landmarks}async function u2e(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let n;if(Wh&&(r.cropped=Ie.cropAndResize(e,[Wh],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],s=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Wi=[[0,0],a,s,[0,0]],r.pad=Vn(r.cropped||e,Wi),r.resize=Ie.resizeBilinear(r.pad,[t,t]),n=pe(r.resize,Qe.tf255)}else e.shape[1]!==t?(r.resize=Ie.resizeBilinear(r.cropped||e,[t,t]),n=pe(r.resize,Qe.tf255)):n=pe(r.cropped||e,Qe.tf255);return Object.keys(r).forEach(a=>re(r[a])),n}function d2e(e,t){for(let r of e)r.position=[Math.trunc(r.position[0]*(t[0]+Wi[2][0]+Wi[2][1])/t[0]-Wi[2][0]),Math.trunc(r.position[1]*(t[1]+Wi[1][0]+Wi[1][1])/t[1]-Wi[1][0]),r.position[2]],r.positionRaw=[r.position[0]/t[0],r.position[1]/t[1],2*r.position[2]/(t[0]+t[1])];if(Wh)for(let r of e)r.positionRaw=[r.positionRaw[0]+Wh[1],r.positionRaw[1]+Wh[0],r.positionRaw[2]],r.position=[Math.trunc(r.positionRaw[0]*t[0]),Math.trunc(r.positionRaw[1]*t[1]),r.positionRaw[2]];return e}async function p2e(e){let t=e.find(o=>o.part==="leftPalm"),r=e.find(o=>o.part==="leftWrist"),n=e.find(o=>o.part==="leftIndex");t.position[2]=((r.position[2]||0)+(n.position[2]||0))/2;let a=e.find(o=>o.part==="rightPalm"),s=e.find(o=>o.part==="rightWrist"),i=e.find(o=>o.part==="rightIndex");a.position[2]=((s.position[2]||0)+(i.position[2]||0))/2}async function h2e(e,t,r){var f;let n={};[n.ld,n.segmentation,n.heatmap,n.world,n.poseflag]=(f=fn.landmarks)==null?void 0:f.execute(e,l2e.landmarks);let a=(await n.poseflag.data())[0],s=await n.ld.data(),i=await n.world.data();Object.keys(n).forEach(m=>re(n[m]));let o=[],l=5;for(let m=0;m<s.length/l;m++){let g=CT(s[l*m+3]),y=CT(s[l*m+4]),A=Math.trunc(100*g*y*a)/100,x=[s[l*m+0]/Cd.landmarks[0],s[l*m+1]/Cd.landmarks[1],s[l*m+2]+0],b=[Math.trunc(r[0]*x[0]),Math.trunc(r[1]*x[1]),x[2]],v=[i[l*m+0],i[l*m+1],i[l*m+2]+0];o.push({part:Rb[m],positionRaw:x,position:b,distance:v,score:A})}if(a<(t.body.minConfidence||0))return null;p2e(o);let u=d2e(o,r),d=u.map(m=>m.position),h=ns(d,[r[0],r[1]]),p={};for(let[m,g]of Object.entries(Mb)){let y=[];for(let A=0;A<g.length-1;A++){let x=u.find(v=>v.part===g[A]),b=u.find(v=>v.part===g[A+1]);x&&b&&y.push([x.position,b.position])}p[m]=y}return{id:0,score:Math.trunc(100*a)/100,box:h.box,boxRaw:h.boxRaw,keypoints:u,annotations:p}}async function $b(e,t){let r=[e.shape[2]||0,e.shape[1]||0],n=(t.body.skipTime||0)>oe()-ST,a=Fb<(t.body.skipFrames||0);if(t.skipAllowed&&n&&a&&T0!==null)Fb++;else{let s={};s.landmarks=await u2e(e,256),T0=await h2e(s.landmarks,t,r),Object.keys(s).forEach(i=>re(s[i])),ST=oe(),Fb=0}return T0?[T0]:[]}var Td=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var as,Ml=0,Pb=[],MT=0,_b=Number.MAX_SAFE_INTEGER;async function FT(e){if(ce.initial&&(as=null),as)e.debug&&ie("cached model:",as.modelUrl);else{as=await je(e.object.modelPath);let t=Object.values(as.modelSignature.inputs);Ml=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return as}async function c2e(e,t,r){if(!e)return[];let n={},a=[],s=await e.array();n.squeeze=et(e);let i=Kt(n.squeeze,6,1);n.stack=sr([i[1],i[0],i[3],i[2]],1),n.boxes=et(n.stack),n.scores=et(i[4]),n.classes=et(i[5]),re([e,...i]),n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.scores,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence||0);let o=await n.nms.data(),l=0;for(let u of Array.from(o)){let d=Math.trunc(100*s[0][u][4])/100,h=s[0][u][5],p=Td[h].label,[c,f]=[s[0][u][0]/Ml,s[0][u][1]/Ml],m=[c,f,s[0][u][2]/Ml-c,s[0][u][3]/Ml-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];a.push({id:l++,score:d,class:h,label:p,box:g,boxRaw:m})}return Object.keys(n).forEach(u=>re(n[u])),a}async function zb(e,t){let r=(t.object.skipTime||0)>oe()-MT,n=_b<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&Pb.length>0?(_b++,Pb):(_b=0,new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[Ml,Ml]),o=t.object.enabled?as==null?void 0:as.execute(i,["tower_0/detections"]):null;MT=oe(),re(i);let l=await c2e(o,s,t);Pb=l,a(l)}))}var N0={};Qd(N0,{connected:()=>Db,kpt:()=>Ob});var Ob=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Db={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Nr,PT=0,qr={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Lb=Number.MAX_SAFE_INTEGER;async function _T(e){return ce.initial&&(Nr=null),Nr?e.debug&&ie("cached model:",Nr.modelUrl):Nr=await je(e.body.modelPath),Nr}async function f2e(e,t){let[r,n]=e.shape,a=G(e,[n*r]),s=fr(a,0),i=(await s.data())[0];if(re([a,s]),i>t){let o=Cn(a,0),l=ad(o,r),u=(await l.data())[0],d=pe(o,Se(r,"int32")),h=(await d.data())[0];return re([l,d]),[u,h,i]}return[0,0,i]}async function Bb(e,t){let r=(t.body.skipTime||0)>oe()-PT,n=Lb<(t.body.skipFrames||0);return t.skipAllowed&&r&&n&&Object.keys(qr.keypoints).length>0?(Lb++,[qr]):(Lb=0,new Promise(async a=>{var h;let s=K(()=>{if(!(Nr!=null&&Nr.inputs[0].shape))return null;let p=Ie.resizeBilinear(e,[Nr.inputs[0].shape[2],Nr.inputs[0].shape[1]],!1),c=L(p,Qe.tf2);return he(c,Qe.tf1)}),i;if(t.body.enabled&&(i=Nr==null?void 0:Nr.execute(s)),PT=oe(),re(s),i){qr.keypoints.length=0;let p=i.squeeze();re(i);let c=p.unstack(2);re(p);for(let f=0;f<c.length;f++){let[m,g,y]=await f2e(c[f],t.body.minConfidence);y>(((h=t.body)==null?void 0:h.minConfidence)||0)&&qr.keypoints.push({score:Math.round(100*y)/100,part:Ob[f],positionRaw:[m/Nr.inputs[0].shape[2],g/Nr.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/Nr.inputs[0].shape[2]),Math.round(e.shape[1]*g/Nr.inputs[0].shape[1])]})}c.forEach(f=>re(f))}qr.score=qr.keypoints.reduce((p,c)=>c.score>p?c.score:p,0);let o=qr.keypoints.map(p=>p.position[0]),l=qr.keypoints.map(p=>p.position[1]);qr.box=[Math.min(...o),Math.min(...l),Math.max(...o)-Math.min(...o),Math.max(...l)-Math.min(...l)];let u=qr.keypoints.map(p=>p.positionRaw[0]),d=qr.keypoints.map(p=>p.positionRaw[1]);qr.boxRaw=[Math.min(...u),Math.min(...d),Math.max(...u)-Math.min(...u),Math.max(...d)-Math.min(...d)];for(let[p,c]of Object.entries(Db)){let f=[];for(let m=0;m<c.length-1;m++){let g=qr.keypoints.find(A=>A.part===c[m]),y=qr.keypoints.find(A=>A.part===c[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}qr.annotations[p]=f}a([qr])}))}var m2e=["angry","disgust","fear","happy","sad","surprise","neutral"],_n,E0=[],OT=0,DT=0,Wb=Number.MAX_SAFE_INTEGER;async function LT(e){var t;return ce.initial&&(_n=null),_n?e.debug&&ie("cached model:",_n.modelUrl):_n=await je((t=e.face.emotion)==null?void 0:t.modelPath),_n}async function Vb(e,t,r,n){var i,o;if(!_n)return[];let a=Wb<(((i=t.face.emotion)==null?void 0:i.skipFrames)||0),s=(((o=t.face.emotion)==null?void 0:o.skipTime)||0)>oe()-DT;return t.skipAllowed&&s&&a&&OT===n&&E0[r]&&E0[r].length>0?(Wb++,E0[r]):(Wb=0,new Promise(async l=>{var d,h;let u=[];if((d=t.face.emotion)!=null&&d.enabled){let p={},c=_n!=null&&_n.inputs[0].shape?_n.inputs[0].shape[2]:0;p.resize=Ie.resizeBilinear(e,[c,c],!1),p.channels=L(p.resize,Qe.rgb),p.grayscale=ke(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Qe.tf05),p.grayscaleMul=L(p.grayscaleSub,Qe.tf2),p.emotion=_n==null?void 0:_n.execute(p.grayscaleMul),DT=oe();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((h=t.face.emotion)==null?void 0:h.minConfidence)||0)&&u.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:m2e[m]});u.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>re(p[m]))}E0[r]=u,OT=n,l(u)}))}var mn,Ub=[],WT=0,VT=0,UT=Number.MAX_SAFE_INTEGER;async function GT(e){return ce.initial&&(mn=null),mn?e.debug&&ie("cached model:",mn.modelUrl):mn=await je(e.face.mobilefacenet.modelPath),mn}async function Gb(e,t,r,n){var i,o;if(!mn)return[];let a=UT<(((i=t.face.embedding)==null?void 0:i.skipFrames)||0),s=(((o=t.face.embedding)==null?void 0:o.skipTime)||0)>oe()-VT;return t.skipAllowed&&s&&a&&WT===n&&Ub[r]?(UT++,Ub[r]):new Promise(async l=>{var d;let u=[];if(((d=t.face.embedding)==null?void 0:d.enabled)&&(mn==null?void 0:mn.inputs[0].shape)){let h={};h.crop=Ie.resizeBilinear(e,[mn.inputs[0].shape[2],mn.inputs[0].shape[1]],!1),h.data=mn==null?void 0:mn.execute(h.crop);let p=await h.data.data();u=Array.from(p)}Ub[r]=u,WT=n,VT=oe(),l(u)})}var ss,Vi=0,g2e=2.3,jb=Kn.leftEyeLower0,Hb=Kn.rightEyeLower0,Nd={leftBounds:[jb[0],jb[jb.length-1]],rightBounds:[Hb[0],Hb[Hb.length-1]]},Ed={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function XT(e){var t;return ce.initial&&(ss=null),ss?e.debug&&ie("cached model:",ss.modelUrl):ss=await je((t=e.face.iris)==null?void 0:t.modelPath),Vi=ss.inputs[0].shape?ss.inputs[0].shape[2]:0,Vi===-1&&(Vi=64),ss}function R0(e,t,r,n){for(let a=0;a<kb.length;a++){let{key:s,indices:i}=kb[a],o=Kn[`${r}${s}`];if(!n||n.includes(s))for(let l=0;l<i.length;l++){let u=i[l];e[o[l]]=[t[u][0],t[u][1],(t[u][2]+e[o[l]][2])/2]}}}var y2e=e=>{let t=e[Nd.leftBounds[0]][2],r=e[Nd.rightBounds[0]][2];return t-r},HT=(e,t,r,n,a,s=!1)=>{let i=v0(b0(pT([e[r],e[n]]),g2e)),o=Sd(i),l=Ie.cropAndResize(t,[[i.startPoint[1]/a,i.startPoint[0]/a,i.endPoint[1]/a,i.endPoint[0]/a]],[0],[Vi,Vi]);if(s&&ce.kernels.includes("flipleftright")){let u=Ie.flipLeftRight(l);re(l),l=u}return{box:i,boxSize:o,crop:l}},qT=(e,t,r,n=!1)=>{let a=[];for(let s=0;s<Ed.numCoordinates;s++){let i=e[s*3],o=e[s*3+1],l=e[s*3+2];a.push([(n?1-i/Vi:i/Vi)*r[0]+t.startPoint[0],o/Vi*r[1]+t.startPoint[1],l])}return{rawCoords:a,iris:a.slice(Ed.index)}},KT=(e,t,r)=>{let n=e[Kn[`${r}EyeUpper0`][Ed.upperCenter]][2],a=e[Kn[`${r}EyeLower0`][Ed.lowerCenter]][2],s=(n+a)/2;return t.map((i,o)=>{let l=s;return o===2?l=n:o===4&&(l=a),[i[0],i[1],l]})};async function ZT(e,t,r,n){if(!ss)return r.debug&&ie("face mesh iris detection requested, but model is not loaded"),e;let{box:a,boxSize:s,crop:i}=HT(e,t,Nd.leftBounds[0],Nd.leftBounds[1],n,!0),{box:o,boxSize:l,crop:u}=HT(e,t,Nd.rightBounds[0],Nd.rightBounds[1],n,!0),d=kt([i,u]);re(i),re(u);let h=ss.execute(d);re(d);let p=await h.data();re(h);let c=p.slice(0,Ed.numCoordinates*3),{rawCoords:f,iris:m}=qT(c,a,s,!0),g=p.slice(Ed.numCoordinates*3),{rawCoords:y,iris:A}=qT(g,o,l),x=y2e(e);Math.abs(x)<30?(R0(e,f,"left",null),R0(e,y,"right",null)):x<1?R0(e,f,"left",["EyeUpper0","EyeLower0"]):R0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=KT(e,m,"left"),v=KT(e,A,"right");return e.concat(b).concat(v)}var za={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},is=null,Rd=0;async function JT(e,t){var o,l,u,d,h,p,c,f,m;let r=(((o=t.face.detector)==null?void 0:o.skipTime)||0)>oe()-za.timestamp,n=za.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!r||!n||za.boxes.length===0?(za.boxes=await bT(e,t),za.timestamp=oe(),za.skipped=0):za.skipped++;let a=[],s=[],i=0;for(let g=0;g<za.boxes.length;g++){let y=za.boxes[g],A=0,x,b={id:i++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,b.tensor]=mT((u=t.face.detector)==null?void 0:u.rotation,y,e,(d=t.face.mesh)!=null&&d.enabled?Rd:w0()),(h=t==null?void 0:t.filter)!=null&&h.equalization){let v=await f0(b.tensor);re(b.tensor),b.tensor=v}if(b.boxScore=Math.round(100*y.confidence)/100,(p=t.face.mesh)!=null&&p.enabled)if(!is)t.debug&&ie("face mesh detection requested, but model is not loaded");else{let[v,C,S]=is.execute(b.tensor),E=await C.data();b.faceScore=Math.round(100*E[0])/100;let R=G(S,[-1,3]),_=await R.array();if(re([S,R,C,v]),b.faceScore<(((c=t.face.detector)==null?void 0:c.minConfidence)||1))y.confidence=b.faceScore;else{(f=t.face.iris)!=null&&f.enabled&&(_=await ZT(_,b.tensor,t,Rd)),b.mesh=fT(_,y,A,x,Rd),b.meshRaw=b.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Rd]);for(let I of Object.keys(Kn))b.annotations[I]=Kn[I].map(O=>b.mesh[O]);b.score=b.faceScore;let M={...gT(b.mesh,y),confidence:y.confidence,landmarks:y.landmarks};b.box=Tb(M,e),b.boxRaw=Nb(M,e),s.push(M)}}else{b.box=Tb(y,e),b.boxRaw=Nb(y,e),b.score=b.boxScore,b.mesh=y.landmarks.map(v=>[(y.startPoint[0]+y.endPoint[0])/2+(y.endPoint[0]+y.startPoint[0])*v[0]/w0(),(y.startPoint[1]+y.endPoint[1])/2+(y.endPoint[1]+y.startPoint[1])*v[1]/w0()]),b.meshRaw=b.mesh.map(v=>[v[0]/(e.shape[2]||0),v[1]/(e.shape[1]||0),(v[2]||0)/Rd]);for(let v of Object.keys(Dh))b.annotations[v]=[b.mesh[Dh[v]]]}b.score>(((m=t.face.detector)==null?void 0:m.minConfidence)||1)?a.push(b):re(b.tensor)}return za.boxes=s,a}async function QT(e){var t;return ce.initial&&(is=null),is?e.debug&&ie("cached model:",is.modelUrl):is=await je((t=e.face.mesh)==null?void 0:t.modelPath),Rd=is.inputs[0].shape?is.inputs[0].shape[2]:0,is}var e9=El,t9=Lh;var gn,M0=[],r9=0,n9=0,Kb=Number.MAX_SAFE_INTEGER;async function a9(e){var t;return ce.initial&&(gn=null),gn?e.debug&&ie("cached model:",gn.modelUrl):gn=await je((t=e.face.description)==null?void 0:t.modelPath),gn}function Xb(e){let t=e.image||e.tensor||e;if(!(gn!=null&&gn.inputs[0].shape))return t;let r=Ie.resizeBilinear(t,[gn.inputs[0].shape[2],gn.inputs[0].shape[1]],!1),n=L(r,Qe.tf255);return re(r),n}async function Zb(e,t,r,n){var i,o,l,u;if(!gn)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let a=Kb<(((i=t.face.description)==null?void 0:i.skipFrames)||0),s=(((o=t.face.description)==null?void 0:o.skipTime)||0)>oe()-r9;return t.skipAllowed&&a&&s&&n9===n&&((l=M0[r])==null?void 0:l.age)&&((u=M0[r])==null?void 0:u.age)>0?(Kb++,M0[r]):(Kb=0,new Promise(async d=>{var p,c;let h={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)!=null&&p.enabled){let f=Xb(e),m=gn==null?void 0:gn.execute(f);r9=oe(),re(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),A=Math.trunc(200*Math.abs(y[0]-.5))/100;A>(((c=t.face.description)==null?void 0:c.minConfidence)||0)&&(h.gender=y[0]<=.5?"female":"male",h.genderScore=Math.min(.99,A));let x=Cn(m.find(R=>R.shape[1]===100),1),b=(await x.data())[0];re(x);let C=await m.find(R=>R.shape[1]===100).data();h.age=Math.round(C[b-1]>C[b+1]?10*b-100*C[b-1]:10*b+100*C[b+1])/10;let S=m.find(R=>R.shape[1]===1024),E=S?await S.data():[];h.descriptor=Array.from(E),m.forEach(R=>re(R))}M0[r]=h,n9=n,d(h)}))}function F0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Vh(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function o9(e,t,r){let n=t.shape[1],a=t.shape[2],s=[[e.startPoint[1]/n,e.startPoint[0]/a,e.endPoint[1]/n,e.endPoint[0]/a]];return Ie.cropAndResize(t,s,[0],r)}function l9(e,t){let r=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],n=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],a=e.palmLandmarks.map(s=>[s[0]*t[0],s[1]*t[1]]);return{startPoint:r,endPoint:n,palmLandmarks:a,confidence:e.confidence}}function $0(e,t=1.5){let r=Vh(e),n=F0(e),a=[t*n[0]/2,t*n[1]/2],s=[r[0]-a[0],r[1]-a[1]],i=[r[0]+a[0],r[1]+a[1]];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function P0(e){let t=Vh(e),r=F0(e),a=Math.max(...r)/2,s=[t[0]-a,t[1]-a],i=[t[0]+a,t[1]+a];return{startPoint:s,endPoint:i,palmLandmarks:e.palmLandmarks}}function A2e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function u9(e,t){let r=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return A2e(r)}var s9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Ui(e,t){let r=0;for(let n=0;n<e.length;n++)r+=e[n]*t[n];return r}function x2e(e,t){let r=[];for(let n=0;n<e.length;n++)r.push(e[n][t]);return r}function i9(e,t){let r=[],n=e.length;for(let a=0;a<n;a++){r.push([]);for(let s=0;s<n;s++)r[a].push(Ui(e[a],x2e(t,s)))}return r}function Jb(e,t){let r=Math.cos(e),n=Math.sin(e),a=[[r,-n,0],[n,r,0],[0,0,1]],s=s9(t[0],t[1]),i=i9(s,a),o=s9(-t[0],-t[1]);return i9(i,o)}function d9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],r=[e[0][2],e[1][2]],n=[-Ui(t[0],r),-Ui(t[1],r)];return[t[0].concat(n[0]),t[1].concat(n[1]),[0,0,1]]}function Qb(e,t){return[Ui(e,t[0]),Ui(e,t[1])]}var 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n={};n.reshape=G(t,[-1,7,2]),n.div=pe(n.reshape,this.inputSizeTensor),n.landmarks=le(n.div,this.anchors[r]);let a=L(n.landmarks,this.inputSizeTensor);return Object.keys(n).forEach(s=>re(n[s])),a}async predict(t,r){let n={};n.resize=Ie.resizeBilinear(t,[this.inputSize,this.inputSize]),n.div=pe(n.resize,Qe.tf127),n.image=he(n.div,Qe.tf1),n.batched=this.model.execute(n.image),n.predictions=et(n.batched),n.slice=Pe(n.predictions,[0,0],[-1,1]),n.sigmoid=Sr(n.slice),n.scores=et(n.sigmoid);let a=await n.scores.data();n.boxes=Pe(n.predictions,[0,1],[-1,4]),n.norm=this.normalizeBoxes(n.boxes),n.nms=await Ie.nonMaxSuppressionAsync(n.norm,n.scores,3*r.hand.maxDetected,r.hand.iouThreshold,r.hand.minConfidence);let s=await n.nms.array(),i=[];for(let o of s){let l={};l.box=Pe(n.norm,[o,0],[1,-1]),l.slice=Pe(n.predictions,[o,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,o),l.palmLandmarks=G(l.norm,[-1,2]);let u=await l.box.data(),d=u.slice(0,2),h=u.slice(2,4),p=await l.palmLandmarks.array(),c={startPoint:d,endPoint:h,palmLandmarks:p,confidence:a[o]},f=l9(c,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);i.push(f),Object.keys(l).forEach(m=>re(l[m]))}return Object.keys(n).forEach(o=>re(n[o])),i}};var w2e=5,c9=1.65,f9=[0,5,9,13,17,1,2],k2e=0,I2e=2,m9=0,t5=class{constructor(t,r){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=r,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let r=t.map(i=>i[0]),n=t.map(i=>i[1]),a=[Math.min(...r),Math.min(...n)],s=[Math.max(...r),Math.max(...n)];return{startPoint:a,endPoint:s}}getBoxForPalmLandmarks(t,r){let n=t.map(s=>Qb([...s,1],r)),a=this.calculateLandmarksBoundingBox(n);return $0(P0(a),w2e)}getBoxForHandLandmarks(t){let r=this.calculateLandmarksBoundingBox(t),n=$0(P0(r),c9);n.palmLandmarks=[];for(let a=0;a<f9.length;a++)n.palmLandmarks.push(t[f9[a]].slice(0,2));return n}transformRawCoords(t,r,n,a){let s=F0(r),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(c=>[i[0]*(c[0]-this.inputSize/2),i[1]*(c[1]-this.inputSize/2),i[2]*c[2]]),l=Jb(n,[0,0]),u=o.map(c=>[...Qb(c,l),c[2]]),d=d9(a),h=[...Vh(r),1],p=[Ui(h,d[0]),Ui(h,d[1])];return u.map(c=>[Math.trunc(c[0]+p[0]),Math.trunc(c[1]+p[1]),Math.trunc(c[2])])}async estimateHands(t,r){let n=!1,a,s=(r.hand.skipTime||0)>oe()-m9,i=this.skipped<(r.hand.skipFrames||0);r.skipAllowed&&s&&i&&(a=await this.handDetector.predict(t,r),this.skipped=0),r.skipAllowed&&this.skipped++,a&&a.length>0&&(a.length!==this.detectedHands&&this.detectedHands!==r.hand.maxDetected||!r.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...a],this.storedBoxes.length>0&&(n=!0));let o=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(r.hand.landmarks){let d=r.hand.rotation?u9(u.palmLandmarks[k2e],u.palmLandmarks[I2e]):0,h=Vh(u),p=[h[0]/t.shape[2],h[1]/t.shape[1]],c=r.hand.rotation&&ce.kernels.includes("rotatewithoffset")?Ie.rotateWithOffset(t,d,0,p):t.clone(),f=Jb(-d,h),m=n?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=o9(m,c,[this.inputSize,this.inputSize]),y=pe(g,Qe.tf255);re(g),re(c);let[A,x]=this.handPoseModel.execute(y);m9=oe(),re(y);let b=(await A.data())[0];if(re(A),b>=r.hand.minConfidence/4){let v=G(x,[-1,3]),C=await v.array();re(x),re(v);let S=this.transformRawCoords(C,m,d,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};o.push(R)}else this.storedBoxes[l]=null;re(x)}else{let d=$0(P0(u),c9),h={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};o.push(h)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=o.length,o.length>r.hand.maxDetected&&(o.length=r.hand.maxDetected),o}};var Kr={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Kr.nameMapping[e],getPoints:e=>Kr.pointsMapping[e]},Gi={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Gi.nameMapping[e]},Dt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Dt.nameMapping[e]},Fl=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,r,n){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([r,n])}direction(t,r,n){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([r,n])}weight(t,r){this.weights[t]=r;let n=this.weights.reduce((a,s)=>a+s,0);this.weightsRelative=this.weights.map(a=>a*5/n)}matchAgainst(t,r){let n=0;for(let a in t){let s=t[a],i=this.curls[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}for(let a in r){let s=r[a],i=this.directions[a];if(typeof i=="undefined"){n+=this.weightsRelative[a];continue}for(let[o,l]of i)if(s===o){n+=l*this.weightsRelative[a];break}}return n/10}};var{thumb:Aa,index:os,middle:ls,ring:$l,pinky:Pl}=Kr,{none:xa,half:C2e,full:ba}=Gi,{verticalUp:Md,verticalDown:$xe,horizontalLeft:r5,horizontalRight:T2e,diagonalUpRight:N2e,diagonalUpLeft:Fd,diagonalDownRight:Pxe,diagonalDownLeft:_xe}=Dt,ji=new Fl("thumbs up");ji.curl(Aa,xa,1);ji.direction(Aa,Md,1);ji.direction(Aa,Fd,.25);ji.direction(Aa,N2e,.25);for(let e of[Kr.index,Kr.middle,Kr.ring,Kr.pinky])ji.curl(e,ba,1),ji.direction(e,r5,1),ji.direction(e,T2e,1);var Yt=new Fl("victory");Yt.curl(Aa,C2e,.5);Yt.curl(Aa,xa,.5);Yt.direction(Aa,Md,1);Yt.direction(Aa,Fd,1);Yt.curl(os,xa,1);Yt.direction(os,Md,.75);Yt.direction(os,Fd,1);Yt.curl(ls,xa,1);Yt.direction(ls,Md,1);Yt.direction(ls,Fd,.75);Yt.curl($l,ba,1);Yt.direction($l,Md,.2);Yt.direction($l,Fd,1);Yt.direction($l,r5,.2);Yt.curl(Pl,ba,1);Yt.direction(Pl,Md,.2);Yt.direction(Pl,Fd,1);Yt.direction(Pl,r5,.2);Yt.weight(os,2);Yt.weight(ls,2);var Hi=new Fl("point");Hi.curl(Aa,ba,1);Hi.curl(os,xa,.5);Hi.curl(ls,ba,.5);Hi.curl($l,ba,.5);Hi.curl(Pl,ba,.5);Hi.weight(os,2);Hi.weight(ls,2);var qi=new Fl("middle finger");qi.curl(Aa,xa,1);qi.curl(os,ba,.5);qi.curl(ls,ba,.5);qi.curl($l,ba,.5);qi.curl(Pl,ba,.5);qi.weight(os,2);qi.weight(ls,2);var $d=new Fl("open palm");$d.curl(Aa,xa,.75);$d.curl(os,xa,.75);$d.curl(ls,xa,.75);$d.curl($l,xa,.75);$d.curl(Pl,xa,.75);var g9=[ji,Yt,Hi,qi,$d];var E2e=.7,_l={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function y9(e,t,r,n){let a=(t-n)/(e-r),s=Math.atan(a)*180/Math.PI;return s<=0?s=-s:s>0&&(s=180-s),s}function x9(e,t){if(!e||!t)return[0,0];let r=y9(e[0],e[1],t[0],t[1]);if(e.length===2)return r;let n=y9(e[1],e[2],t[1],t[2]);return[r,n]}function A9(e,t=1){let r=0,n=0,a=0;return e>=75&&e<=105?r=1*t:e>=25&&e<=155?n=1*t:a=1*t,[r,n,a]}function R2e(e,t,r){let n=e[0]-t[0],a=e[0]-r[0],s=t[0]-r[0],i=e[1]-t[1],o=e[1]-r[1],l=t[1]-r[1],u=e[2]-t[2],d=e[2]-r[2],h=t[2]-r[2],p=Math.sqrt(n*n+i*i+u*u),c=Math.sqrt(a*a+o*o+d*d),f=Math.sqrt(s*s+l*l+h*h),m=(f*f+p*p-c*c)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>_l.NO_CURL_START_LIMIT?y=Gi.none:g>_l.HALF_CURL_START_LIMIT?y=Gi.half:y=Gi.full,y}function b9(e,t,r,n){let a;return n===Math.abs(e)?e>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:n===Math.abs(t)?t>0?a=Dt.horizontalLeft:a=Dt.horizontalRight:r>0?a=Dt.horizontalLeft:a=Dt.horizontalRight,a}function v9(e,t,r,n){let a;return n===Math.abs(e)?e<0?a=Dt.verticalDown:a=Dt.verticalUp:n===Math.abs(t)?t<0?a=Dt.verticalDown:a=Dt.verticalUp:r<0?a=Dt.verticalDown:a=Dt.verticalUp,a}function M2e(e,t,r,n,a,s,i,o){let l,u=v9(e,t,r,n),d=b9(a,s,i,o);return u===Dt.verticalUp?d===Dt.horizontalLeft?l=Dt.diagonalUpLeft:l=Dt.diagonalUpRight:d===Dt.horizontalLeft?l=Dt.diagonalDownLeft:l=Dt.diagonalDownRight,l}function F2e(e,t,r,n){let a=e[0]-t[0],s=e[0]-r[0],i=t[0]-r[0],o=e[1]-t[1],l=e[1]-r[1],u=t[1]-r[1],d=Math.max(Math.abs(a),Math.abs(s),Math.abs(i)),h=Math.max(Math.abs(o),Math.abs(l),Math.abs(u)),p=0,c=0,f=0,m=h/(d+1e-5);m>1.5?p+=_l.DISTANCE_VOTE_POWER:m>.66?c+=_l.DISTANCE_VOTE_POWER:f+=_l.DISTANCE_VOTE_POWER;let g=Math.sqrt(a*a+o*o),y=Math.sqrt(s*s+l*l),A=Math.sqrt(i*i+u*u),x=Math.max(g,y,A),b=e[0],v=e[1],C=r[0],S=r[1];x===g?(C=r[0],S=r[1]):x===A&&(b=t[0],v=t[1]);let _=x9([b,v],[C,S]),M=A9(_,_l.TOTAL_ANGLE_VOTE_POWER);p+=M[0],c+=M[1],f+=M[2];for(let O of n){let z=A9(O,_l.SINGLE_ANGLE_VOTE_POWER);p+=z[0],c+=z[1],f+=z[2]}let I;return p===Math.max(p,c,f)?I=v9(l,o,u,h):f===Math.max(c,f)?I=b9(s,a,i,d):I=M2e(l,o,u,h,s,a,i,d),I}function w9(e){let t=[],r=[],n=[],a=[];if(!e)return{curls:n,directions:a};for(let s of Kr.all){let i=Kr.getPoints(s),o=[],l=[];for(let u of i){let d=e[u[0]],h=e[u[1]],p=x9(d,h),c=p[0],f=p[1];o.push(c),l.push(f)}t.push(o),r.push(l)}for(let s of Kr.all){let i=s===Kr.thumb?1:0,o=Kr.getPoints(s),l=e[o[i][0]],u=e[o[i+1][1]],d=e[o[3][1]],h=R2e(l,u,d),p=F2e(l,u,d,t[s].slice(i));n[s]=h,a[s]=p}return{curls:n,directions:a}}function _0(e){if(!e||e.length===0)return null;let t=w9(e),r={};for(let n of Kr.all)r[Kr.getName(n)]={curl:Gi.getName(t.curls[n]),direction:Dt.getName(t.directions[n])};return r}function k9(e){let t=[];if(!e||e.length===0)return t;let r=w9(e);for(let n of g9){let a=n.matchAgainst(r.curls,r.directions);a>=E2e&&t.push({name:n.name,confidence:a})}return t}var I9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Pd,_d,S9;async function a5(e,t){let r=await S9.estimateHands(e,t);if(!r)return[];let n=[];for(let a=0;a<r.length;a++){let s={};if(r[a].landmarks)for(let d of Object.keys(I9))s[d]=I9[d].map(h=>r[a].landmarks[h]);let i=r[a].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(i&&i.length>0){for(let d of i)d[0]<o[0]&&(o[0]=d[0]),d[1]<o[1]&&(o[1]=d[1]),d[0]>o[2]&&(o[2]=d[0]),d[1]>o[3]&&(o[3]=d[1]);o[2]-=o[0],o[3]-=o[1],l=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=r[a].box?[Math.trunc(Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.max(0,r[a].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,r[a].box.bottomRight[0])-Math.max(0,r[a].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,r[a].box.bottomRight[1])-Math.max(0,r[a].box.topLeft[1]))]:[0,0,0,0],l=[r[a].box.topLeft[0]/(e.shape[2]||0),r[a].box.topLeft[1]/(e.shape[1]||0),(r[a].box.bottomRight[0]-r[a].box.topLeft[0])/(e.shape[2]||0),(r[a].box.bottomRight[1]-r[a].box.topLeft[1])/(e.shape[1]||0)];let u=_0(i);n.push({id:a,score:Math.round(100*r[a].confidence)/100,boxScore:Math.round(100*r[a].boxConfidence)/100,fingerScore:Math.round(100*r[a].fingerConfidence)/100,label:"hand",box:o,boxRaw:l,keypoints:i,annotations:s,landmarks:u})}return n}async function s5(e){var r,n;ce.initial&&(Pd=null,_d=null),!Pd||!_d?[Pd,_d]=await Promise.all([e.hand.enabled?je((r=e.hand.detector)==null?void 0:r.modelPath):null,e.hand.landmarks?je((n=e.hand.skeleton)==null?void 0:n.modelPath):null]):(e.debug&&ie("cached model:",Pd.modelUrl),e.debug&&ie("cached model:",_d.modelUrl));let t=new e5(Pd);return S9=new t5(t,_d),[Pd,_d]}var lr=[null,null],$2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Ki=[[0,0],[0,0]],P2e=["hand","fist","pinch","point","face","tip","pinchtip"],T9=4,N9=1.6,_2e=512,z2e=1.4,z0=Number.MAX_SAFE_INTEGER,i5=0,us=[0,0],Gt={boxes:[],hands:[]},E9={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function R9(e){var t;if(ce.initial&&(lr[0]=null),lr[0])e.debug&&ie("cached model:",lr[0].modelUrl);else{O0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),lr[0]=await je((t=e.hand.detector)==null?void 0:t.modelPath);let r=Object.values(lr[0].modelSignature.inputs);Ki[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ki[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[0]}async function M9(e){var t;if(ce.initial&&(lr[1]=null),lr[1])e.debug&&ie("cached model:",lr[1].modelUrl);else{lr[1]=await je((t=e.hand.skeleton)==null?void 0:t.modelPath);let r=Object.values(lr[1].modelSignature.inputs);Ki[1][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ki[1][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0}return lr[1]}async function O2e(e,t){let r=[];if(!e||!lr[0])return r;let n={},a=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,_2e),i=Math.round(s*a/8)*8;n.resize=Ie.resizeBilinear(e,[s,i]),n.cast=me(n.resize,"int32"),[n.rawScores,n.rawBoxes]=await lr[0].executeAsync(n.cast,$2e),n.boxes=et(n.rawBoxes,[0,2]),n.scores=et(n.rawScores,[0]);let o=en(n.scores,1);re(o[T9]),o.splice(T9,1),n.filtered=sr(o,1),re(o),n.max=fr(n.filtered,1),n.argmax=Cn(n.filtered,1);let l=0;n.nms=await Ie.nonMaxSuppressionAsync(n.boxes,n.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await n.nms.data(),d=await n.max.data(),h=await n.argmax.data();for(let p of Array.from(u)){let c=Pe(n.boxes,p,1),f=await c.data();re(c);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=S0(m,z2e),y=[Math.trunc(m[0]*us[0]),Math.trunc(m[1]*us[1]),Math.trunc(m[2]*us[0]),Math.trunc(m[3]*us[1])],A=d[p],x=P2e[h[p]],b={id:l++,score:A,box:y,boxRaw:g,label:x};r.push(b)}return Object.keys(n).forEach(p=>re(n[p])),r.sort((p,c)=>c.score-p.score),r.length>(t.hand.maxDetected||1)&&(r.length=t.hand.maxDetected||1),r}async function o5(e,t,r){let n={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&lr[1]&&r.hand.landmarks&&t.score>(r.hand.minConfidence||0)){let a={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];a.crop=Ie.cropAndResize(e,[s],[0],[Ki[1][0],Ki[1][1]],"bilinear"),a.div=pe(a.crop,Qe.tf255),[a.score,a.keypoints]=lr[1].execute(a.div,["Identity_1","Identity"]);let i=(await a.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(i))))/100;if(o>=(r.hand.minConfidence||0)){n.fingerScore=o,a.reshaped=G(a.keypoints,[-1,3]);let d=(await a.reshaped.array()).map(h=>[h[0]/Ki[1][1],h[1]/Ki[1][0],h[2]||0]).map(h=>[h[0]*t.boxRaw[2],h[1]*t.boxRaw[3],h[2]||0]);n.keypoints=d.map(h=>[us[0]*(h[0]+t.boxRaw[0]),us[1]*(h[1]+t.boxRaw[1]),h[2]||0]),n.landmarks=_0(n.keypoints);for(let h of Object.keys(E9))n.annotations[h]=E9[h].map(p=>n.landmarks&&n.keypoints[p]?n.keypoints[p]:null)}Object.keys(a).forEach(l=>re(a[l]))}return n}async function l5(e,t){var a,s;if(!lr[0]||!lr[1]||!((a=lr[0])!=null&&a.inputs[0].shape)||!((s=lr[1])!=null&&s.inputs[0].shape))return[];us=[e.shape[2]||0,e.shape[1]||0],z0++;let r=(t.hand.skipTime||0)>oe()-i5,n=z0<(t.hand.skipFrames||0);return t.skipAllowed&&r&&n?Gt.hands:new Promise(async i=>{let o=3*(t.hand.skipTime||0)>oe()-i5,l=z0<3*(t.hand.skipFrames||0);t.skipAllowed&&Gt.hands.length===t.hand.maxDetected?Gt.hands=await Promise.all(Gt.boxes.map(d=>o5(e,d,t))):t.skipAllowed&&o&&l&&Gt.hands.length>0?Gt.hands=await Promise.all(Gt.boxes.map(d=>o5(e,d,t))):(Gt.boxes=await O2e(e,t),i5=oe(),Gt.hands=await Promise.all(Gt.boxes.map(d=>o5(e,d,t))),z0=0);let u=[...Gt.boxes];if(Gt.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d<Gt.hands.length;d++){let h=IT(Gt.hands[d].keypoints,us);if(h.box[2]/(e.shape[2]||1)>.05&&h.box[3]/(e.shape[1]||1)>.05&&Gt.hands[d].fingerScore&&Gt.hands[d].fingerScore>(t.hand.minConfidence||0)){let p=S0(h.box,N9),c=S0(h.boxRaw,N9);Gt.boxes.push({...u[d],box:p,boxRaw:c})}}for(let d=0;d<Gt.hands.length;d++){let h=ns(Gt.hands[d].keypoints,us);Gt.hands[d].box=h.box,Gt.hands[d].boxRaw=h.boxRaw}i(Gt.hands)})}var Er,D0=[],u5=Number.MAX_SAFE_INTEGER,$9=0,P9=0;async function _9(e){var t;return ce.initial&&(Er=null),Er?e.debug&&ie("cached model:",Er.modelUrl):Er=await je((t=e.face.liveness)==null?void 0:t.modelPath),Er}async function d5(e,t,r,n){var i,o;if(!Er)return 0;let a=(((i=t.face.liveness)==null?void 0:i.skipTime)||0)>oe()-P9,s=u5<(((o=t.face.liveness)==null?void 0:o.skipFrames)||0);return t.skipAllowed&&a&&s&&$9===n&&D0[r]?(u5++,D0[r]):(u5=0,new Promise(async l=>{let u=Ie.resizeBilinear(e,[Er!=null&&Er.inputs[0].shape?Er.inputs[0].shape[2]:0,Er!=null&&Er.inputs[0].shape?Er.inputs[0].shape[1]:0],!1),d=Er==null?void 0:Er.execute(u),h=(await d.data())[0];D0[r]=Math.round(100*h)/100,$9=n,P9=oe(),re([u,d]),l(D0[r])}))}var Uh={};Qd(Uh,{connected:()=>B0,horizontal:()=>p5,kpt:()=>L0,relative:()=>c5,vertical:()=>h5});var L0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],p5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],h5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],c5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],B0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var 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o=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[i].position[0]-e.keypoints[n].position[0])]:[0,0],l=e.keypoints[a]?[Math.abs(e.keypoints[i].position[0]-e.keypoints[a].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[a].position[0])]:[0,0];if(o[0]>o[1]||l[0]>l[1]){let u=e.keypoints[n];e.keypoints[n]=e.keypoints[a],e.keypoints[a]=u}}}function D9(e){for(let t=0;t<e.length;t++)if(e[t]&&yn.keypoints[t]){let r=[Math.abs(e[t].positionRaw[0]-yn.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-yn.keypoints[t].positionRaw[1])];r[0]<O9&&r[1]<O9?e[t]=yn.keypoints[t]:yn.keypoints[t]=e[t]}else yn.keypoints[t]=e[t];return e}function L9(e,t){let r={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;yn.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],r.pad=Vn(e,yn.padding),r.resize=Ie.resizeBilinear(r.pad,[t,t]);let n=me(r.resize,"int32");return Object.keys(r).forEach(a=>re(r[a])),n}function B9(e,t){e.keypoints=e.keypoints.filter(n=>n&&n.position);for(let n of e.keypoints)n.position=[n.position[0]*(t[0]+yn.padding[2][0]+yn.padding[2][1])/t[0]-yn.padding[2][0],n.position[1]*(t[1]+yn.padding[1][0]+yn.padding[1][1])/t[1]-yn.padding[1][0]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1]];let r=ns(e.keypoints.map(n=>n.position),t);return e.box=r.box,e.boxRaw=r.boxRaw,e}var An,W0=0,m5=Number.MAX_SAFE_INTEGER,zl={boxes:[],bodies:[],last:0};async function W9(e){return ce.initial&&(An=null),An?e.debug&&ie("cached model:",An.modelUrl):(O0(["size"],e),An=await je(e.body.modelPath)),W0=An.inputs[0].shape?An.inputs[0].shape[2]:0,W0<64&&(W0=256),An}async function L2e(e,t,r){let n=e[0][0],a=[],s=0;for(let d=0;d<n.length;d++)if(s=n[d][2],s>t.body.minConfidence){let h=[n[d][1],n[d][0]];a.push({score:Math.round(100*s)/100,part:L0[d],positionRaw:h,position:[Math.round((r.shape[2]||0)*h[0]),Math.round((r.shape[1]||0)*h[1])]})}s=a.reduce((d,h)=>h.score>d?h.score:d,0);let i=[],o=ns(a.map(d=>d.position),[r.shape[2],r.shape[1]]),l={};for(let[d,h]of Object.entries(B0)){let p=[];for(let c=0;c<h.length-1;c++){let f=a.find(g=>g.part===h[c]),m=a.find(g=>g.part===h[c+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[d]=p}let u={id:0,score:s,box:o.box,boxRaw:o.boxRaw,keypoints:a,annotations:l};return f5(u),i.push(u),i}async function B2e(e,t,r){let n=[];for(let a=0;a<e[0].length;a++){let s=e[0][a],i=Math.round(100*s[51+4])/100;if(i>t.body.minConfidence){let o=[];for(let h=0;h<17;h++){let p=s[3*h+2];if(p>t.body.minConfidence){let c=[s[3*h+1],s[3*h+0]];o.push({part:L0[h],score:Math.round(100*p)/100,positionRaw:c,position:[Math.round((r.shape[2]||0)*c[0]),Math.round((r.shape[1]||0)*c[1])]})}}let l=ns(o.map(h=>h.position),[r.shape[2],r.shape[1]]),u={};for(let[h,p]of Object.entries(B0)){let c=[];for(let f=0;f<p.length-1;f++){let m=o.find(y=>y.part===p[f]),g=o.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&c.push([m.position,g.position])}u[h]=c}let d={id:a,score:i,box:l.box,boxRaw:l.boxRaw,keypoints:[...o],annotations:u};f5(d),n.push(d)}}return n.sort((a,s)=>s.score-a.score),n.length>t.body.maxDetected&&(n.length=t.body.maxDetected),n}async function g5(e,t){if(!An||!(An!=null&&An.inputs[0].shape))return[];t.skipAllowed||(zl.boxes.length=0),m5++;let r=(t.body.skipTime||0)>oe()-zl.last,n=m5<(t.body.skipFrames||0);return t.skipAllowed&&r&&n?zl.bodies:new Promise(async a=>{let s={};m5=0,s.input=L9(e,W0),s.res=An==null?void 0:An.execute(s.input),zl.last=oe();let i=await s.res.array();zl.bodies=s.res.shape[2]===17?await L2e(i,t,e):await B2e(i,t,e);for(let o of zl.bodies)B9(o,[e.shape[2]||1,e.shape[1]||1]),D9(o.keypoints);Object.keys(s).forEach(o=>re(s[o])),a(zl.bodies)})}var zd,V0=[],U9=0,y5=Number.MAX_SAFE_INTEGER,G0=0,U0=2.5;async function G9(e){if(!zd||ce.initial){zd=await je(e.object.modelPath);let t=Object.values(zd.modelSignature.inputs);G0=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&ie("cached model:",zd.modelUrl);return zd}async function W2e(e,t,r){let n=0,a=[];for(let l of[1,2,4])K(async()=>{let u=l*13,d=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)===Td.length)),h=et(e.find(m=>m.shape[1]===u**2&&(m.shape[2]||0)<Td.length)),c=await h.reshape([-1,4,h.shape[1]/4]).argMax(2).array(),f=await d.array();for(let m=0;m<d.shape[0];m++)for(let g=0;g<d.shape[1];g++){let y=f[m][g];if(y>(r.object.minConfidence||0)&&g!==61){let A=(.5+Math.trunc(m%u))/u,x=(.5+Math.trunc(m/u))/u,b=c[m].map(I=>I*(u/l/G0)),[v,C]=[A-U0/l*b[0],x-U0/l*b[1]],[S,E]=[A+U0/l*b[2]-v,x+U0/l*b[3]-C],R=[v,C,S,E];R=R.map(I=>Math.max(0,Math.min(I,1)));let _=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],M={id:n++,score:Math.round(100*y)/100,class:g+1,label:Td[g].label,box:_.map(I=>Math.trunc(I)),boxRaw:R};a.push(M)}}});e.forEach(l=>re(l));let s=a.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),i=a.map(l=>l.score),o=[];if(s&&s.length>0){let l=await Ie.nonMaxSuppressionAsync(s,i,r.object.maxDetected,r.object.iouThreshold,r.object.minConfidence);o=await l.data(),re(l)}return a=a.filter((l,u)=>o.includes(u)).sort((l,u)=>u.score-l.score),a}async function A5(e,t){let r=(t.object.skipTime||0)>oe()-U9,n=y5<(t.object.skipFrames||0);return t.skipAllowed&&r&&n&&V0.length>0?(y5++,V0):(y5=0,!ce.kernels.includes("mod")||!ce.kernels.includes("sparsetodense")?V0:new Promise(async a=>{let s=[e.shape[2]||0,e.shape[1]||0],i=Ie.resizeBilinear(e,[G0,G0],!1),o=pe(i,Qe.tf255),l=o.transpose([0,3,1,2]);re(o),re(i);let u;t.object.enabled&&(u=zd.execute(l)),U9=oe(),re(l);let d=await W2e(u,s,t);V0=d,a(d)}))}var jh=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],V2e=jh.length,Gh=jh.reduce((e,t,r)=>(e[t]=r,e),{}),U2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],sbe=U2e.map(([e,t])=>[Gh[e],Gh[t]]),H9=[["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"]];function q9(e){let t=e.reduce(({maxX:r,maxY:n,minX:a,minY:s},{position:{x:i,y:o}})=>({maxX:Math.max(r,i),maxY:Math.max(n,o),minX:Math.min(a,i),minY:Math.min(s,o)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function K9(e,[t,r],[n,a]){let s=t/n,i=r/a,o=(u,d)=>({id:d,score:u.score,boxRaw:[u.box[0]/a,u.box[1]/n,u.box[2]/a,u.box[3]/n],box:[Math.trunc(u.box[0]*i),Math.trunc(u.box[1]*s),Math.trunc(u.box[2]*i),Math.trunc(u.box[3]*s)],keypoints:u.keypoints.map(({score:h,part:p,position:c})=>({score:h,part:p,position:[Math.trunc(c.x*i),Math.trunc(c.y*s)],positionRaw:[c.x/n,c.y/n]})),annotations:{}});return e.map((u,d)=>o(u,d))}var x5=class{constructor(t,r){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new 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b5(e,t,r,n){return{y:n.get(e,t,r),x:n.get(e,t,r+V2e)}}function v5(e,t,r){let{heatmapY:n,heatmapX:a,id:s}=e,{y:i,x:o}=b5(n,a,s,r);return{x:e.heatmapX*t+o,y:e.heatmapY*t+i}}function w5(e,t,r){return e<t?t:e>r?r:e}function X9(e,t,r,n){let a=r-e,s=n-t;return a*a+s*s}function k5(e,t){return{x:e.x+t.x,y:e.y+t.y}}var va,j2e=["MobilenetV1/offset_2/BiasAdd","MobilenetV1/heatmap_2/BiasAdd","MobilenetV1/displacement_fwd_2/BiasAdd","MobilenetV1/displacement_bwd_2/BiasAdd"],j0=1,Od=16,H2e=50**2;function Z9(e,t,r,n,a,s,i=2){let o=y=>({y:s.get(y.y,y.x,e),x:s.get(y.y,y.x,s.shape[2]/2+e)}),l=(y,A,x)=>({y:w5(Math.round(y.y/Od),0,A-1),x:w5(Math.round(y.x/Od),0,x-1)}),[u,d]=n.shape,h=l(t.position,u,d),p=o(h),f=k5(t.position,p);for(let y=0;y<i;y++){let A=l(f,u,d),x=b5(A.y,A.x,r,a);f=k5({x:A.x*Od,y:A.y*Od},{x:x.x,y:x.y})}let m=l(f,u,d),g=n.get(m.y,m.x,r);return{position:f,part:jh[r],score:g}}function q2e(e,t,r,n,a){let 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yaw:${Dd(d.rotation.angle.yaw)}\xB0 pitch:${Dd(d.rotation.angle.pitch)}\xB0`),d.rotation.gaze.bearing&&h.push(`gaze: ${Dd(d.rotation.gaze.bearing)}\xB0`)),h.length===0&&h.push("face"),a.fillStyle=n.color;for(let p=h.length-1;p>=0;p--){let c=Math.max(d.box[0],0),f=p*n.lineHeight+d.box[1];n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(h[p],c+5,f+16)),a.fillStyle=n.labelColor,a.fillText(h[p],c+4,f+15)}}if(a.lineWidth=2,d.mesh&&d.mesh.length>0){if(n.drawPoints)for(let h of d.mesh)R5(a,h[0],h[1],h[2],n);if(n.drawPolygons){if(d.mesh.length>450)for(let h=0;h<El.length/3;h++){let p=[El[h*3+0],El[h*3+1],El[h*3+2]].map(c=>d.mesh[c]);lN(a,p,n)}if(d.annotations&&d.annotations.leftEyeIris&&d.annotations.leftEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let h=Math.abs(d.annotations.leftEyeIris[3][0]-d.annotations.leftEyeIris[1][0])/2,p=Math.abs(d.annotations.leftEyeIris[4][1]-d.annotations.leftEyeIris[2][1])/2;a.ellipse(d.annotations.leftEyeIris[0][0],d.annotations.leftEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(d.annotations&&d.annotations.rightEyeIris&&d.annotations.rightEyeIris[0]){a.strokeStyle=n.useDepth?"rgba(255, 200, 255, 0.3)":n.color,a.beginPath();let h=Math.abs(d.annotations.rightEyeIris[3][0]-d.annotations.rightEyeIris[1][0])/2,p=Math.abs(d.annotations.rightEyeIris[4][1]-d.annotations.rightEyeIris[2][1])/2;a.ellipse(d.annotations.rightEyeIris[0][0],d.annotations.rightEyeIris[0][1],h,p,0,0,2*Math.PI),a.stroke(),n.fillPolygons&&(a.fillStyle=n.useDepth?"rgba(255, 255, 200, 0.3)":n.color,a.fill())}if(n.drawGaze&&((s=d.rotation)==null?void 0:s.angle)&&typeof Path2D!="undefined"){a.strokeStyle="pink";let h=d.box[0]+d.box[2]/2-d.box[3]*Dd(d.rotation.angle.yaw)/90,p=d.box[1]+d.box[3]/2+d.box[2]*Dd(d.rotation.angle.pitch)/90,c=new Path2D(`
             M ${d.box[0]+d.box[2]/2} ${d.box[1]}
             C
               ${h} ${d.box[1]},
@@ -7052,7 +7050,7 @@ return a / b;`,Eie=`
               ${d.box[0]} ${p},
               ${d.box[0]+d.box[2]} ${p},
               ${d.box[0]+d.box[2]} ${d.box[1]+d.box[3]/2}
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n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(qh(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=n.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:n.color,M5(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=n.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:n.color,a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)tAe(a,l,n)}}}async function _5(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,qh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=n.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:n.color,M5(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||0;a.fillStyle=n.useDepth?`rgba(${127.5+2*u}, ${127.5-2*u}, 255, 0.5)`:n.color,a.fillText(l,o[o.length-1][0]+4,o[o.length-1][1]+4)};a.font=n.font,i(s.annotations.index,"index"),i(s.annotations.middle,"middle"),i(s.annotations.ring,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palm,"palm")}if(n.drawPolygons&&s.annotations){let i=o=>{if(!(!o||o.length===0||!o[0]))for(let l=0;l<o.length;l++){a.beginPath();let u=o[l][2]||0;a.strokeStyle=n.useDepth?`rgba(${127.5+l*u}, ${127.5-l*u}, 255, 0.5)`:n.color,a.moveTo(o[l>0?l-1:0][0],o[l>0?l-1:0][1]),a.lineTo(o[l][0],o[l][1]),a.stroke()}};a.lineWidth=n.lineWidth,i(s.annotations.index),i(s.annotations.middle),i(s.annotations.ring),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function z5(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,qh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels){let i=`${s.label} 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n=oe(),a=vr(ds,r),s=Promise.all([$5(e,t.face,a),P5(e,t.body,a),_5(e,t.hand,a),z5(e,t.object,a),F5(e,t.gesture,a)]);return R5=ce.perfadd?R5+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=R5,s}var Bd=.1,O5=.5;function nAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function hN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Kn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Bd&&Bd>0&&(a=a.map(i=>({x:i.x>.5?i.x+Bd:i.x-Bd,y:i.y>.5?i.y+Bd:i.y-Bd})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)nAe(i/t,o/t,a)||(n.set(O5*n.get(0,o,i,0),0,o,i,0),n.set(O5*n.get(0,o,i,1),0,o,i,1),n.set(O5*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var sAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},cN=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,v,C,S,E]=m,R,_,M;return x<1?x>-1?(M=Math.asin(x),_=Math.atan2(-C,g),R=Math.atan2(-v,b)):(M=-Math.PI/2,_=-Math.atan2(S,E),R=0):(M=Math.PI/2,_=Math.atan2(S,E),R=0),isNaN(R)&&(R=0),isNaN(_)&&(_=0),isNaN(M)&&(M=0),{pitch:2*-R,yaw:2*-_,roll:2*-M}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let o=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[i[10],i[152],i[234],i[454]].map(m=>[m[0]*t[0]/o,m[1]*t[1]/o,m[2]]),u=r(n(l[1],l[0])),d=r(n(l[3],l[2])),h=r(a(d,u));d=a(u,h);let p=[d[0],d[1],d[2],u[0],u[1],u[2],h[0],h[1],h[2]],c=s(p),f=i.length===478?sAe(e):{bearing:0,strength:0};return{angle:c,matrix:p,gaze:f}};var D5=async(e,t)=>{var c,f,m,g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X,D,Q;let r=oe(),n,a,s,i,o,l,u,d,h=[];e.state="run:face";let p=await JT(t,e.config);if(e.performance.face=ce.perfadd?(e.performance.face||0)+Math.trunc(oe()-r):Math.trunc(oe()-r),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){ie("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)==null?void 0:c.mask){let ae=await hN(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?cN(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=((f=e.config.face.emotion)==null?void 0:f.enabled)?Ub(p[V].tensor||ct([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=((m=e.config.face.emotion)==null?void 0:m.enabled)?await Ub(p[V].tensor||ct([]),e.config,V,p.length):[],e.performance.emotion=ce.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=((g=e.config.face.antispoof)==null?void 0:g.enabled)?wb(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=((y=e.config.face.antispoof)==null?void 0:y.enabled)?await wb(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.antispoof=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=((A=e.config.face.liveness)==null?void 0:A.enabled)?p5(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=((x=e.config.face.liveness)==null?void 0:x.enabled)?await p5(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.liveness=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=((b=e.config.face.gear)==null?void 0:b.enabled)?mb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=((v=e.config.face.gear)==null?void 0:v.enabled)?await mb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(n=((C=e.config.face.ssrnet)==null?void 0:C.enabled)?yb(p[V].tensor||ct([]),e.config,V,p.length):null,s=((S=e.config.face.ssrnet)==null?void 0:S.enabled)?bb(p[V].tensor||ct([]),e.config,V,p.length):null):(e.state="run:ssrnet",r=oe(),n=((E=e.config.face.ssrnet)==null?void 0:E.enabled)?await yb(p[V].tensor||ct([]),e.config,V,p.length):null,s=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await bb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.ssrnet=Math.trunc(oe()-r)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?o=((_=e.config.face.mobilefacenet)==null?void 0:_.enabled)?jb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:mobilefacenet",r=oe(),o=((M=e.config.face.mobilefacenet)==null?void 0:M.enabled)?await jb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.mobilefacenet=Math.trunc(oe()-r)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?d=((I=e.config.face.description)==null?void 0:I.enabled)?Yb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=((O=e.config.face.description)==null?void 0:O.enabled)?await Yb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=ce.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),!((D=e.config.face.iris)==null?void 0:D.enabled);let Y=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=((Q=e.config.face.detector)==null?void 0:Q.return)?et(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};(d==null?void 0:d.age)&&(Z.age=d.age),(d==null?void 0:d.gender)&&(Z.gender=d.gender),(d==null?void 0:d.genderScore)&&(Z.genderScore=d==null?void 0:d.genderScore),(d==null?void 0:d.descriptor)&&(Z.embedding=d==null?void 0:d.descriptor),(d==null?void 0:d.race)&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),Y&&Y!==0&&(Z.iris=Math.trunc(500/Y/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var fN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},mN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},gN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking right"}):p>.05&&t.push({iris:r,gesture:"looking left"});let c=Math.abs(e[r].mesh[145][1]-e[r].annotations.rightEyeIris[0][1])/e[r].box[3],f=Math.abs(e[r].mesh[374][1]-e[r].annotations.leftEyeIris[0][1])/e[r].box[3];(f<.01||c<.01||f>.022||c>.022)&&(u=!1),(f<.01||c<.01)&&t.push({iris:r,gesture:"looking down"}),(f>.022||c>.022)&&t.push({iris:r,gesture:"looking up"}),u&&t.push({iris:r,gesture:"looking center"})}return t},yN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=[];if(e[r].annotations)for(let[a,s]of Object.entries(e[r].annotations))a!=="palmBase"&&Array.isArray(s)&&s[0]&&n.push({name:a.toLowerCase(),position:s[0]});if(n&&n.length>0){let a=n.reduce((i,o)=>(i.position[2]||0)<(o.position[2]||0)?i:o);t.push({hand:r,gesture:`${a.name} forward`});let s=n.reduce((i,o)=>i.position[1]<o.position[1]?i:o);t.push({hand:r,gesture:`${s.name} up`})}if(e[r].keypoints){let a=k9(e[r].keypoints);for(let s of a)t.push({hand:r,gesture:s.name})}}return t};var Ne={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},L5=0;function AN(e,t){var i,o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X;let r=oe();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let n=Date.now()-e.timestamp,a=n<1e3?8-Math.log(n+1):1;if(e.canvas&&(Ne.canvas=e.canvas),e.error&&(Ne.error=e.error),!Ne.body||e.body.length!==Ne.body.length)Ne.body=JSON.parse(JSON.stringify(e.body));else for(let D=0;D<e.body.length;D++){let Q=e.body[D].box.map((Z,ae)=>((a-1)*Ne.body[D].box[ae]+Z)/a),V=e.body[D].boxRaw.map((Z,ae)=>((a-1)*Ne.body[D].boxRaw[ae]+Z)/a),ee=e.body[D].keypoints.map((Z,ae)=>{var de,Ae,be,Ee,Me,De,Be,Ze,ot;return{score:Z.score,part:Z.part,position:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[0]||0)+(Z.position[0]||0))/a:Z.position[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[1]||0)+(Z.position[1]||0))/a:Z.position[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].position[2]||0)+(Z.position[2]||0))/a:Z.position[2]],positionRaw:[Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[0]||0)+(Z.positionRaw[0]||0))/a:Z.positionRaw[0],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[1]||0)+(Z.positionRaw[1]||0))/a:Z.positionRaw[1],Ne.body[D].keypoints[ae]?((a-1)*(Ne.body[D].keypoints[ae].positionRaw[2]||0)+(Z.positionRaw[2]||0))/a:Z.positionRaw[2]],distance:[Ne.body[D].keypoints[ae]?((a-1)*(((de=Ne.body[D].keypoints[ae].distance)==null?void 0:de[0])||0)+(((Ae=Z.distance)==null?void 0:Ae[0])||0))/a:(be=Z.distance)==null?void 0:be[0],Ne.body[D].keypoints[ae]?((a-1)*(((Ee=Ne.body[D].keypoints[ae].distance)==null?void 0:Ee[1])||0)+(((Me=Z.distance)==null?void 0:Me[1])||0))/a:(De=Z.distance)==null?void 0:De[1],Ne.body[D].keypoints[ae]?((a-1)*(((Be=Ne.body[D].keypoints[ae].distance)==null?void 0:Be[2])||0)+(((Ze=Z.distance)==null?void 0:Ze[2])||0))/a:(ot=Z.distance)==null?void 0:ot[2]]}}),Y={},se={connected:{}};((o=(i=t.body)==null?void 0:i.modelPath)==null?void 0:o.includes("efficientpose"))?se=E0:((u=(l=t.body)==null?void 0:l.modelPath)==null?void 0:u.includes("blazepose"))?se=I0:((h=(d=t.body)==null?void 0:d.modelPath)==null?void 0:h.includes("movenet"))&&(se=Gh);for(let[Z,ae]of Object.entries(se.connected)){let de=[];for(let Ae=0;Ae<ae.length-1;Ae++){let be=ee.find(Me=>Me.part===ae[Ae]),Ee=ee.find(Me=>Me.part===ae[Ae+1]);be&&Ee&&de.push([be.position,Ee.position])}Y[Z]=de}Ne.body[D]={...e.body[D],box:Q,boxRaw:V,keypoints:ee,annotations:Y}}if(!Ne.hand||e.hand.length!==Ne.hand.length)Ne.hand=JSON.parse(JSON.stringify(e.hand));else for(let D=0;D<e.hand.length;D++){let Q=e.hand[D].box.map((se,Z)=>((a-1)*Ne.hand[D].box[Z]+se)/a),V=e.hand[D].boxRaw.map((se,Z)=>((a-1)*Ne.hand[D].boxRaw[Z]+se)/a);Ne.hand[D].keypoints.length!==e.hand[D].keypoints.length&&(Ne.hand[D].keypoints=e.hand[D].keypoints);let ee=e.hand[D].keypoints&&e.hand[D].keypoints.length>0?e.hand[D].keypoints.map((se,Z)=>se.map((ae,de)=>((a-1)*(Ne.hand[D].keypoints[Z][de]||1)+(ae||0))/a)):[],Y={};if(Object.keys(Ne.hand[D].annotations).length!==Object.keys(e.hand[D].annotations).length)Ne.hand[D].annotations=e.hand[D].annotations,Y=Ne.hand[D].annotations;else if(e.hand[D].annotations)for(let se of Object.keys(e.hand[D].annotations))Y[se]=e.hand[D].annotations[se]&&e.hand[D].annotations[se][0]?e.hand[D].annotations[se].map((Z,ae)=>Z.map((de,Ae)=>((a-1)*Ne.hand[D].annotations[se][ae][Ae]+de)/a)):null;Ne.hand[D]={...e.hand[D],box:Q,boxRaw:V,keypoints:ee,annotations:Y}}if(!Ne.face||e.face.length!==Ne.face.length)Ne.face=JSON.parse(JSON.stringify(e.face));else for(let D=0;D<e.face.length;D++){let Q=e.face[D].box.map((ee,Y)=>((a-1)*Ne.face[D].box[Y]+ee)/a),V=e.face[D].boxRaw.map((ee,Y)=>((a-1)*Ne.face[D].boxRaw[Y]+ee)/a);if(e.face[D].rotation){let ee={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};ee.matrix=(p=e.face[D].rotation)==null?void 0:p.matrix,ee.angle={roll:((a-1)*(((f=(c=Ne.face[D].rotation)==null?void 0:c.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[D].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/a,yaw:((a-1)*(((A=(y=Ne.face[D].rotation)==null?void 0:y.angle)==null?void 0:A.yaw)||0)+(((b=(x=e.face[D].rotation)==null?void 0:x.angle)==null?void 0:b.yaw)||0))/a,pitch:((a-1)*(((C=(v=Ne.face[D].rotation)==null?void 0:v.angle)==null?void 0:C.pitch)||0)+(((E=(S=e.face[D].rotation)==null?void 0:S.angle)==null?void 0:E.pitch)||0))/a},ee.gaze={bearing:((a-1)*(((_=(R=Ne.face[D].rotation)==null?void 0:R.gaze)==null?void 0:_.bearing)||0)+(((I=(M=e.face[D].rotation)==null?void 0:M.gaze)==null?void 0:I.bearing)||0))/a,strength:((a-1)*(((z=(O=Ne.face[D].rotation)==null?void 0:O.gaze)==null?void 0:z.strength)||0)+(((X=(j=e.face[D].rotation)==null?void 0:j.gaze)==null?void 0:X.strength)||0))/a},Ne.face[D]={...e.face[D],rotation:ee,box:Q,boxRaw:V}}Ne.face[D]={...e.face[D],box:Q,boxRaw:V}}if(!Ne.object||e.object.length!==Ne.object.length)Ne.object=JSON.parse(JSON.stringify(e.object));else for(let D=0;D<e.object.length;D++){let Q=e.object[D].box.map((ee,Y)=>((a-1)*Ne.object[D].box[Y]+ee)/a),V=e.object[D].boxRaw.map((ee,Y)=>((a-1)*Ne.object[D].boxRaw[Y]+ee)/a);Ne.object[D]={...e.object[D],box:Q,boxRaw:V}}if(e.persons){let D=e.persons;if(!Ne.persons||D.length!==Ne.persons.length)Ne.persons=JSON.parse(JSON.stringify(D));else for(let Q=0;Q<D.length;Q++)Ne.persons[Q].box=D[Q].box.map((V,ee)=>((a-1)*Ne.persons[Q].box[ee]+V)/a)}e.gesture&&(Ne.gesture=e.gesture);let s=oe();return L5=ce.perfadd?L5+Math.round(s-r):Math.round(s-r),e.performance&&(Ne.performance={...e.performance,interpolate:L5}),Ne}function K0(e,t,r={order:2,multiplier:25}){let n=0;for(let a=0;a<e.length;a++){let s=!r.order||r.order===2?e[a]-t[a]:Math.abs(e[a]-t[a]);n+=!r.order||r.order===2?s*s:s**r.order}return(r.multiplier||20)*n}var xN=(e,t,r,n)=>{if(e===0)return 1;let a=t===2?Math.sqrt(e):e**(1/t),s=(1-a/100-r)/(n-r);return Math.max(Math.min(s,1),0)};function bN(e,t,r={order:2,multiplier:25,min:.2,max:.8}){let n=K0(e,t,r);return xN(n,r.order||2,r.min||0,r.max||1)}function vN(e,t,r={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let n=Number.MAX_SAFE_INTEGER,a=-1;for(let i=0;i<t.length;i++){let o=K0(e,t[i],r);if(o<n&&(n=o,a=i),n<(r.threshold||0))break}let s=xN(n,r.order||2,r.min||0,r.max||1);return{index:a,distance:n,similarity:s}}function wN(e,t,r,n,a){var o,l,u,d,h,p,c,f,m,g,y,A,x,b,v,C;let s=0,i=[];for(let S of e){let E={id:s++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let z of t)S.box[0]>z.box[0]&&S.box[0]<z.box[0]+z.box[2]&&S.box[1]+S.box[3]>z.box[1]&&S.box[1]+S.box[3]<z.box[1]+z.box[3]&&(E.body=z);if(E.body)for(let z of r)z.box[0]+z.box[2]>E.body.box[0]&&z.box[0]+z.box[2]<E.body.box[0]+E.body.box[2]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=z),z.box[0]<E.body.box[0]+E.body.box[2]&&z.box[0]>E.body.box[0]&&z.box[1]+z.box[3]>E.body.box[1]&&z.box[1]+z.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=z);for(let z of n)z.face!==void 0&&z.face===S.id?(o=E.gestures)==null||o.push(z):z.iris!==void 0&&z.iris===S.id?(l=E.gestures)==null||l.push(z):z.body!==void 0&&z.body===((u=E.body)==null?void 0:u.id)?(d=E.gestures)==null||d.push(z):z.hand!==void 0&&z.hand===((p=(h=E.hands)==null?void 0:h.left)==null?void 0:p.id)?(c=E.gestures)==null||c.push(z):z.hand!==void 0&&z.hand===((m=(f=E.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=E.gestures)==null||g.push(z));let R=[],_=[],M=z=>{z&&z.length===4&&(R.push(z[0],z[0]+z[2]),_.push(z[1],z[1]+z[3]))};M((y=E.face)==null?void 0:y.box),M((A=E.body)==null?void 0:A.box),M((b=(x=E.hands)==null?void 0:x.left)==null?void 0:b.box),M((C=(v=E.hands)==null?void 0:v.right)==null?void 0:C.box);let I=Math.min(...R),O=Math.min(..._);E.box=[I,O,Math.max(...R)-I,Math.max(..._)-O],a&&a[1]&&a[2]&&(E.boxRaw=[E.box[0]/a[2],E.box[1]/a[1],E.box[2]/a[2],E.box[3]/a[1]]),i.push(E)}return i}var X0=`
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n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round";for(let i=0;i<t.length;i++){if(a.strokeStyle=n.color,a.fillStyle=n.color,a.lineWidth=n.lineWidth,a.font=n.font,n.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(Hh(a,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+n.lineHeight,t[i].box[2])),a.fillStyle=n.labelColor,a.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+n.lineHeight,t[i].box[2]))),n.drawPoints&&t[i].keypoints)for(let o=0;o<t[i].keypoints.length;o++)!t[i].keypoints[o].score||t[i].keypoints[o].score===0||(a.fillStyle=n.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:n.color,R5(a,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,n));if(n.drawLabels&&t[i].keypoints){a.font=n.font;for(let o of t[i].keypoints)!o.score||o.score===0||(a.fillStyle=n.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:n.color,a.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4))}if(n.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let o of Object.values(t[i].annotations))for(let l of o)tAe(a,l,n)}}}async function P5(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s of t){if(n.drawBoxes&&(a.strokeStyle=n.color,a.fillStyle=n.color,Hh(a,s.box[0],s.box[1],s.box[2],s.box[3],n),n.drawLabels&&(n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(`hand:${Math.trunc(100*s.score)}%`,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])),a.stroke()),n.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)a.fillStyle=n.useDepth?`rgba(${127.5+2*(i[2]||0)}, ${127.5-2*(i[2]||0)}, 255, 0.5)`:n.color,R5(a,i[0],i[1],0,n);if(n.drawLabels&&s.annotations){let i=(o,l)=>{if(!o||o.length===0||!o[0])return;let u=o[o.length-1][2]||0;a.fillStyle=n.useDepth?`rgba(${127.5+2*u}, ${127.5-2*u}, 255, 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${Math.round(100*s.score)}%`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,s.box[0]+3,1+s.box[1]+n.lineHeight,s.box[2])),a.fillStyle=n.labelColor,a.fillText(i,s.box[0]+2,0+s.box[1]+n.lineHeight,s.box[2])}a.stroke()}}}async function uN(e,t,r){let n=vr(ds,r);if(!t||!e)return;let a=Ol(e);if(!!a){a.lineJoin="round",a.font=n.font;for(let s=0;s<t.length;s++)if(n.drawBoxes){if(a.strokeStyle=n.color,a.fillStyle=n.color,Hh(a,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],n),n.drawLabels){let i=`person #${s}`;n.shadowColor&&n.shadowColor!==""&&(a.fillStyle=n.shadowColor,a.fillText(i,t[s].box[0]+3,1+t[s].box[1]+n.lineHeight,t[s].box[2])),a.fillStyle=n.labelColor,a.fillText(i,t[s].box[0]+2,0+t[s].box[1]+n.lineHeight,t[s].box[2])}a.stroke()}}}async function dN(e,t){if(!e||!t)return;let r=Ol(t);!r||r.drawImage(e,0,0)}async function pN(e,t,r){if(!t||!t.performance||!t||!e)return null;let n=oe(),a=vr(ds,r),s=Promise.all([F5(e,t.face,a),$5(e,t.body,a),P5(e,t.hand,a),_5(e,t.object,a),M5(e,t.gesture,a)]);return E5=ce.perfadd?E5+Math.round(oe()-n):Math.round(oe()-n),t.performance.draw=E5,s}var Ld=.1,z5=.5;function nAe(e,t,r){let n=!1,a=r.length-1;for(let s=0;s<r.length;a=s++)r[s].y>t!=r[a].y>t&&e<(r[a].x-r[s].x)*(t-r[s].y)/(r[a].y-r[s].y)+r[s].x&&(n=!n);return n}async function hN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,r=e.tensor.shape[1]||0,n=await e.tensor.buffer(),a=[];for(let i of Kn.silhouette)a.push({x:(e.mesh[i][0]-e.box[0])/e.box[2],y:(e.mesh[i][1]-e.box[1])/e.box[3]});Ld&&Ld>0&&(a=a.map(i=>({x:i.x>.5?i.x+Ld:i.x-Ld,y:i.y>.5?i.y+Ld:i.y-Ld})));for(let i=0;i<t;i++)for(let o=0;o<r;o++)nAe(i/t,o/t,a)||(n.set(z5*n.get(0,o,i,0),0,o,i,0),n.set(z5*n.get(0,o,i,1),0,o,i,1),n.set(z5*n.get(0,o,i,2),0,o,i,2));let s=n.toTensor();return re(n),s}var sAe=e=>{let t=(h,p)=>Math.atan2(h[1]-p[1],h[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let r=[0,-.1],n=1,a=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),s=a?e.mesh[473]:e.mesh[468],i=a?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=a?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(i[0]-s[0])/o[0]-r[0],n*(s[1]-i[1])/o[1]-r[1]],u=Math.sqrt(l[0]*l[0]+l[1]*l[1]);return u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},cN=(e,t)=>{let r=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},n=(m,g)=>{let y=m[0]-g[0],A=m[1]-g[1],x=m[2]-g[2];return[y,A,x]},a=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],A=m[2]*g[0]-m[0]*g[2],x=m[0]*g[1]-m[1]*g[0];return[y,A,x]},s=m=>{let[g,y,A,x,b,v,C,S,E]=m,R,_,M;return 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V=0;V<p.length;V++){if(e.analyze("Get Face"),!p[V].tensor||p[V].tensor.isDisposedInternal){ie("Face object is disposed:",p[V].tensor);continue}if((c=e.config.face.detector)!=null&&c.mask){let ae=await hN(p[V]);re(p[V].tensor),p[V].tensor=ae}let ee=p[V].mesh&&p[V].mesh.length>200?cN(p[V],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?i=(f=e.config.face.emotion)!=null&&f.enabled?Vb(p[V].tensor||ct([]),e.config,V,p.length):[]:(e.state="run:emotion",r=oe(),i=(m=e.config.face.emotion)!=null&&m.enabled?await Vb(p[V].tensor||ct([]),e.config,V,p.length):[],e.performance.emotion=ce.perfadd?(e.performance.emotion||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=(g=e.config.face.antispoof)!=null&&g.enabled?vb(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:antispoof",r=oe(),l=(y=e.config.face.antispoof)!=null&&y.enabled?await vb(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.antispoof=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?u=(A=e.config.face.liveness)!=null&&A.enabled?d5(p[V].tensor||ct([]),e.config,V,p.length):0:(e.state="run:liveness",r=oe(),u=(x=e.config.face.liveness)!=null&&x.enabled?await d5(p[V].tensor||ct([]),e.config,V,p.length):0,e.performance.liveness=ce.perfadd?(e.performance.antispoof||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?a=(b=e.config.face.gear)!=null&&b.enabled?fb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:gear",r=oe(),a=(v=e.config.face.gear)!=null&&v.enabled?await fb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.gear=Math.trunc(oe()-r)),e.analyze("End GEAR:"),e.analyze("Start 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Description:"),e.config.async?d=(I=e.config.face.description)!=null&&I.enabled?Zb(p[V].tensor||ct([]),e.config,V,p.length):null:(e.state="run:description",r=oe(),d=(O=e.config.face.description)!=null&&O.enabled?await Zb(p[V].tensor||ct([]),e.config,V,p.length):null,e.performance.description=ce.perfadd?(e.performance.description||0)+Math.trunc(oe()-r):Math.trunc(oe()-r)),e.analyze("End Description:"),e.config.async&&([n,s,i,o,d,a,l,u]=await Promise.all([n,s,i,o,d,a,l,u])),e.analyze("Finish Face:"),((z=e.config.face.ssrnet)==null?void 0:z.enabled)&&n&&s&&(d={...d,age:n.age,gender:s.gender,genderScore:s.genderScore}),((j=e.config.face.gear)==null?void 0:j.enabled)&&a&&(d={...d,age:a.age,gender:a.gender,genderScore:a.genderScore,race:a.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&o&&(d.descriptor=o),(D=e.config.face.iris)!=null&&D.enabled;let Y=p[V].annotations&&p[V].annotations.leftEyeIris&&p[V].annotations.leftEyeIris[0]&&p[V].annotations.rightEyeIris&&p[V].annotations.rightEyeIris[0]&&p[V].annotations.leftEyeIris.length>0&&p[V].annotations.rightEyeIris.length>0&&p[V].annotations.leftEyeIris[0]!==null&&p[V].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[V].annotations.leftEyeIris[3][0]-p[V].annotations.leftEyeIris[1][0]),Math.abs(p[V].annotations.rightEyeIris[4][1]-p[V].annotations.rightEyeIris[2][1]))/t.shape[2]:0,se=(Q=e.config.face.detector)!=null&&Q.return?et(p[V].tensor):null;re(p[V].tensor),p[V].tensor&&delete p[V].tensor;let Z={...p[V],id:V};d!=null&&d.age&&(Z.age=d.age),d!=null&&d.gender&&(Z.gender=d.gender),d!=null&&d.genderScore&&(Z.genderScore=d==null?void 0:d.genderScore),d!=null&&d.descriptor&&(Z.embedding=d==null?void 0:d.descriptor),d!=null&&d.race&&(Z.race=d==null?void 0:d.race),i&&(Z.emotion=i),l&&(Z.real=l),u&&(Z.live=u),Y&&Y!==0&&(Z.iris=Math.trunc(500/Y/11.7)/100),ee&&(Z.rotation=ee),se&&(Z.tensor=se),h.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),h};var fN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){let n=e[r].keypoints.find(l=>l.part==="leftWrist"),a=e[r].keypoints.find(l=>l.part==="rightWrist"),s=e[r].keypoints.find(l=>l.part==="nose");s&&n&&a&&n.position[1]<s.position[1]&&a.position[1]<s.position[1]?t.push({body:r,gesture:"i give up"}):s&&n&&n.position[1]<s.position[1]?t.push({body:r,gesture:"raise left hand"}):s&&a&&a.position[1]<s.position[1]&&t.push({body:r,gesture:"raise right hand"});let i=e[r].keypoints.find(l=>l.part==="leftShoulder"),o=e[r].keypoints.find(l=>l.part==="rightShoulder");i&&o&&Math.abs(i.positionRaw[1]-o.positionRaw[1])>.1&&t.push({body:r,gesture:`leaning ${i.position[1]>o.position[1]?"left":"right"}`})}return t},mN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++)if(e[r].mesh&&e[r].mesh.length>450){let n=(e[r].mesh[33][2]||0)-(e[r].mesh[263][2]||0),a=e[r].mesh[33][0]-e[r].mesh[263][0];Math.abs(n/a)<=.15?t.push({face:r,gesture:"facing center"}):t.push({face:r,gesture:`facing ${n<0?"left":"right"}`}),Math.abs(e[r].mesh[374][1]-e[r].mesh[386][1])/Math.abs(e[r].mesh[443][1]-e[r].mesh[450][1])<.2&&t.push({face:r,gesture:"blink left eye"}),Math.abs(e[r].mesh[145][1]-e[r].mesh[159][1])/Math.abs(e[r].mesh[223][1]-e[r].mesh[230][1])<.2&&t.push({face:r,gesture:"blink right eye"});let o=Math.min(100,500*Math.abs(e[r].mesh[13][1]-e[r].mesh[14][1])/Math.abs(e[r].mesh[10][1]-e[r].mesh[152][1]));o>10&&t.push({face:r,gesture:`mouth ${Math.trunc(o)}% open`});let l=e[r].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:r,gesture:`head ${l<0?"up":"down"}`})}return t},gN=e=>{if(!e)return[];let t=[];for(let r=0;r<e.length;r++){if(!e[r].annotations||!e[r].annotations.leftEyeIris||!e[r].annotations.leftEyeIris[0]||!e[r].annotations.rightEyeIris||!e[r].annotations.rightEyeIris[0])continue;let n=e[r].annotations.leftEyeIris[3][0]-e[r].annotations.leftEyeIris[1][0],a=e[r].annotations.leftEyeIris[4][1]-e[r].annotations.leftEyeIris[2][1],s=Math.abs(n*a),i=e[r].annotations.rightEyeIris[3][0]-e[r].annotations.rightEyeIris[1][0],o=e[r].annotations.rightEyeIris[4][1]-e[r].annotations.rightEyeIris[2][1],l=Math.abs(i*o),u=!1;Math.abs(s-l)/Math.max(s,l)<.25&&(u=!0,t.push({iris:r,gesture:"facing center"}));let h=Math.abs(e[r].mesh[263][0]-e[r].annotations.leftEyeIris[0][0])/e[r].box[2],p=Math.abs(e[r].mesh[33][0]-e[r].annotations.rightEyeIris[0][0])/e[r].box[2];(h>.06||p>.06)&&(u=!1),h>p?h>.05&&t.push({iris:r,gesture:"looking 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N5,this.draw={options:ds,canvas:(r,n)=>dN(r,n),face:(r,n,a)=>$5(r,n,a),body:(r,n,a)=>P5(r,n,a),hand:(r,n,a)=>_5(r,n,a),gesture:(r,n,a)=>F5(r,n,a),object:(r,n,a)=>z5(r,n,a),person:(r,n,a)=>uN(r,n,a),all:(r,n,a)=>pN(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=e9,this.faceUVMap=t9,this.gl=Nt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(gs)),this.config.backend=t}validate(t){return N1(gs,t||this.config)}now(){return oe()}image(t,r=!0){return Sd(t,this.config,r)}async segmentation(t,r){return eN(t,r,this.config)}enhance(t){return Zb(t)}compare(t,r){return VC(this.config,t,r)}async init(){await q0(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=vr(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await q0(this)||ie("error: backend check failed"),await td(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await rN(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await nN(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return AN(t,this.config)}async warmup(t){let r=oe(),n=await kN(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X,D,Q,V,ee,Y;this.state="config";let a;this.config=vr(this.config,r),this.state="check";let s=tp(this,Y0).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await q0(this),await this.load(),a=oe(),this.state="image";let o=await Sd(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await WC(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?D5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await D5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?vr(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?u=this.config.body.enabled?S5(o.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?u=this.config.body.enabled?Pb(o.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("efficientpose"))?u=this.config.body.enabled?Wb(o.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("movenet"))&&(u=this.config.body.enabled?y5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?u=this.config.body.enabled?await S5(o.tensor,p):[]:((v=this.config.body.modelPath)==null?void 0:v.includes("blazepose"))?u=this.config.body.enabled?await Pb(o.tensor,p):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("efficientpose"))?u=this.config.body.enabled?await Wb(o.tensor,p):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("movenet"))&&(u=this.config.body.enabled?await y5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?vr(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handdetect"))?d=this.config.hand.enabled?s5(o.tensor,c):[]:((M=(_=this.config.hand.detector)==null?void 0:_.modelPath)==null?void 0:M.includes("handtrack"))&&(d=this.config.hand.enabled?u5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),((O=(I=this.config.hand.detector)==null?void 0:I.modelPath)==null?void 0:O.includes("handdetect"))?d=this.config.hand.enabled?await s5(o.tensor,c):[]:((j=(z=this.config.hand.detector)==null?void 0:z.modelPath)==null?void 0:j.includes("handtrack"))&&(d=this.config.hand.enabled?await u5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?h=this.config.object.enabled?x5(o.tensor,this.config):[]:((D=this.config.object.modelPath)==null?void 0:D.includes("centernet"))&&(h=this.config.object.enabled?Ob(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),((Q=this.config.object.modelPath)==null?void 0:Q.includes("nanodet"))?h=this.config.object.enabled?await x5(o.tensor,this.config):[]:((V=this.config.object.modelPath)==null?void 0:V.includes("centernet"))&&(h=this.config.object.enabled?await Ob(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...mN(l),...fN(u),...yN(d),...gN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((Y=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Y.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return wN(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Wd=new WeakMap,Kh=new WeakMap,Xh=new WeakMap,Y0=new WeakMap;return IE(gAe);})();
+2Q==`;async function hAe(e){let t=(a,s="application/octet-stream")=>fetch(`data:${s};base64,${a}`).then(i=>i.blob()),r,n;switch(e.config.warmup){case"face":r=await t(K0);break;case"body":case"full":r=await t(X0);break;default:r=null}if(r){let a=await createImageBitmap(r);n=await e.detect(a,e.config),a.close()}return n}async function cAe(e){return new Promise(t=>{let r;switch(e.config.warmup){case"face":r="data:image/jpeg;base64,"+K0;break;case"full":case"body":r="data:image/jpeg;base64,"+X0;break;default:r=null}let n;if(typeof Image!="undefined")n=new Image;else if(ce.Image)n=new ce.Image;else return;n.onload=async()=>{let a=Hr(n.naturalWidth,n.naturalHeight);if(!a)ie("Warmup: Canvas not found"),t(void 0);else{let s=a.getContext("2d");s&&s.drawImage(n,0,0);let i=await e.image(a),o=await e.detect(i.tensor,e.config);t(o)}},r?n.src=r:t(void 0)})}async function fAe(e){let t=a=>Buffer.from(a,"base64"),r;e.config.warmup==="face"?r=t(K0):r=t(X0);let n;if("node"in Ue){let a=(void 0).decodeJpeg(r),s=a.expandDims(0);e.tf.dispose(a),n=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&ie("Warmup tfjs-node not loaded");return n}async function kN(e,t){let r=oe();if(e.state="warmup",t&&(e.config=vr(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:oe(),persons:[],error:null};let n;return new Promise(async a=>{typeof createImageBitmap=="function"?n=await hAe(e):typeof Image!="undefined"||ce.Canvas!==void 0?n=await cAe(e):n=await fAe(e);let s=oe();e.config.debug&&ie("Warmup",e.config.warmup,Math.round(s-r),"ms"),e.emit("warmup"),a(n)})}var Bd,qh,Kh,Z0,IN=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");tp(this,Bd,void 0);tp(this,qh,void 0);tp(this,Kh,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!ep(this,qh))return;let r=this.tf.engine().state.numTensors,n=ep(this,Bd);rp(this,Bd,r);let a=r-n;a!==0&&ie(...t,a)});tp(this,Z0,t=>{if(!ep(this,Kh))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof rt))return"input must be a tensor";try{this.tf.getBackend()}catch(r){return"backend not loaded"}return null});fe(this,"similarity",bN);fe(this,"distance",q0);fe(this,"match",vN);fe(this,"emit",t=>{var r;this.events&&this.events.dispatchEvent&&((r=this.events)==null||r.dispatchEvent(new Event(t)))});this.env=ce,gs.wasmPath=zh["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${u2}/dist/`,gs.modelBasePath=ce.browser?"../models/":"file://models/",gs.backend=ce.browser?"humangl":"tensorflow",this.version=pb,Object.defineProperty(this,"version",{value:pb}),this.config=JSON.parse(JSON.stringify(gs)),Object.seal(this.config),t&&(this.config=vr(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",GC(this.config),this.tf=Ue,this.state="idle",rp(this,Bd,0),rp(this,qh,!1),rp(this,Kh,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new T5,this.draw={options:ds,canvas:(r,n)=>dN(r,n),face:(r,n,a)=>F5(r,n,a),body:(r,n,a)=>$5(r,n,a),hand:(r,n,a)=>P5(r,n,a),gesture:(r,n,a)=>M5(r,n,a),object:(r,n,a)=>_5(r,n,a),person:(r,n,a)=>uN(r,n,a),all:(r,n,a)=>pN(r,n,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=e9,this.faceUVMap=t9,this.gl=Nt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(gs)),this.config.backend=t}validate(t){return T1(gs,t||this.config)}now(){return oe()}image(t,r=!0){return Id(t,this.config,r)}async segmentation(t,r){return eN(t,r,this.config)}enhance(t){return Xb(t)}compare(t,r){return VC(this.config,t,r)}async init(){await H0(this,!0),await this.tf.ready()}async load(t){this.state="load";let r=oe(),n=Object.values(this.models).filter(i=>i).length;t&&(this.config=vr(this.config,t)),this.env.initial&&(this.config.debug&&ie(`version: ${this.version}`),this.config.debug&&ie(`tfjs version: ${this.tf.version["tfjs-core"]}`),await H0(this)||ie("error: backend check failed"),await ed(),this.env.browser&&(this.config.debug&&ie("configuration:",this.config),this.config.debug&&ie("environment:",this.env),this.config.debug&&ie("tf flags:",this.tf.ENV.flags))),await rN(this),this.env.initial&&this.config.debug&&ie("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(i=>i).length!==n&&(await nN(this),this.emit("load"));let s=Math.trunc(oe()-r);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return AN(t,this.config)}async warmup(t){let r=oe(),n=await kN(this,t),a=oe();return this.performance.warmup=Math.trunc(a-r),n}async profile(t,r){let n=await this.tf.profile(()=>this.detect(t,r)),a={};for(let o of n.kernels)a[o.name]?a[o.name]+=o.kernelTimeMs:a[o.name]=o.kernelTimeMs;let s=[];Object.entries(a).forEach(o=>s.push({name:o[0],ms:o[1]})),s.sort((o,l)=>l.ms-o.ms),s.length=20;let i={};for(let o of s)i[o.name]=o.ms;return i}async detect(t,r){return this.state="detect",new Promise(async n=>{var g,y,A,x,b,v,C,S,E,R,_,M,I,O,z,j,X,D,Q,V,ee,Y;this.state="config";let a;this.config=vr(this.config,r),this.state="check";let s=ep(this,Z0).call(this,t);s&&(ie(s,t),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:s}));let i=oe();await H0(this),await this.load(),a=oe(),this.state="image";let o=await Id(t,this.config);if(this.process=o,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Get Image:"),!o.tensor){this.config.debug&&ie("could not convert input to tensor"),this.emit("error"),n({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:oe(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),a=oe(),this.config.skipAllowed=await WC(this.config,o.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(oe()-a):Math.trunc(oe()-a),this.analyze("Check Changed:");let l=[],u=[],d=[],h=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?O5(this,o.tensor):[],this.performance.face&&delete this.performance.face):(a=oe(),l=this.config.face.enabled?await O5(this,o.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?vr(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?I5(o.tensor,p):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?$b(o.tensor,p):[]:(A=this.config.body.modelPath)!=null&&A.includes("efficientpose")?u=this.config.body.enabled?Bb(o.tensor,p):[]:(x=this.config.body.modelPath)!=null&&x.includes("movenet")&&(u=this.config.body.enabled?g5(o.tensor,p):[]),this.performance.body&&delete this.performance.body):(a=oe(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await I5(o.tensor,p):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?u=this.config.body.enabled?await $b(o.tensor,p):[]:(C=this.config.body.modelPath)!=null&&C.includes("efficientpose")?u=this.config.body.enabled?await Bb(o.tensor,p):[]:(S=this.config.body.modelPath)!=null&&S.includes("movenet")&&(u=this.config.body.enabled?await g5(o.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let c=this.config.hand.maxDetected===-1?vr(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&R.includes("handdetect")?d=this.config.hand.enabled?a5(o.tensor,c):[]:(M=(_=this.config.hand.detector)==null?void 0:_.modelPath)!=null&&M.includes("handtrack")&&(d=this.config.hand.enabled?l5(o.tensor,c):[]),this.performance.hand&&delete this.performance.hand):(a=oe(),(O=(I=this.config.hand.detector)==null?void 0:I.modelPath)!=null&&O.includes("handdetect")?d=this.config.hand.enabled?await a5(o.tensor,c):[]:(j=(z=this.config.hand.detector)==null?void 0:z.modelPath)!=null&&j.includes("handtrack")&&(d=this.config.hand.enabled?await l5(o.tensor,c):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?h=this.config.object.enabled?A5(o.tensor,this.config):[]:(D=this.config.object.modelPath)!=null&&D.includes("centernet")&&(h=this.config.object.enabled?zb(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(a=oe(),(Q=this.config.object.modelPath)!=null&&Q.includes("nanodet")?h=this.config.object.enabled?await A5(o.tensor,this.config):[]:(V=this.config.object.modelPath)!=null&&V.includes("centernet")&&(h=this.config.object.enabled?await zb(o.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,d,h]=await Promise.all([l,u,d,h])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(a=oe(),f=[...mN(l),...fN(u),...yN(d),...gN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(oe()-a):Math.trunc(oe()-a)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(oe()-i):Math.trunc(oe()-i);let m=((Y=(ee=this.process)==null?void 0:ee.tensor)==null?void 0:Y.shape)||[];this.result={face:l,body:u,hand:d,gesture:f,object:h,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return wN(l,u,d,f,m)}},re(o.tensor),this.emit("detect"),this.state="idle",n(this.result)})}};Bd=new WeakMap,qh=new WeakMap,Kh=new WeakMap,Z0=new WeakMap;return IE(gAe);})();
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index ea79461c..3fc88ee5 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -96,7 +96,7 @@ var Dr=Object.create;var $e=Object.defineProperty;var qr=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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function OA(e){var t;return T.initial&&(q0=null),q0?e.debug&&b("cached model:",q0.modelUrl):q0=await G((t=e.face.detector)==null?void 0:t.modelPath),xe=q0.inputs[0].shape?q0.inputs[0].shape[2]:0,st=N.scalar(xe,"int32"),CA=N.tensor2d(EA(xe)),q0}function v2(e){let t={};t.boxStarts=N.slice(e,[0,1],[-1,2]),t.centers=N.add(t.boxStarts,CA),t.boxSizes=N.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=N.div(t.boxSizes,st),t.centersNormalized=N.div(t.centers,st),t.halfBoxSize=N.div(t.boxSizesNormalized,H.tf2),t.starts=N.sub(t.centersNormalized,t.halfBoxSize),t.ends=N.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=N.mul(t.starts,st),t.endNormalized=N.mul(t.ends,st);let o=N.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(A=>N.dispose(t[A])),o}async function IA(e,t){var a,y,c,i;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=N.image.resizeBilinear(e,[xe,xe]),o.div=N.div(o.resized,H.tf127),o.normalized=N.sub(o.div,H.tf05);let A=q0==null?void 0:q0.execute(o.normalized);if(Array.isArray(A)){let l=A.sort((x,d)=>x.size-d.size);o.concat384=N.concat([l[0],l[2]],2),o.concat512=N.concat([l[1],l[3]],2),o.concat=N.concat([o.concat512,o.concat384],1),o.batch=N.squeeze(o.concat,0)}else o.batch=N.squeeze(A);N.dispose(A),o.boxes=v2(o.batch),o.logits=N.slice(o.batch,[0,0],[-1,1]),o.sigmoid=N.sigmoid(o.logits),o.scores=N.squeeze(o.sigmoid),o.nms=await N.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let n=await o.nms.array(),r=[],s=await o.scores.data();for(let l=0;l<n.length;l++){let x=s[n[l]];if(x>(((i=t.face.detector)==null?void 0:i.minConfidence)||0)){let d={};d.bbox=N.slice(o.boxes,[n[l],0],[1,-1]),d.slice=N.slice(o.batch,[n[l],WA-1],[1,-1]),d.squeeze=N.squeeze(d.slice),d.landmarks=N.reshape(d.squeeze,[WA,-1]);let u=await d.bbox.data(),f={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await d.landmarks.array(),confidence:x},M=RA(f,[(e.shape[2]||0)/xe,(e.shape[1]||0)/xe]),h=Rt(M,t.face.scale||M2),P=wt(h);r.push(P),Object.keys(d).forEach(m=>N.dispose(d[m]))}}return Object.keys(o).forEach(l=>N.dispose(o[l])),r}var k0=V(F());var Et={};mt(Et,{connected:()=>w5,kpt:()=>R5});var R5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],w5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var R0=V(F()),LA=224,T2,R2=5,zt=[8,16,32,32,32];async function BA(){let e=[],t=0;for(;t<R2;){let o=0,A=t;for(;A<zt.length&&zt[A]===zt[t];)o+=2,A++;let n=zt[t],r=Math.ceil(LA/n),s=Math.ceil(LA/n);for(let a=0;a<r;++a)for(let y=0;y<s;++y)for(let c=0;c<o;++c)e.push({x:(y+.5)/s,y:(a+.5)/r});t=A}T2={x:R0.tensor1d(e.map(o=>o.x)),y:R0.tensor1d(e.map(o=>o.y))}}function ee(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[A[0],A[1],n[0]-A[0],n[1]-A[1]],s=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{box:r,boxRaw:s}}function GA(e,t=[1,1]){let o=[e.map(c=>c[0]),e.map(c=>c[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[(A[0]+n[0])/2,(A[1]+n[1])/2],s=Math.max(r[0]-A[0],r[1]-A[1],-r[0]+n[0],-r[1]+n[1]),a=[Math.trunc(r[0]-s),Math.trunc(r[1]-s),Math.trunc(2*s),Math.trunc(2*s)],y=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:y}}function jt(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var VA={initial:!0},w0={detector:null,landmarks:null},Ie={detector:[224,224],landmarks:[256,256]},k5=Number.MAX_SAFE_INTEGER,k2={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Wt=null,at,ce=[[0,0],[0,0],[0,0],[0,0]],HA=0,FA=e=>1-1/(1+Math.exp(e));async function ZA(e){if(VA.initial&&(w0.detector=null),!w0.detector&&e.body.detector&&e.body.detector.modelPath){w0.detector=await G(e.body.detector.modelPath);let t=Object.values(w0.detector.modelSignature.inputs);Ie.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&w0.detector&&b("cached model:",w0.detector.modelUrl);return await BA(),w0.detector}async function XA(e){if(VA.initial&&(w0.landmarks=null),w0.landmarks)e.debug&&b("cached model:",w0.landmarks.modelUrl);else{w0.landmarks=await G(e.body.modelPath);let t=Object.values(w0.landmarks.modelSignature.inputs);Ie.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return w0.landmarks}async function E2(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let A;if(at&&(o.cropped=k0.image.cropAndResize(e,[at],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],r=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ce=[[0,0],n,r,[0,0]],o.pad=k0.pad(o.cropped||e,ce),o.resize=k0.image.resizeBilinear(o.pad,[t,t]),A=k0.div(o.resize,H.tf255)}else e.shape[1]!==t?(o.resize=k0.image.resizeBilinear(o.cropped||e,[t,t]),A=k0.div(o.resize,H.tf255)):A=k0.div(o.cropped||e,H.tf255);return Object.keys(o).forEach(n=>k0.dispose(o[n])),A}function z2(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ce[2][0]+ce[2][1])/t[0]-ce[2][0]),Math.trunc(o.position[1]*(t[1]+ce[1][0]+ce[1][1])/t[1]-ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(at)for(let o of e)o.positionRaw=[o.positionRaw[0]+at[1],o.positionRaw[1]+at[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function j2(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),A=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(A.position[2]||0))/2;let n=e.find(a=>a.part==="rightPalm"),r=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");n.position[2]=((r.position[2]||0)+(s.position[2]||0))/2}async function S2(e,t,o){var u;let A={};[A.ld,A.segmentation,A.heatmap,A.world,A.poseflag]=(u=w0.landmarks)==null?void 0:u.execute(e,k2.landmarks);let n=(await A.poseflag.data())[0],r=await A.ld.data(),s=await A.world.data();Object.keys(A).forEach(f=>k0.dispose(A[f]));let a=[],y=5;for(let f=0;f<r.length/y;f++){let M=FA(r[y*f+3]),h=FA(r[y*f+4]),P=Math.trunc(100*M*h*n)/100,m=[r[y*f+0]/Ie.landmarks[0],r[y*f+1]/Ie.landmarks[1],r[y*f+2]+0],p=[Math.trunc(o[0]*m[0]),Math.trunc(o[1]*m[1]),m[2]],k=[s[y*f+0],s[y*f+1],s[y*f+2]+0];a.push({part:R5[f],positionRaw:m,position:p,distance:k,score:P})}if(n<(t.body.minConfidence||0))return null;j2(a);let c=z2(a,o),i=c.map(f=>f.position),l=ee(i,[o[0],o[1]]),x={};for(let[f,M]of Object.entries(w5)){let h=[];for(let P=0;P<M.length-1;P++){let m=c.find(k=>k.part===M[P]),p=c.find(k=>k.part===M[P+1]);m&&p&&h.push([m.position,p.position])}x[f]=h}return{id:0,score:Math.trunc(100*n)/100,box:l.box,boxRaw:l.boxRaw,keypoints:c,annotations:x}}async function E5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],A=(t.body.skipTime||0)>g()-HA,n=k5<(t.body.skipFrames||0);if(t.skipAllowed&&A&&n&&Wt!==null)k5++;else{let r={};r.landmarks=await E2(e,256),Wt=await S2(r.landmarks,t,o),Object.keys(r).forEach(s=>k0.dispose(r[s])),HA=g(),k5=0}return Wt?[Wt]:[]}var b0=V(F());var Ne=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var te,ve=0,z5=[],qA=0,j5=Number.MAX_SAFE_INTEGER;async function UA(e){if(T.initial&&(te=null),te)e.debug&&b("cached model:",te.modelUrl);else{te=await G(e.object.modelPath);let t=Object.values(te.modelSignature.inputs);ve=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return te}async function W2(e,t,o){if(!e)return[];let A={},n=[],r=await e.array();A.squeeze=b0.squeeze(e);let s=b0.split(A.squeeze,6,1);A.stack=b0.stack([s[1],s[0],s[3],s[2]],1),A.boxes=b0.squeeze(A.stack),A.scores=b0.squeeze(s[4]),A.classes=b0.squeeze(s[5]),b0.dispose([e,...s]),A.nms=await b0.image.nonMaxSuppressionAsync(A.boxes,A.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await A.nms.data(),y=0;for(let c of Array.from(a)){let i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Ne[l].label,[d,u]=[r[0][c][0]/ve,r[0][c][1]/ve],f=[d,u,r[0][c][2]/ve-d,r[0][c][3]/ve-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function S5(e,t){let o=(t.object.skipTime||0)>g()-qA,A=j5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&z5.length>0?(j5++,z5):(j5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[ve,ve]),a=t.object.enabled?te==null?void 0:te.execute(s,["tower_0/detections"]):null;qA=g(),b0.dispose(s);let y=await W2(a,r,t);z5=y,n(y)}))}var K=V(F());var Ct={};mt(Ct,{connected:()=>C5,kpt:()=>W5});var 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n=N5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-$A;return t.skipAllowed&&r&&n&&_A===A&&Ot[o]&&Ot[o].length>0?(N5++,Ot[o]):(N5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)==null?void 0:i.enabled){let x={},d=(O0==null?void 0:O0.inputs[0].shape)?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),$A=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:O2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Ot[o]=c,_A=A,y(c)}))}var rn=V(F());var E0,B5=[],on=0,An=0,nn=Number.MAX_SAFE_INTEGER;async function sn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function G5(e,t,o,A){var s,a;if(!E0)return[];let n=nn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&n&&on===A&&B5[o]?(nn++,B5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=rn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}B5[o]=c,on=A,An=g(),y(c)})}var He=V(F());var G0=V(F());var oe,de=0,I2=2.3,H5=L0.leftEyeLower0,F5=L0.rightEyeLower0,Le={leftBounds:[H5[0],H5[H5.length-1]],rightBounds:[F5[0],F5[F5.length-1]]},Be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function cn(e){var t;return T.initial&&(oe=null),oe?e.debug&&b("cached model:",oe.modelUrl):oe=await G((t=e.face.iris)==null?void 0:t.modelPath),de=oe.inputs[0].shape?oe.inputs[0].shape[2]:0,de===-1&&(de=64),oe}function It(e,t,o,A){for(let n=0;n<h5.length;n++){let{key:r,indices:s}=h5[n],a=L0[`${o}${r}`];if(!A||A.includes(r))for(let y=0;y<s.length;y++){let c=s[y];e[a[y]]=[t[c][0],t[c][1],(t[c][2]+e[a[y]][2])/2]}}}var N2=e=>{let t=e[Le.leftBounds[0]][2],o=e[Le.rightBounds[0]][2];return t-o},ln=(e,t,o,A,n,r=!1)=>{let s=wt(Rt(wA([e[o],e[A]]),I2)),a=Oe(s),y=G0.image.cropAndResize(t,[[s.startPoint[1]/n,s.startPoint[0]/n,s.endPoint[1]/n,s.endPoint[0]/n]],[0],[de,de]);if(r&&T.kernels.includes("flipleftright")){let c=G0.image.flipLeftRight(y);G0.dispose(y),y=c}return{box:s,boxSize:a,crop:y}},yn=(e,t,o,A=!1)=>{let n=[];for(let r=0;r<Be.numCoordinates;r++){let s=e[r*3],a=e[r*3+1],y=e[r*3+2];n.push([(A?1-s/de:s/de)*o[0]+t.startPoint[0],a/de*o[1]+t.startPoint[1],y])}return{rawCoords:n,iris:n.slice(Be.index)}},xn=(e,t,o)=>{let A=e[L0[`${o}EyeUpper0`][Be.upperCenter]][2],n=e[L0[`${o}EyeLower0`][Be.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function dn(e,t,o,A){if(!oe)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=ln(e,t,Le.leftBounds[0],Le.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=ln(e,t,Le.rightBounds[0],Le.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=oe.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,Be.numCoordinates*3),{rawCoords:u,iris:f}=yn(d,n,r,!0),M=x.slice(Be.numCoordinates*3),{rawCoords:h,iris:P}=yn(M,a,y),m=N2(e);Math.abs(m)<30?(It(e,u,"left",null),It(e,h,"right",null)):m<1?It(e,u,"left",["EyeUpper0","EyeLower0"]):It(e,h,"right",["EyeUpper0","EyeLower0"]);let p=xn(e,f,"left"),k=xn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Ae=null,Ge=0;async function mn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await IA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=jA((c=t.face.detector)==null?void 0:c.rotation,h,e,((i=t.face.mesh)==null?void 0:i.enabled)?Ge:kt()),(l=t==null?void 0:t.filter)==null?void 0:l.equalization){let k=await pt(p.tensor);He.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)==null?void 0:x.enabled)if(!Ae)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=Ae.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=He.reshape(O,[-1,3]),J=await 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it(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Rn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Tn.image.cropAndResize(t,r,[0],o)}function wn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Bt(e,t=1.5){let o=it(e),A=Lt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Gt(e){let t=it(e),o=Lt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function L2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function kn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return L2(o)}var Mn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function fe(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function vn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(fe(e[n],B2(t,r)))}return o}function U5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Mn(t[0],t[1]),s=vn(r,n),a=Mn(-t[0],-t[1]);return vn(s,a)}function En(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-fe(t[0],o),-fe(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function J5(e,t){return[fe(e,t[0]),fe(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=wn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var F2=5,Sn=1.65,Wn=[0,5,9,13,17,1,2],V2=0,Z2=2,Cn=0,K5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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Object.keys(Un))A.annotations[l]=Un[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function Ao(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])==null?void 0:n.inputs[0].shape)||!((r=l0[1])==null?void 0:r.inputs[0].shape))return[];se=[e.shape[2]||0,e.shape[1]||0],Ft++;let o=(t.hand.skipTime||0)>g()-to,A=Ft<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-to,y=Ft<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>oo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>oo(e,i,t))):(o0.boxes=await os(e,t),to=g(),o0.hands=await Promise.all(o0.boxes.map(i=>oo(e,i,t))),Ft=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let 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a=e.keypoints[A]?[Math.abs(e.keypoints[r].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0])]:[0,0],y=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[r].position[0]-e.keypoints[n].position[0])]:[0,0];if(a[0]>a[1]||y[0]>y[1]){let c=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=c}}}function or(e){for(let t=0;t<e.length;t++)if(e[t]&&S0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-S0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-S0.keypoints[t].positionRaw[1])];o[0]<tr&&o[1]<tr?e[t]=S0.keypoints[t]:S0.keypoints[t]=e[t]}else S0.keypoints[t]=e[t];return e}function Ar(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;S0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ge.pad(e,S0.padding),o.resize=ge.image.resizeBilinear(o.pad,[t,t]);let A=ge.cast(o.resize,"int32");return Object.keys(o).forEach(n=>ge.dispose(o[n])),A}function nr(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+S0.padding[2][0]+S0.padding[2][1])/t[0]-S0.padding[2][0],A.position[1]*(t[1]+S0.padding[1][0]+S0.padding[1][1])/t[1]-S0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=ee(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,Ut=0,yo=Number.MAX_SAFE_INTEGER,Ee={boxes:[],bodies:[],last:0};async function sr(e){return T.initial&&(W0=null),W0?e.debug&&b("cached model:",W0.modelUrl):(Vt(["size"],e),W0=await G(e.body.modelPath)),Ut=W0.inputs[0].shape?W0.inputs[0].shape[2]:0,Ut<64&&(Ut=256),W0}async function ns(e,t,o){let A=e[0][0],n=[],r=0;for(let i=0;i<A.length;i++)if(r=A[i][2],r>t.body.minConfidence){let l=[A[i][1],A[i][0]];n.push({score:Math.round(100*r)/100,part:Dt[i],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}r=n.reduce((i,l)=>l.score>i?l.score:i,0);let s=[],a=ee(n.map(i=>i.position),[o.shape[2],o.shape[1]]),y={};for(let[i,l]of Object.entries(qt)){let x=[];for(let d=0;d<l.length-1;d++){let u=n.find(M=>M.part===l[d]),f=n.find(M=>M.part===l[d+1]);u&&f&&u.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&x.push([u.position,f.position])}y[i]=x}let c={id:0,score:r,box:a.box,boxRaw:a.boxRaw,keypoints:n,annotations:y};return lo(c),s.push(c),s}async function rs(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let r=e[0][n],s=Math.round(100*r[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Dt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=ee(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(qt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};lo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function xo(e,t){if(!W0||!(W0==null?void 0:W0.inputs[0].shape))return[];t.skipAllowed||(Ee.boxes.length=0),yo++;let o=(t.body.skipTime||0)>g()-Ee.last,A=yo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ee.bodies:new Promise(async n=>{let r={};yo=0,r.input=Ar(e,Ut),r.res=W0==null?void 0:W0.execute(r.input),Ee.last=g();let s=await r.res.array();Ee.bodies=r.res.shape[2]===17?await ns(s,t,e):await rs(s,t,e);for(let a of Ee.bodies)nr(a,[e.shape[2]||1,e.shape[1]||1]),or(a.keypoints);Object.keys(r).forEach(a=>rr.dispose(r[a])),n(Ee.bodies)})}var M0=V(F());var qe,Jt=[],ir=0,co=Number.MAX_SAFE_INTEGER,Kt=0,Yt=2.5;async function lr(e){if(!qe||T.initial){qe=await G(e.object.modelPath);let t=Object.values(qe.modelSignature.inputs);Kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",qe.modelUrl);return qe}async function ss(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ne.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ne.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/Kt)),[k,C]=[P-Yt/y*p[0],m-Yt/y*p[1]],[O,j]=[P+Yt/y*p[2]-k,m+Yt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ne[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function fo(e,t){let o=(t.object.skipTime||0)>g()-ir,A=co<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Jt.length>0?(co++,Jt):(co=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Jt:new Promise(async 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xt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],as=xt.length,yt=xt.reduce((e,t,o)=>(e[t]=o,e),{}),is=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=is.map(([e,t])=>[yt[e],yt[t]]),xr=[["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"]];function cr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var mo=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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function OA(e){var t;return T.initial&&(q0=null),q0?e.debug&&b("cached model:",q0.modelUrl):q0=await G((t=e.face.detector)==null?void 0:t.modelPath),xe=q0.inputs[0].shape?q0.inputs[0].shape[2]:0,st=N.scalar(xe,"int32"),CA=N.tensor2d(EA(xe)),q0}function v2(e){let t={};t.boxStarts=N.slice(e,[0,1],[-1,2]),t.centers=N.add(t.boxStarts,CA),t.boxSizes=N.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=N.div(t.boxSizes,st),t.centersNormalized=N.div(t.centers,st),t.halfBoxSize=N.div(t.boxSizesNormalized,H.tf2),t.starts=N.sub(t.centersNormalized,t.halfBoxSize),t.ends=N.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=N.mul(t.starts,st),t.endNormalized=N.mul(t.ends,st);let o=N.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(A=>N.dispose(t[A])),o}async function IA(e,t){var a,y,c,i;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=N.image.resizeBilinear(e,[xe,xe]),o.div=N.div(o.resized,H.tf127),o.normalized=N.sub(o.div,H.tf05);let A=q0==null?void 0:q0.execute(o.normalized);if(Array.isArray(A)){let l=A.sort((x,d)=>x.size-d.size);o.concat384=N.concat([l[0],l[2]],2),o.concat512=N.concat([l[1],l[3]],2),o.concat=N.concat([o.concat512,o.concat384],1),o.batch=N.squeeze(o.concat,0)}else o.batch=N.squeeze(A);N.dispose(A),o.boxes=v2(o.batch),o.logits=N.slice(o.batch,[0,0],[-1,1]),o.sigmoid=N.sigmoid(o.logits),o.scores=N.squeeze(o.sigmoid),o.nms=await N.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let n=await o.nms.array(),r=[],s=await o.scores.data();for(let l=0;l<n.length;l++){let x=s[n[l]];if(x>(((i=t.face.detector)==null?void 0:i.minConfidence)||0)){let d={};d.bbox=N.slice(o.boxes,[n[l],0],[1,-1]),d.slice=N.slice(o.batch,[n[l],WA-1],[1,-1]),d.squeeze=N.squeeze(d.slice),d.landmarks=N.reshape(d.squeeze,[WA,-1]);let u=await d.bbox.data(),f={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await d.landmarks.array(),confidence:x},M=RA(f,[(e.shape[2]||0)/xe,(e.shape[1]||0)/xe]),h=Rt(M,t.face.scale||M2),P=wt(h);r.push(P),Object.keys(d).forEach(m=>N.dispose(d[m]))}}return Object.keys(o).forEach(l=>N.dispose(o[l])),r}var k0=V(F());var Et={};mt(Et,{connected:()=>w5,kpt:()=>R5});var R5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],w5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var R0=V(F()),LA=224,T2,R2=5,zt=[8,16,32,32,32];async function BA(){let e=[],t=0;for(;t<R2;){let o=0,A=t;for(;A<zt.length&&zt[A]===zt[t];)o+=2,A++;let n=zt[t],r=Math.ceil(LA/n),s=Math.ceil(LA/n);for(let a=0;a<r;++a)for(let y=0;y<s;++y)for(let c=0;c<o;++c)e.push({x:(y+.5)/s,y:(a+.5)/r});t=A}T2={x:R0.tensor1d(e.map(o=>o.x)),y:R0.tensor1d(e.map(o=>o.y))}}function ee(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[A[0],A[1],n[0]-A[0],n[1]-A[1]],s=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{box:r,boxRaw:s}}function GA(e,t=[1,1]){let o=[e.map(c=>c[0]),e.map(c=>c[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[(A[0]+n[0])/2,(A[1]+n[1])/2],s=Math.max(r[0]-A[0],r[1]-A[1],-r[0]+n[0],-r[1]+n[1]),a=[Math.trunc(r[0]-s),Math.trunc(r[1]-s),Math.trunc(2*s),Math.trunc(2*s)],y=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:y}}function jt(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var VA={initial:!0},w0={detector:null,landmarks:null},Ie={detector:[224,224],landmarks:[256,256]},k5=Number.MAX_SAFE_INTEGER,k2={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Wt=null,at,ce=[[0,0],[0,0],[0,0],[0,0]],HA=0,FA=e=>1-1/(1+Math.exp(e));async function ZA(e){if(VA.initial&&(w0.detector=null),!w0.detector&&e.body.detector&&e.body.detector.modelPath){w0.detector=await G(e.body.detector.modelPath);let t=Object.values(w0.detector.modelSignature.inputs);Ie.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&w0.detector&&b("cached model:",w0.detector.modelUrl);return await BA(),w0.detector}async function XA(e){if(VA.initial&&(w0.landmarks=null),w0.landmarks)e.debug&&b("cached model:",w0.landmarks.modelUrl);else{w0.landmarks=await G(e.body.modelPath);let t=Object.values(w0.landmarks.modelSignature.inputs);Ie.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return w0.landmarks}async function E2(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let A;if(at&&(o.cropped=k0.image.cropAndResize(e,[at],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],r=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ce=[[0,0],n,r,[0,0]],o.pad=k0.pad(o.cropped||e,ce),o.resize=k0.image.resizeBilinear(o.pad,[t,t]),A=k0.div(o.resize,H.tf255)}else e.shape[1]!==t?(o.resize=k0.image.resizeBilinear(o.cropped||e,[t,t]),A=k0.div(o.resize,H.tf255)):A=k0.div(o.cropped||e,H.tf255);return Object.keys(o).forEach(n=>k0.dispose(o[n])),A}function z2(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ce[2][0]+ce[2][1])/t[0]-ce[2][0]),Math.trunc(o.position[1]*(t[1]+ce[1][0]+ce[1][1])/t[1]-ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(at)for(let o of e)o.positionRaw=[o.positionRaw[0]+at[1],o.positionRaw[1]+at[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function j2(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),A=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(A.position[2]||0))/2;let n=e.find(a=>a.part==="rightPalm"),r=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");n.position[2]=((r.position[2]||0)+(s.position[2]||0))/2}async function S2(e,t,o){var u;let A={};[A.ld,A.segmentation,A.heatmap,A.world,A.poseflag]=(u=w0.landmarks)==null?void 0:u.execute(e,k2.landmarks);let n=(await A.poseflag.data())[0],r=await A.ld.data(),s=await A.world.data();Object.keys(A).forEach(f=>k0.dispose(A[f]));let a=[],y=5;for(let f=0;f<r.length/y;f++){let M=FA(r[y*f+3]),h=FA(r[y*f+4]),P=Math.trunc(100*M*h*n)/100,m=[r[y*f+0]/Ie.landmarks[0],r[y*f+1]/Ie.landmarks[1],r[y*f+2]+0],p=[Math.trunc(o[0]*m[0]),Math.trunc(o[1]*m[1]),m[2]],k=[s[y*f+0],s[y*f+1],s[y*f+2]+0];a.push({part:R5[f],positionRaw:m,position:p,distance:k,score:P})}if(n<(t.body.minConfidence||0))return null;j2(a);let c=z2(a,o),i=c.map(f=>f.position),l=ee(i,[o[0],o[1]]),x={};for(let[f,M]of Object.entries(w5)){let h=[];for(let P=0;P<M.length-1;P++){let m=c.find(k=>k.part===M[P]),p=c.find(k=>k.part===M[P+1]);m&&p&&h.push([m.position,p.position])}x[f]=h}return{id:0,score:Math.trunc(100*n)/100,box:l.box,boxRaw:l.boxRaw,keypoints:c,annotations:x}}async function E5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],A=(t.body.skipTime||0)>g()-HA,n=k5<(t.body.skipFrames||0);if(t.skipAllowed&&A&&n&&Wt!==null)k5++;else{let r={};r.landmarks=await E2(e,256),Wt=await S2(r.landmarks,t,o),Object.keys(r).forEach(s=>k0.dispose(r[s])),HA=g(),k5=0}return Wt?[Wt]:[]}var b0=V(F());var Ne=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var te,ve=0,z5=[],qA=0,j5=Number.MAX_SAFE_INTEGER;async function UA(e){if(T.initial&&(te=null),te)e.debug&&b("cached model:",te.modelUrl);else{te=await G(e.object.modelPath);let t=Object.values(te.modelSignature.inputs);ve=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return te}async function W2(e,t,o){if(!e)return[];let A={},n=[],r=await e.array();A.squeeze=b0.squeeze(e);let s=b0.split(A.squeeze,6,1);A.stack=b0.stack([s[1],s[0],s[3],s[2]],1),A.boxes=b0.squeeze(A.stack),A.scores=b0.squeeze(s[4]),A.classes=b0.squeeze(s[5]),b0.dispose([e,...s]),A.nms=await b0.image.nonMaxSuppressionAsync(A.boxes,A.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await A.nms.data(),y=0;for(let c of Array.from(a)){let i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Ne[l].label,[d,u]=[r[0][c][0]/ve,r[0][c][1]/ve],f=[d,u,r[0][c][2]/ve-d,r[0][c][3]/ve-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function S5(e,t){let o=(t.object.skipTime||0)>g()-qA,A=j5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&z5.length>0?(j5++,z5):(j5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[ve,ve]),a=t.object.enabled?te==null?void 0:te.execute(s,["tower_0/detections"]):null;qA=g(),b0.dispose(s);let y=await W2(a,r,t);z5=y,n(y)}))}var K=V(F());var Ct={};mt(Ct,{connected:()=>C5,kpt:()=>W5});var 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n=N5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-$A;return t.skipAllowed&&r&&n&&_A===A&&Ot[o]&&Ot[o].length>0?(N5++,Ot[o]):(N5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),$A=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:O2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Ot[o]=c,_A=A,y(c)}))}var rn=V(F());var E0,B5=[],on=0,An=0,nn=Number.MAX_SAFE_INTEGER;async function sn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function G5(e,t,o,A){var s,a;if(!E0)return[];let n=nn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&n&&on===A&&B5[o]?(nn++,B5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=rn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}B5[o]=c,on=A,An=g(),y(c)})}var He=V(F());var G0=V(F());var oe,de=0,I2=2.3,H5=L0.leftEyeLower0,F5=L0.rightEyeLower0,Le={leftBounds:[H5[0],H5[H5.length-1]],rightBounds:[F5[0],F5[F5.length-1]]},Be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function cn(e){var t;return T.initial&&(oe=null),oe?e.debug&&b("cached model:",oe.modelUrl):oe=await G((t=e.face.iris)==null?void 0:t.modelPath),de=oe.inputs[0].shape?oe.inputs[0].shape[2]:0,de===-1&&(de=64),oe}function It(e,t,o,A){for(let n=0;n<h5.length;n++){let{key:r,indices:s}=h5[n],a=L0[`${o}${r}`];if(!A||A.includes(r))for(let y=0;y<s.length;y++){let c=s[y];e[a[y]]=[t[c][0],t[c][1],(t[c][2]+e[a[y]][2])/2]}}}var N2=e=>{let t=e[Le.leftBounds[0]][2],o=e[Le.rightBounds[0]][2];return t-o},ln=(e,t,o,A,n,r=!1)=>{let s=wt(Rt(wA([e[o],e[A]]),I2)),a=Oe(s),y=G0.image.cropAndResize(t,[[s.startPoint[1]/n,s.startPoint[0]/n,s.endPoint[1]/n,s.endPoint[0]/n]],[0],[de,de]);if(r&&T.kernels.includes("flipleftright")){let c=G0.image.flipLeftRight(y);G0.dispose(y),y=c}return{box:s,boxSize:a,crop:y}},yn=(e,t,o,A=!1)=>{let n=[];for(let r=0;r<Be.numCoordinates;r++){let s=e[r*3],a=e[r*3+1],y=e[r*3+2];n.push([(A?1-s/de:s/de)*o[0]+t.startPoint[0],a/de*o[1]+t.startPoint[1],y])}return{rawCoords:n,iris:n.slice(Be.index)}},xn=(e,t,o)=>{let A=e[L0[`${o}EyeUpper0`][Be.upperCenter]][2],n=e[L0[`${o}EyeLower0`][Be.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function dn(e,t,o,A){if(!oe)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=ln(e,t,Le.leftBounds[0],Le.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=ln(e,t,Le.rightBounds[0],Le.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=oe.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,Be.numCoordinates*3),{rawCoords:u,iris:f}=yn(d,n,r,!0),M=x.slice(Be.numCoordinates*3),{rawCoords:h,iris:P}=yn(M,a,y),m=N2(e);Math.abs(m)<30?(It(e,u,"left",null),It(e,h,"right",null)):m<1?It(e,u,"left",["EyeUpper0","EyeLower0"]):It(e,h,"right",["EyeUpper0","EyeLower0"]);let p=xn(e,f,"left"),k=xn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Ae=null,Ge=0;async function mn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await IA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=jA((c=t.face.detector)==null?void 0:c.rotation,h,e,(i=t.face.mesh)!=null&&i.enabled?Ge:kt()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let k=await pt(p.tensor);He.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!Ae)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=Ae.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=He.reshape(O,[-1,3]),J=await 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it(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Rn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Tn.image.cropAndResize(t,r,[0],o)}function wn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Bt(e,t=1.5){let o=it(e),A=Lt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Gt(e){let t=it(e),o=Lt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function L2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function kn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return L2(o)}var Mn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function fe(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function vn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(fe(e[n],B2(t,r)))}return o}function U5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Mn(t[0],t[1]),s=vn(r,n),a=Mn(-t[0],-t[1]);return vn(s,a)}function En(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-fe(t[0],o),-fe(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function J5(e,t){return[fe(e,t[0]),fe(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=wn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var F2=5,Sn=1.65,Wn=[0,5,9,13,17,1,2],V2=0,Z2=2,Cn=0,K5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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e;S0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ge.pad(e,S0.padding),o.resize=ge.image.resizeBilinear(o.pad,[t,t]);let A=ge.cast(o.resize,"int32");return Object.keys(o).forEach(n=>ge.dispose(o[n])),A}function nr(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+S0.padding[2][0]+S0.padding[2][1])/t[0]-S0.padding[2][0],A.position[1]*(t[1]+S0.padding[1][0]+S0.padding[1][1])/t[1]-S0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=ee(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,Ut=0,yo=Number.MAX_SAFE_INTEGER,Ee={boxes:[],bodies:[],last:0};async function sr(e){return T.initial&&(W0=null),W0?e.debug&&b("cached 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a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Dt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=ee(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(qt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};lo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function xo(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(Ee.boxes.length=0),yo++;let o=(t.body.skipTime||0)>g()-Ee.last,A=yo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ee.bodies:new Promise(async n=>{let r={};yo=0,r.input=Ar(e,Ut),r.res=W0==null?void 0:W0.execute(r.input),Ee.last=g();let s=await r.res.array();Ee.bodies=r.res.shape[2]===17?await ns(s,t,e):await rs(s,t,e);for(let a of Ee.bodies)nr(a,[e.shape[2]||1,e.shape[1]||1]),or(a.keypoints);Object.keys(r).forEach(a=>rr.dispose(r[a])),n(Ee.bodies)})}var M0=V(F());var qe,Jt=[],ir=0,co=Number.MAX_SAFE_INTEGER,Kt=0,Yt=2.5;async function lr(e){if(!qe||T.initial){qe=await G(e.object.modelPath);let t=Object.values(qe.modelSignature.inputs);Kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",qe.modelUrl);return qe}async function ss(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ne.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ne.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/Kt)),[k,C]=[P-Yt/y*p[0],m-Yt/y*p[1]],[O,j]=[P+Yt/y*p[2]-k,m+Yt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ne[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function fo(e,t){let o=(t.object.skipTime||0)>g()-ir,A=co<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Jt.length>0?(co++,Jt):(co=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Jt:new Promise(async 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xt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],as=xt.length,yt=xt.reduce((e,t,o)=>(e[t]=o,e),{}),is=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=is.map(([e,t])=>[yt[e],yt[t]]),xr=[["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"]];function cr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var mo=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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l=Math.abs(i.annotations.leftEyeIris[3][0]-i.annotations.leftEyeIris[1][0])/2,x=Math.abs(i.annotations.leftEyeIris[4][1]-i.annotations.leftEyeIris[2][1])/2;n.ellipse(i.annotations.leftEyeIris[0][0],i.annotations.leftEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(i.annotations&&i.annotations.rightEyeIris&&i.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let l=Math.abs(i.annotations.rightEyeIris[3][0]-i.annotations.rightEyeIris[1][0])/2,x=Math.abs(i.annotations.rightEyeIris[4][1]-i.annotations.rightEyeIris[2][1])/2;n.ellipse(i.annotations.rightEyeIris[0][0],i.annotations.rightEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((r=i.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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             C
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@@ -108,7 +108,7 @@ var Dr=Object.create;var $e=Object.defineProperty;var qr=Object.getOwnPropertyDe
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Description:"),e.config.async?i=((q=e.config.face.description)==null?void 0:q.enabled)?D5(x[w].tensor||A0.tensor([]),e.config,w,x.length):null:(e.state="run:description",o=g(),i=((x0=e.config.face.description)==null?void 0:x0.enabled)?await D5(x[w].tensor||A0.tensor([]),e.config,w,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([A,r,s,a,i,n,y,c]=await Promise.all([A,r,s,a,i,n,y,c])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&A&&r&&(i={...i,age:A.age,gender:r.gender,genderScore:r.genderScore}),((C0=e.config.face.gear)==null?void 0:C0.enabled)&&n&&(i={...i,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((ie=e.config.face.mobilefacenet)==null?void 0:ie.enabled)&&a&&(i.descriptor=a),!((v=e.config.face.iris)==null?void 0:v.enabled);let n0=x[w].annotations&&x[w].annotations.leftEyeIris&&x[w].annotations.leftEyeIris[0]&&x[w].annotations.rightEyeIris&&x[w].annotations.rightEyeIris[0]&&x[w].annotations.leftEyeIris.length>0&&x[w].annotations.rightEyeIris.length>0&&x[w].annotations.leftEyeIris[0]!==null&&x[w].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[w].annotations.leftEyeIris[3][0]-x[w].annotations.leftEyeIris[1][0]),Math.abs(x[w].annotations.rightEyeIris[4][1]-x[w].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=((a0=e.config.face.detector)==null?void 0:a0.return)?A0.squeeze(x[w].tensor):null;A0.dispose(x[w].tensor),x[w].tensor&&delete x[w].tensor;let I={...x[w],id:w};(i==null?void 0:i.age)&&(I.age=i.age),(i==null?void 0:i.gender)&&(I.gender=i.gender),(i==null?void 0:i.genderScore)&&(I.genderScore=i==null?void 0:i.genderScore),(i==null?void 0:i.descriptor)&&(I.embedding=i==null?void 0:i.descriptor),(i==null?void 0:i.race)&&(I.race=i==null?void 0:i.race),s&&(I.emotion=s),y&&(I.real=y),c&&(I.live=c),n0&&n0!==0&&(I.iris=Math.trunc(500/n0/11.7)/100),Q&&(I.rotation=Q),y0&&(I.tensor=y0),l.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Or=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(y=>y.part==="leftWrist"),n=e[o].keypoints.find(y=>y.part==="rightWrist"),r=e[o].keypoints.find(y=>y.part==="nose");r&&A&&n&&A.position[1]<r.position[1]&&n.position[1]<r.position[1]?t.push({body:o,gesture:"i give up"}):r&&A&&A.position[1]<r.position[1]?t.push({body:o,gesture:"raise left hand"}):r&&n&&n.position[1]<r.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(y=>y.part==="leftShoulder"),a=e[o].keypoints.find(y=>y.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Ir=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let y=e[o].mesh[152][2]||0;Math.abs(y)>10&&t.push({face:o,gesture:`head ${y<0?"up":"down"}`})}return t},Nr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],r=Math.abs(A*n),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],y=Math.abs(s*a),c=!1;Math.abs(r-y)/Math.max(r,y)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(c=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let d=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||d<.01||u>.022||d>.022)&&(c=!1),(u<.01||d<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||d>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},Lr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,r]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(r)&&r[0]&&A.push({name:n.toLowerCase(),position:r[0]});if(A&&A.length>0){let n=A.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${n.name} forward`});let r=A.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${r.name} up`})}if(e[o].keypoints){let n=Fn(e[o].keypoints);for(let r of n)t.push({hand:o,gesture:r.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},Oo=0;function Br(e,t){var s,a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie;let o=g();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let A=Date.now()-e.timestamp,n=A<1e3?8-Math.log(A+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let a0=e.body[v].box.map((I,U)=>((n-1)*z.body[v].box[U]+I)/n),w=e.body[v].boxRaw.map((I,U)=>((n-1)*z.body[v].boxRaw[U]+I)/n),Q=e.body[v].keypoints.map((I,U)=>{var 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Object.keys(e.hand[v].annotations))n0[y0]=e.hand[v].annotations[y0]&&e.hand[v].annotations[y0][0]?e.hand[v].annotations[y0].map((I,U)=>I.map((Y0,K0)=>((n-1)*z.hand[v].annotations[y0][U][K0]+Y0)/n)):null;z.hand[v]={...e.hand[v],box:a0,boxRaw:w,keypoints:Q,annotations:n0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let a0=e.face[v].box.map((Q,n0)=>((n-1)*z.face[v].box[n0]+Q)/n),w=e.face[v].boxRaw.map((Q,n0)=>((n-1)*z.face[v].boxRaw[n0]+Q)/n);if(e.face[v].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(x=e.face[v].rotation)==null?void 0:x.matrix,Q.angle={roll:((n-1)*(((u=(d=z.face[v].rotation)==null?void 0:d.angle)==null?void 0:u.roll)||0)+(((M=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:M.roll)||0))/n,yaw:((n-1)*(((P=(h=z.face[v].rotation)==null?void 0:h.angle)==null?void 0:P.yaw)||0)+(((p=(m=e.face[v].rotation)==null?void 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v=e.persons;if(!z.persons||v.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(v));else for(let a0=0;a0<v.length;a0++)z.persons[a0].box=v[a0].box.map((w,Q)=>((n-1)*z.persons[a0].box[Q]+w)/n)}e.gesture&&(z.gesture=e.gesture);let r=g();return Oo=T.perfadd?Oo+Math.round(r-o):Math.round(r-o),e.performance&&(z.performance={...e.performance,interpolate:Oo}),z}function $t(e,t,o={order:2,multiplier:25}){let A=0;for(let n=0;n<e.length;n++){let r=!o.order||o.order===2?e[n]-t[n]:Math.abs(e[n]-t[n]);A+=!o.order||o.order===2?r*r:r**o.order}return(o.multiplier||20)*A}var Gr=(e,t,o,A)=>{if(e===0)return 1;let n=t===2?Math.sqrt(e):e**(1/t),r=(1-n/100-o)/(A-o);return Math.max(Math.min(r,1),0)};function Hr(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let A=$t(e,t,o);return Gr(A,o.order||2,o.min||0,o.max||1)}function 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n0=x[w].annotations&&x[w].annotations.leftEyeIris&&x[w].annotations.leftEyeIris[0]&&x[w].annotations.rightEyeIris&&x[w].annotations.rightEyeIris[0]&&x[w].annotations.leftEyeIris.length>0&&x[w].annotations.rightEyeIris.length>0&&x[w].annotations.leftEyeIris[0]!==null&&x[w].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[w].annotations.leftEyeIris[3][0]-x[w].annotations.leftEyeIris[1][0]),Math.abs(x[w].annotations.rightEyeIris[4][1]-x[w].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=(a0=e.config.face.detector)!=null&&a0.return?A0.squeeze(x[w].tensor):null;A0.dispose(x[w].tensor),x[w].tensor&&delete x[w].tensor;let I={...x[w],id:w};i!=null&&i.age&&(I.age=i.age),i!=null&&i.gender&&(I.gender=i.gender),i!=null&&i.genderScore&&(I.genderScore=i==null?void 0:i.genderScore),i!=null&&i.descriptor&&(I.embedding=i==null?void 0:i.descriptor),i!=null&&i.race&&(I.race=i==null?void 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vo,this.draw={options:ae,canvas:(o,A)=>zr(o,A),face:(o,A,n)=>Eo(o,A,n),body:(o,A,n)=>zo(o,A,n),hand:(o,A,n)=>jo(o,A,n),gesture:(o,A,n)=>ko(o,A,n),object:(o,A,n)=>So(o,A,n),person:(o,A,n)=>Er(o,A,n),all:(o,A,n)=>jr(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=un,this.faceUVMap=hn,this.gl=$,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(le)),this.config.backend=t}validate(t){return A5(le,t||this.config)}now(){return g()}image(t,o=!0){return We(t,this.config,o)}async segmentation(t,o){return br(t,o,this.config)}enhance(t){return X5(t)}compare(t,o){return AA(this.config,t,o)}async init(){await _t(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=g(),A=Object.values(this.models).filter(s=>s).length;t&&(this.config=c0(this.config,t)),this.env.initial&&(this.config.debug&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`),await _t(this)||b("error: backend check failed"),await D0.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("environment:",this.env),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await Pr(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==A&&(await Mr(this),this.emit("load"));let r=Math.trunc(g()-o);r>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+r:r)}next(t=this.result){return Br(t,this.config)}async warmup(t){let o=g(),A=await Zr(this,t),n=g();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let a of A.kernels)n[a.name]?n[a.name]+=a.kernelTimeMs:n[a.name]=a.kernelTimeMs;let r=[];Object.entries(n).forEach(a=>r.push({name:a[0],ms:a[1]})),r.sort((a,y)=>y.ms-a.ms),r.length=20;let s={};for(let a of r)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async A=>{var M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie,v,a0,w,Q,n0;this.state="config";let n;this.config=c0(this.config,o),this.state="check";let r=et(this,o5).call(this,t);r&&(b(r,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:r}));let s=g();await _t(this),await this.load(),n=g(),this.state="image";let a=await We(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=g(),this.config.skipAllowed=await oA(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Check Changed:");let y=[],c=[],i=[],l=[];this.state="detect:face",this.config.async?(y=this.config.face.enabled?Co(this,a.tensor):[],this.performance.face&&delete this.performance.face):(n=g(),y=this.config.face.enabled?await Co(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(y=await y),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?c0(this.config,{body:{maxDetected:this.config.face.enabled?1*y.length:1}}):this.config;this.config.async?(((M=this.config.body.modelPath)==null?void 0:M.includes("posenet"))?c=this.config.body.enabled?go(a.tensor,x):[]:((h=this.config.body.modelPath)==null?void 0:h.includes("blazepose"))?c=this.config.body.enabled?E5(a.tensor,x):[]:((P=this.config.body.modelPath)==null?void 0:P.includes("efficientpose"))?c=this.config.body.enabled?I5(a.tensor,x):[]:((m=this.config.body.modelPath)==null?void 0:m.includes("movenet"))&&(c=this.config.body.enabled?xo(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(n=g(),((p=this.config.body.modelPath)==null?void 0:p.includes("posenet"))?c=this.config.body.enabled?await go(a.tensor,x):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?c=this.config.body.enabled?await E5(a.tensor,x):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("efficientpose"))?c=this.config.body.enabled?await I5(a.tensor,x):[]:((O=this.config.body.modelPath)==null?void 0:O.includes("movenet"))&&(c=this.config.body.enabled?await xo(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?c0(this.config,{hand:{maxDetected:this.config.face.enabled?2*y.length:1}}):this.config;this.config.async?(((L=(j=this.config.hand.detector)==null?void 0:j.modelPath)==null?void 0:L.includes("handdetect"))?i=this.config.hand.enabled?$5(a.tensor,d):[]:((Z=(J=this.config.hand.detector)==null?void 0:J.modelPath)==null?void 0:Z.includes("handtrack"))&&(i=this.config.hand.enabled?Ao(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),((x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)==null?void 0:x0.includes("handdetect"))?i=this.config.hand.enabled?await $5(a.tensor,d):[]:((C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:C0.includes("handtrack"))&&(i=this.config.hand.enabled?await Ao(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((ie=this.config.object.modelPath)==null?void 0:ie.includes("nanodet"))?l=this.config.object.enabled?fo(a.tensor,this.config):[]:((v=this.config.object.modelPath)==null?void 0:v.includes("centernet"))&&(l=this.config.object.enabled?S5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),((a0=this.config.object.modelPath)==null?void 0:a0.includes("nanodet"))?l=this.config.object.enabled?await fo(a.tensor,this.config):[]:((w=this.config.object.modelPath)==null?void 0:w.includes("centernet"))&&(l=this.config.object.enabled?await S5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Ir(y),...Or(c),...Lr(i),...Nr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Vr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Ke=new WeakMap,dt=new WeakMap,ft=new WeakMap,o5=new WeakMap;module.exports=_r(Is);0&&(module.exports={Human,defaults,env});
+2Q==`;var Io=V(F());async function Ss(e){let t=(n,r="application/octet-stream")=>fetch(`data:${r};base64,${n}`).then(s=>s.blob()),o,A;switch(e.config.warmup){case"face":o=await t(e5);break;case"body":case"full":o=await t(t5);break;default:o=null}if(o){let n=await createImageBitmap(o);A=await e.detect(n,e.config),n.close()}return A}async function Ws(e){return new Promise(t=>{let o;switch(e.config.warmup){case"face":o="data:image/jpeg;base64,"+e5;break;case"full":case"body":o="data:image/jpeg;base64,"+t5;break;default:o=null}let A;if(typeof Image!="undefined")A=new Image;else if(T.Image)A=new T.Image;else return;A.onload=async()=>{let n=h0(A.naturalWidth,A.naturalHeight);if(!n)b("Warmup: Canvas not found"),t(void 0);else{let r=n.getContext("2d");r&&r.drawImage(A,0,0);let s=await e.image(n),a=await e.detect(s.tensor,e.config);t(a)}},o?A.src=o:t(void 0)})}async function Cs(e){let t=n=>Buffer.from(n,"base64"),o;e.config.warmup==="face"?o=t(e5):o=t(t5);let A;if("node"in Io){let n=Io.node.decodeJpeg(o),r=n.expandDims(0);e.tf.dispose(n),A=await e.detect(r,e.config),e.tf.dispose(r)}else e.config.debug&&b("Warmup tfjs-node not loaded");return A}async function Zr(e,t){let o=g();if(e.state="warmup",t&&(e.config=c0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:g(),persons:[],error:null};let A;return new Promise(async n=>{typeof createImageBitmap=="function"?A=await Ss(e):typeof Image!="undefined"||T.Canvas!==void 0?A=await Ws(e):A=await Cs(e);let r=g();e.config.debug&&b("Warmup",e.config.warmup,Math.round(r-o),"ms"),e.emit("warmup"),n(A)})}var Ke,dt,ft,o5,Xr=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");tt(this,Ke,void 0);tt(this,dt,void 0);tt(this,ft,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!et(this,dt))return;let o=this.tf.engine().state.numTensors,A=et(this,Ke);ot(this,Ke,o);let n=o-A;n!==0&&b(...t,n)});tt(this,o5,t=>{if(!et(this,ft))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof D0.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});R(this,"similarity",Hr);R(this,"distance",$t);R(this,"match",Fr);R(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});this.env=T,le.wasmPath=D0.version["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${D0.version_core}/dist/`,le.modelBasePath=T.browser?"../models/":"file://models/",le.backend=T.browser?"humangl":"tensorflow",this.version=s5,Object.defineProperty(this,"version",{value:s5}),this.config=JSON.parse(JSON.stringify(le)),Object.seal(this.config),t&&(this.config=c0(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",rA(this.config),this.tf=D0,this.state="idle",ot(this,Ke,0),ot(this,dt,!1),ot(this,ft,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new vo,this.draw={options:ae,canvas:(o,A)=>zr(o,A),face:(o,A,n)=>Eo(o,A,n),body:(o,A,n)=>zo(o,A,n),hand:(o,A,n)=>jo(o,A,n),gesture:(o,A,n)=>ko(o,A,n),object:(o,A,n)=>So(o,A,n),person:(o,A,n)=>Er(o,A,n),all:(o,A,n)=>jr(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=un,this.faceUVMap=hn,this.gl=$,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(le)),this.config.backend=t}validate(t){return A5(le,t||this.config)}now(){return g()}image(t,o=!0){return We(t,this.config,o)}async segmentation(t,o){return br(t,o,this.config)}enhance(t){return X5(t)}compare(t,o){return AA(this.config,t,o)}async init(){await _t(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=g(),A=Object.values(this.models).filter(s=>s).length;t&&(this.config=c0(this.config,t)),this.env.initial&&(this.config.debug&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`),await _t(this)||b("error: backend check failed"),await D0.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("environment:",this.env),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await Pr(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==A&&(await Mr(this),this.emit("load"));let r=Math.trunc(g()-o);r>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+r:r)}next(t=this.result){return Br(t,this.config)}async warmup(t){let o=g(),A=await Zr(this,t),n=g();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let a of A.kernels)n[a.name]?n[a.name]+=a.kernelTimeMs:n[a.name]=a.kernelTimeMs;let r=[];Object.entries(n).forEach(a=>r.push({name:a[0],ms:a[1]})),r.sort((a,y)=>y.ms-a.ms),r.length=20;let s={};for(let a of r)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async A=>{var M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie,v,a0,w,Q,n0;this.state="config";let n;this.config=c0(this.config,o),this.state="check";let r=et(this,o5).call(this,t);r&&(b(r,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:r}));let s=g();await _t(this),await this.load(),n=g(),this.state="image";let a=await We(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=g(),this.config.skipAllowed=await oA(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Check Changed:");let y=[],c=[],i=[],l=[];this.state="detect:face",this.config.async?(y=this.config.face.enabled?Co(this,a.tensor):[],this.performance.face&&delete this.performance.face):(n=g(),y=this.config.face.enabled?await Co(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(y=await y),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?c0(this.config,{body:{maxDetected:this.config.face.enabled?1*y.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?c=this.config.body.enabled?go(a.tensor,x):[]:(h=this.config.body.modelPath)!=null&&h.includes("blazepose")?c=this.config.body.enabled?E5(a.tensor,x):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?I5(a.tensor,x):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?xo(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(n=g(),(p=this.config.body.modelPath)!=null&&p.includes("posenet")?c=this.config.body.enabled?await go(a.tensor,x):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await E5(a.tensor,x):[]:(C=this.config.body.modelPath)!=null&&C.includes("efficientpose")?c=this.config.body.enabled?await I5(a.tensor,x):[]:(O=this.config.body.modelPath)!=null&&O.includes("movenet")&&(c=this.config.body.enabled?await xo(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?c0(this.config,{hand:{maxDetected:this.config.face.enabled?2*y.length:1}}):this.config;this.config.async?((L=(j=this.config.hand.detector)==null?void 0:j.modelPath)!=null&&L.includes("handdetect")?i=this.config.hand.enabled?$5(a.tensor,d):[]:(Z=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&Z.includes("handtrack")&&(i=this.config.hand.enabled?Ao(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),(x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&x0.includes("handdetect")?i=this.config.hand.enabled?await $5(a.tensor,d):[]:(C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&C0.includes("handtrack")&&(i=this.config.hand.enabled?await Ao(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((ie=this.config.object.modelPath)!=null&&ie.includes("nanodet")?l=this.config.object.enabled?fo(a.tensor,this.config):[]:(v=this.config.object.modelPath)!=null&&v.includes("centernet")&&(l=this.config.object.enabled?S5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),(a0=this.config.object.modelPath)!=null&&a0.includes("nanodet")?l=this.config.object.enabled?await fo(a.tensor,this.config):[]:(w=this.config.object.modelPath)!=null&&w.includes("centernet")&&(l=this.config.object.enabled?await S5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Ir(y),...Or(c),...Lr(i),...Nr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Vr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Ke=new WeakMap,dt=new WeakMap,ft=new WeakMap,o5=new WeakMap;module.exports=_r(Is);0&&(module.exports={Human,defaults,env});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
index 38567980..89f2bc9f 100644
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@@ -96,7 +96,7 @@ var Dr=Object.create;var $e=Object.defineProperty;var qr=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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function OA(e){var t;return T.initial&&(q0=null),q0?e.debug&&b("cached model:",q0.modelUrl):q0=await G((t=e.face.detector)==null?void 0:t.modelPath),xe=q0.inputs[0].shape?q0.inputs[0].shape[2]:0,st=N.scalar(xe,"int32"),CA=N.tensor2d(EA(xe)),q0}function v2(e){let t={};t.boxStarts=N.slice(e,[0,1],[-1,2]),t.centers=N.add(t.boxStarts,CA),t.boxSizes=N.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=N.div(t.boxSizes,st),t.centersNormalized=N.div(t.centers,st),t.halfBoxSize=N.div(t.boxSizesNormalized,H.tf2),t.starts=N.sub(t.centersNormalized,t.halfBoxSize),t.ends=N.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=N.mul(t.starts,st),t.endNormalized=N.mul(t.ends,st);let o=N.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(A=>N.dispose(t[A])),o}async function IA(e,t){var a,y,c,i;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=N.image.resizeBilinear(e,[xe,xe]),o.div=N.div(o.resized,H.tf127),o.normalized=N.sub(o.div,H.tf05);let A=q0==null?void 0:q0.execute(o.normalized);if(Array.isArray(A)){let l=A.sort((x,d)=>x.size-d.size);o.concat384=N.concat([l[0],l[2]],2),o.concat512=N.concat([l[1],l[3]],2),o.concat=N.concat([o.concat512,o.concat384],1),o.batch=N.squeeze(o.concat,0)}else o.batch=N.squeeze(A);N.dispose(A),o.boxes=v2(o.batch),o.logits=N.slice(o.batch,[0,0],[-1,1]),o.sigmoid=N.sigmoid(o.logits),o.scores=N.squeeze(o.sigmoid),o.nms=await N.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let n=await o.nms.array(),r=[],s=await o.scores.data();for(let l=0;l<n.length;l++){let x=s[n[l]];if(x>(((i=t.face.detector)==null?void 0:i.minConfidence)||0)){let d={};d.bbox=N.slice(o.boxes,[n[l],0],[1,-1]),d.slice=N.slice(o.batch,[n[l],WA-1],[1,-1]),d.squeeze=N.squeeze(d.slice),d.landmarks=N.reshape(d.squeeze,[WA,-1]);let u=await d.bbox.data(),f={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await d.landmarks.array(),confidence:x},M=RA(f,[(e.shape[2]||0)/xe,(e.shape[1]||0)/xe]),h=Rt(M,t.face.scale||M2),P=wt(h);r.push(P),Object.keys(d).forEach(m=>N.dispose(d[m]))}}return Object.keys(o).forEach(l=>N.dispose(o[l])),r}var k0=V(F());var Et={};mt(Et,{connected:()=>E5,kpt:()=>k5});var k5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],E5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var R0=V(F()),LA=224,T2,R2=5,zt=[8,16,32,32,32];async function BA(){let e=[],t=0;for(;t<R2;){let o=0,A=t;for(;A<zt.length&&zt[A]===zt[t];)o+=2,A++;let n=zt[t],r=Math.ceil(LA/n),s=Math.ceil(LA/n);for(let a=0;a<r;++a)for(let y=0;y<s;++y)for(let c=0;c<o;++c)e.push({x:(y+.5)/s,y:(a+.5)/r});t=A}T2={x:R0.tensor1d(e.map(o=>o.x)),y:R0.tensor1d(e.map(o=>o.y))}}function ee(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[A[0],A[1],n[0]-A[0],n[1]-A[1]],s=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{box:r,boxRaw:s}}function GA(e,t=[1,1]){let o=[e.map(c=>c[0]),e.map(c=>c[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[(A[0]+n[0])/2,(A[1]+n[1])/2],s=Math.max(r[0]-A[0],r[1]-A[1],-r[0]+n[0],-r[1]+n[1]),a=[Math.trunc(r[0]-s),Math.trunc(r[1]-s),Math.trunc(2*s),Math.trunc(2*s)],y=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:y}}function jt(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var VA={initial:!0},w0={detector:null,landmarks:null},Ie={detector:[224,224],landmarks:[256,256]},z5=Number.MAX_SAFE_INTEGER,k2={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Wt=null,at,ce=[[0,0],[0,0],[0,0],[0,0]],HA=0,FA=e=>1-1/(1+Math.exp(e));async function ZA(e){if(VA.initial&&(w0.detector=null),!w0.detector&&e.body.detector&&e.body.detector.modelPath){w0.detector=await G(e.body.detector.modelPath);let t=Object.values(w0.detector.modelSignature.inputs);Ie.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&w0.detector&&b("cached model:",w0.detector.modelUrl);return await BA(),w0.detector}async function XA(e){if(VA.initial&&(w0.landmarks=null),w0.landmarks)e.debug&&b("cached model:",w0.landmarks.modelUrl);else{w0.landmarks=await G(e.body.modelPath);let t=Object.values(w0.landmarks.modelSignature.inputs);Ie.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return w0.landmarks}async function E2(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let A;if(at&&(o.cropped=k0.image.cropAndResize(e,[at],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],r=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ce=[[0,0],n,r,[0,0]],o.pad=k0.pad(o.cropped||e,ce),o.resize=k0.image.resizeBilinear(o.pad,[t,t]),A=k0.div(o.resize,H.tf255)}else e.shape[1]!==t?(o.resize=k0.image.resizeBilinear(o.cropped||e,[t,t]),A=k0.div(o.resize,H.tf255)):A=k0.div(o.cropped||e,H.tf255);return Object.keys(o).forEach(n=>k0.dispose(o[n])),A}function z2(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ce[2][0]+ce[2][1])/t[0]-ce[2][0]),Math.trunc(o.position[1]*(t[1]+ce[1][0]+ce[1][1])/t[1]-ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(at)for(let o of e)o.positionRaw=[o.positionRaw[0]+at[1],o.positionRaw[1]+at[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function j2(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),A=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(A.position[2]||0))/2;let n=e.find(a=>a.part==="rightPalm"),r=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");n.position[2]=((r.position[2]||0)+(s.position[2]||0))/2}async function S2(e,t,o){var u;let A={};[A.ld,A.segmentation,A.heatmap,A.world,A.poseflag]=(u=w0.landmarks)==null?void 0:u.execute(e,k2.landmarks);let n=(await A.poseflag.data())[0],r=await A.ld.data(),s=await A.world.data();Object.keys(A).forEach(f=>k0.dispose(A[f]));let a=[],y=5;for(let f=0;f<r.length/y;f++){let M=FA(r[y*f+3]),h=FA(r[y*f+4]),P=Math.trunc(100*M*h*n)/100,m=[r[y*f+0]/Ie.landmarks[0],r[y*f+1]/Ie.landmarks[1],r[y*f+2]+0],p=[Math.trunc(o[0]*m[0]),Math.trunc(o[1]*m[1]),m[2]],k=[s[y*f+0],s[y*f+1],s[y*f+2]+0];a.push({part:k5[f],positionRaw:m,position:p,distance:k,score:P})}if(n<(t.body.minConfidence||0))return null;j2(a);let c=z2(a,o),i=c.map(f=>f.position),l=ee(i,[o[0],o[1]]),x={};for(let[f,M]of Object.entries(E5)){let h=[];for(let P=0;P<M.length-1;P++){let m=c.find(k=>k.part===M[P]),p=c.find(k=>k.part===M[P+1]);m&&p&&h.push([m.position,p.position])}x[f]=h}return{id:0,score:Math.trunc(100*n)/100,box:l.box,boxRaw:l.boxRaw,keypoints:c,annotations:x}}async function j5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],A=(t.body.skipTime||0)>g()-HA,n=z5<(t.body.skipFrames||0);if(t.skipAllowed&&A&&n&&Wt!==null)z5++;else{let r={};r.landmarks=await E2(e,256),Wt=await S2(r.landmarks,t,o),Object.keys(r).forEach(s=>k0.dispose(r[s])),HA=g(),z5=0}return Wt?[Wt]:[]}var b0=V(F());var Ne=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var te,ve=0,S5=[],qA=0,W5=Number.MAX_SAFE_INTEGER;async function UA(e){if(T.initial&&(te=null),te)e.debug&&b("cached model:",te.modelUrl);else{te=await G(e.object.modelPath);let t=Object.values(te.modelSignature.inputs);ve=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return te}async function W2(e,t,o){if(!e)return[];let A={},n=[],r=await e.array();A.squeeze=b0.squeeze(e);let s=b0.split(A.squeeze,6,1);A.stack=b0.stack([s[1],s[0],s[3],s[2]],1),A.boxes=b0.squeeze(A.stack),A.scores=b0.squeeze(s[4]),A.classes=b0.squeeze(s[5]),b0.dispose([e,...s]),A.nms=await b0.image.nonMaxSuppressionAsync(A.boxes,A.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await A.nms.data(),y=0;for(let c of Array.from(a)){let i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Ne[l].label,[d,u]=[r[0][c][0]/ve,r[0][c][1]/ve],f=[d,u,r[0][c][2]/ve-d,r[0][c][3]/ve-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function C5(e,t){let o=(t.object.skipTime||0)>g()-qA,A=W5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&S5.length>0?(W5++,S5):(W5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[ve,ve]),a=t.object.enabled?te==null?void 0:te.execute(s,["tower_0/detections"]):null;qA=g(),b0.dispose(s);let y=await W2(a,r,t);S5=y,n(y)}))}var K=V(F());var Ct={};mt(Ct,{connected:()=>I5,kpt:()=>O5});var 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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(I5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var B0=V(F());var O2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Ot=[],_A=0,$A=0,B5=Number.MAX_SAFE_INTEGER;async function en(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await G((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function G5(e,t,o,A){var s,a;if(!O0)return[];let n=B5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-$A;return t.skipAllowed&&r&&n&&_A===A&&Ot[o]&&Ot[o].length>0?(B5++,Ot[o]):(B5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)==null?void 0:i.enabled){let x={},d=(O0==null?void 0:O0.inputs[0].shape)?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),$A=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:O2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Ot[o]=c,_A=A,y(c)}))}var rn=V(F());var E0,H5=[],on=0,An=0,nn=Number.MAX_SAFE_INTEGER;async function sn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function F5(e,t,o,A){var s,a;if(!E0)return[];let n=nn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&n&&on===A&&H5[o]?(nn++,H5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=rn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}H5[o]=c,on=A,An=g(),y(c)})}var He=V(F());var G0=V(F());var oe,de=0,I2=2.3,V5=L0.leftEyeLower0,Z5=L0.rightEyeLower0,Le={leftBounds:[V5[0],V5[V5.length-1]],rightBounds:[Z5[0],Z5[Z5.length-1]]},Be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function cn(e){var t;return T.initial&&(oe=null),oe?e.debug&&b("cached model:",oe.modelUrl):oe=await G((t=e.face.iris)==null?void 0:t.modelPath),de=oe.inputs[0].shape?oe.inputs[0].shape[2]:0,de===-1&&(de=64),oe}function It(e,t,o,A){for(let n=0;n<g5.length;n++){let{key:r,indices:s}=g5[n],a=L0[`${o}${r}`];if(!A||A.includes(r))for(let y=0;y<s.length;y++){let c=s[y];e[a[y]]=[t[c][0],t[c][1],(t[c][2]+e[a[y]][2])/2]}}}var N2=e=>{let t=e[Le.leftBounds[0]][2],o=e[Le.rightBounds[0]][2];return t-o},ln=(e,t,o,A,n,r=!1)=>{let s=wt(Rt(wA([e[o],e[A]]),I2)),a=Oe(s),y=G0.image.cropAndResize(t,[[s.startPoint[1]/n,s.startPoint[0]/n,s.endPoint[1]/n,s.endPoint[0]/n]],[0],[de,de]);if(r&&T.kernels.includes("flipleftright")){let c=G0.image.flipLeftRight(y);G0.dispose(y),y=c}return{box:s,boxSize:a,crop:y}},yn=(e,t,o,A=!1)=>{let n=[];for(let r=0;r<Be.numCoordinates;r++){let s=e[r*3],a=e[r*3+1],y=e[r*3+2];n.push([(A?1-s/de:s/de)*o[0]+t.startPoint[0],a/de*o[1]+t.startPoint[1],y])}return{rawCoords:n,iris:n.slice(Be.index)}},xn=(e,t,o)=>{let A=e[L0[`${o}EyeUpper0`][Be.upperCenter]][2],n=e[L0[`${o}EyeLower0`][Be.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function dn(e,t,o,A){if(!oe)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=ln(e,t,Le.leftBounds[0],Le.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=ln(e,t,Le.rightBounds[0],Le.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=oe.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,Be.numCoordinates*3),{rawCoords:u,iris:f}=yn(d,n,r,!0),M=x.slice(Be.numCoordinates*3),{rawCoords:h,iris:P}=yn(M,a,y),m=N2(e);Math.abs(m)<30?(It(e,u,"left",null),It(e,h,"right",null)):m<1?It(e,u,"left",["EyeUpper0","EyeLower0"]):It(e,h,"right",["EyeUpper0","EyeLower0"]);let p=xn(e,f,"left"),k=xn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Ae=null,Ge=0;async function mn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await IA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=jA((c=t.face.detector)==null?void 0:c.rotation,h,e,((i=t.face.mesh)==null?void 0:i.enabled)?Ge:kt()),(l=t==null?void 0:t.filter)==null?void 0:l.equalization){let k=await pt(p.tensor);He.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)==null?void 0:x.enabled)if(!Ae)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=Ae.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=He.reshape(O,[-1,3]),J=await L.array();if(He.dispose([O,L,C,k]),p.faceScore<(((d=t.face.detector)==null?void 0:d.minConfidence)||1))h.confidence=p.faceScore;else{((u=t.face.iris)==null?void 0:u.enabled)&&(J=await dn(J,p.tensor,t,Ge)),p.mesh=zA(J,h,P,m,Ge),p.meshRaw=p.mesh.map(q=>[q[0]/(e.shape[2]||0),q[1]/(e.shape[1]||0),(q[2]||0)/Ge]);for(let q of Object.keys(L0))p.annotations[q]=L0[q].map(x0=>p.mesh[x0]);p.score=p.faceScore;let Z={...SA(p.mesh,h),confidence:h.confidence,landmarks:h.landmarks};p.box=T5(Z,e),p.boxRaw=R5(Z,e),r.push(Z)}}else{p.box=T5(h,e),p.boxRaw=R5(h,e),p.score=p.boxScore,p.mesh=h.landmarks.map(k=>[(h.startPoint[0]+h.endPoint[0])/2+(h.endPoint[0]+h.startPoint[0])*k[0]/kt(),(h.startPoint[1]+h.endPoint[1])/2+(h.endPoint[1]+h.startPoint[1])*k[1]/kt()]),p.meshRaw=p.mesh.map(k=>[k[0]/(e.shape[2]||0),k[1]/(e.shape[1]||0),(k[2]||0)/Ge]);for(let k of Object.keys(nt))p.annotations[k]=[p.mesh[nt[k]]]}p.score>(((f=t.face.detector)==null?void 0:f.minConfidence)||1)?n.push(p):He.dispose(p.tensor)}return U0.boxes=r,n}async function pn(e){var t;return T.initial&&(Ae=null),Ae?e.debug&&b("cached model:",Ae.modelUrl):Ae=await G((t=e.face.mesh)==null?void 0:t.modelPath),Ge=Ae.inputs[0].shape?Ae.inputs[0].shape[2]:0,Ae}var un=Pe,hn=rt;var H0=V(F());var z0,Nt=[],bn=0,gn=0,D5=Number.MAX_SAFE_INTEGER;async function Pn(e){var t;return T.initial&&(z0=null),z0?e.debug&&b("cached model:",z0.modelUrl):z0=await G((t=e.face.description)==null?void 0:t.modelPath),z0}function q5(e){let t=e.image||e.tensor||e;if(!(z0==null?void 0:z0.inputs[0].shape))return t;let o=H0.image.resizeBilinear(t,[z0.inputs[0].shape[2],z0.inputs[0].shape[1]],!1),A=H0.mul(o,H.tf255);return H0.dispose(o),A}async function U5(e,t,o,A){var s,a,y,c;if(!z0)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let n=D5<(((s=t.face.description)==null?void 0:s.skipFrames)||0),r=(((a=t.face.description)==null?void 0:a.skipTime)||0)>g()-bn;return t.skipAllowed&&n&&r&&gn===A&&((y=Nt[o])==null?void 0:y.age)&&((c=Nt[o])==null?void 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it(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Rn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Tn.image.cropAndResize(t,r,[0],o)}function wn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Bt(e,t=1.5){let o=it(e),A=Lt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Gt(e){let t=it(e),o=Lt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function L2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function kn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return L2(o)}var Mn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function fe(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function vn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(fe(e[n],B2(t,r)))}return o}function Y5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Mn(t[0],t[1]),s=vn(r,n),a=Mn(-t[0],-t[1]);return vn(s,a)}function En(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-fe(t[0],o),-fe(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function K5(e,t){return[fe(e,t[0]),fe(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=wn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var F2=5,Sn=1.65,Wn=[0,5,9,13,17,1,2],V2=0,Z2=2,Cn=0,_5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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Object.keys(Un))A.annotations[l]=Un[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function ro(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])==null?void 0:n.inputs[0].shape)||!((r=l0[1])==null?void 0:r.inputs[0].shape))return[];se=[e.shape[2]||0,e.shape[1]||0],Ft++;let o=(t.hand.skipTime||0)>g()-Ao,A=Ft<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-Ao,y=Ft<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>no(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>no(e,i,t))):(o0.boxes=await os(e,t),Ao=g(),o0.hands=await Promise.all(o0.boxes.map(i=>no(e,i,t))),Ft=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let 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model:",W0.modelUrl):(Vt(["size"],e),W0=await G(e.body.modelPath)),Ut=W0.inputs[0].shape?W0.inputs[0].shape[2]:0,Ut<64&&(Ut=256),W0}async function ns(e,t,o){let A=e[0][0],n=[],r=0;for(let i=0;i<A.length;i++)if(r=A[i][2],r>t.body.minConfidence){let l=[A[i][1],A[i][0]];n.push({score:Math.round(100*r)/100,part:Dt[i],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}r=n.reduce((i,l)=>l.score>i?l.score:i,0);let s=[],a=ee(n.map(i=>i.position),[o.shape[2],o.shape[1]]),y={};for(let[i,l]of Object.entries(qt)){let x=[];for(let d=0;d<l.length-1;d++){let u=n.find(M=>M.part===l[d]),f=n.find(M=>M.part===l[d+1]);u&&f&&u.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&x.push([u.position,f.position])}y[i]=x}let c={id:0,score:r,box:a.box,boxRaw:a.boxRaw,keypoints:n,annotations:y};return xo(c),s.push(c),s}async function rs(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let r=e[0][n],s=Math.round(100*r[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Dt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=ee(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(qt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};xo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function fo(e,t){if(!W0||!(W0==null?void 0:W0.inputs[0].shape))return[];t.skipAllowed||(Ee.boxes.length=0),co++;let o=(t.body.skipTime||0)>g()-Ee.last,A=co<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ee.bodies:new Promise(async n=>{let r={};co=0,r.input=Ar(e,Ut),r.res=W0==null?void 0:W0.execute(r.input),Ee.last=g();let s=await r.res.array();Ee.bodies=r.res.shape[2]===17?await ns(s,t,e):await rs(s,t,e);for(let a of Ee.bodies)nr(a,[e.shape[2]||1,e.shape[1]||1]),or(a.keypoints);Object.keys(r).forEach(a=>rr.dispose(r[a])),n(Ee.bodies)})}var M0=V(F());var qe,Jt=[],ir=0,mo=Number.MAX_SAFE_INTEGER,Kt=0,Yt=2.5;async function lr(e){if(!qe||T.initial){qe=await G(e.object.modelPath);let t=Object.values(qe.modelSignature.inputs);Kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",qe.modelUrl);return qe}async function ss(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ne.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ne.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/Kt)),[k,C]=[P-Yt/y*p[0],m-Yt/y*p[1]],[O,j]=[P+Yt/y*p[2]-k,m+Yt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ne[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function po(e,t){let o=(t.object.skipTime||0)>g()-ir,A=mo<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Jt.length>0?(mo++,Jt):(mo=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Jt:new Promise(async 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xt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],as=xt.length,yt=xt.reduce((e,t,o)=>(e[t]=o,e),{}),is=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=is.map(([e,t])=>[yt[e],yt[t]]),xr=[["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"]];function cr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var uo=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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function OA(e){var t;return T.initial&&(q0=null),q0?e.debug&&b("cached model:",q0.modelUrl):q0=await G((t=e.face.detector)==null?void 0:t.modelPath),xe=q0.inputs[0].shape?q0.inputs[0].shape[2]:0,st=N.scalar(xe,"int32"),CA=N.tensor2d(EA(xe)),q0}function v2(e){let t={};t.boxStarts=N.slice(e,[0,1],[-1,2]),t.centers=N.add(t.boxStarts,CA),t.boxSizes=N.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=N.div(t.boxSizes,st),t.centersNormalized=N.div(t.centers,st),t.halfBoxSize=N.div(t.boxSizesNormalized,H.tf2),t.starts=N.sub(t.centersNormalized,t.halfBoxSize),t.ends=N.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=N.mul(t.starts,st),t.endNormalized=N.mul(t.ends,st);let o=N.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(A=>N.dispose(t[A])),o}async function IA(e,t){var a,y,c,i;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=N.image.resizeBilinear(e,[xe,xe]),o.div=N.div(o.resized,H.tf127),o.normalized=N.sub(o.div,H.tf05);let A=q0==null?void 0:q0.execute(o.normalized);if(Array.isArray(A)){let l=A.sort((x,d)=>x.size-d.size);o.concat384=N.concat([l[0],l[2]],2),o.concat512=N.concat([l[1],l[3]],2),o.concat=N.concat([o.concat512,o.concat384],1),o.batch=N.squeeze(o.concat,0)}else o.batch=N.squeeze(A);N.dispose(A),o.boxes=v2(o.batch),o.logits=N.slice(o.batch,[0,0],[-1,1]),o.sigmoid=N.sigmoid(o.logits),o.scores=N.squeeze(o.sigmoid),o.nms=await N.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let n=await o.nms.array(),r=[],s=await o.scores.data();for(let l=0;l<n.length;l++){let x=s[n[l]];if(x>(((i=t.face.detector)==null?void 0:i.minConfidence)||0)){let d={};d.bbox=N.slice(o.boxes,[n[l],0],[1,-1]),d.slice=N.slice(o.batch,[n[l],WA-1],[1,-1]),d.squeeze=N.squeeze(d.slice),d.landmarks=N.reshape(d.squeeze,[WA,-1]);let u=await d.bbox.data(),f={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await d.landmarks.array(),confidence:x},M=RA(f,[(e.shape[2]||0)/xe,(e.shape[1]||0)/xe]),h=Rt(M,t.face.scale||M2),P=wt(h);r.push(P),Object.keys(d).forEach(m=>N.dispose(d[m]))}}return Object.keys(o).forEach(l=>N.dispose(o[l])),r}var k0=V(F());var Et={};mt(Et,{connected:()=>E5,kpt:()=>k5});var k5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],E5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var R0=V(F()),LA=224,T2,R2=5,zt=[8,16,32,32,32];async function BA(){let e=[],t=0;for(;t<R2;){let o=0,A=t;for(;A<zt.length&&zt[A]===zt[t];)o+=2,A++;let n=zt[t],r=Math.ceil(LA/n),s=Math.ceil(LA/n);for(let a=0;a<r;++a)for(let y=0;y<s;++y)for(let c=0;c<o;++c)e.push({x:(y+.5)/s,y:(a+.5)/r});t=A}T2={x:R0.tensor1d(e.map(o=>o.x)),y:R0.tensor1d(e.map(o=>o.y))}}function ee(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[A[0],A[1],n[0]-A[0],n[1]-A[1]],s=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{box:r,boxRaw:s}}function GA(e,t=[1,1]){let o=[e.map(c=>c[0]),e.map(c=>c[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[(A[0]+n[0])/2,(A[1]+n[1])/2],s=Math.max(r[0]-A[0],r[1]-A[1],-r[0]+n[0],-r[1]+n[1]),a=[Math.trunc(r[0]-s),Math.trunc(r[1]-s),Math.trunc(2*s),Math.trunc(2*s)],y=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:y}}function jt(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var VA={initial:!0},w0={detector:null,landmarks:null},Ie={detector:[224,224],landmarks:[256,256]},z5=Number.MAX_SAFE_INTEGER,k2={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Wt=null,at,ce=[[0,0],[0,0],[0,0],[0,0]],HA=0,FA=e=>1-1/(1+Math.exp(e));async function ZA(e){if(VA.initial&&(w0.detector=null),!w0.detector&&e.body.detector&&e.body.detector.modelPath){w0.detector=await G(e.body.detector.modelPath);let t=Object.values(w0.detector.modelSignature.inputs);Ie.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&w0.detector&&b("cached model:",w0.detector.modelUrl);return await BA(),w0.detector}async function XA(e){if(VA.initial&&(w0.landmarks=null),w0.landmarks)e.debug&&b("cached model:",w0.landmarks.modelUrl);else{w0.landmarks=await G(e.body.modelPath);let t=Object.values(w0.landmarks.modelSignature.inputs);Ie.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return w0.landmarks}async function E2(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let A;if(at&&(o.cropped=k0.image.cropAndResize(e,[at],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],r=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ce=[[0,0],n,r,[0,0]],o.pad=k0.pad(o.cropped||e,ce),o.resize=k0.image.resizeBilinear(o.pad,[t,t]),A=k0.div(o.resize,H.tf255)}else e.shape[1]!==t?(o.resize=k0.image.resizeBilinear(o.cropped||e,[t,t]),A=k0.div(o.resize,H.tf255)):A=k0.div(o.cropped||e,H.tf255);return Object.keys(o).forEach(n=>k0.dispose(o[n])),A}function z2(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ce[2][0]+ce[2][1])/t[0]-ce[2][0]),Math.trunc(o.position[1]*(t[1]+ce[1][0]+ce[1][1])/t[1]-ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(at)for(let o of e)o.positionRaw=[o.positionRaw[0]+at[1],o.positionRaw[1]+at[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function j2(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),A=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(A.position[2]||0))/2;let n=e.find(a=>a.part==="rightPalm"),r=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");n.position[2]=((r.position[2]||0)+(s.position[2]||0))/2}async function S2(e,t,o){var u;let A={};[A.ld,A.segmentation,A.heatmap,A.world,A.poseflag]=(u=w0.landmarks)==null?void 0:u.execute(e,k2.landmarks);let n=(await A.poseflag.data())[0],r=await A.ld.data(),s=await A.world.data();Object.keys(A).forEach(f=>k0.dispose(A[f]));let a=[],y=5;for(let f=0;f<r.length/y;f++){let M=FA(r[y*f+3]),h=FA(r[y*f+4]),P=Math.trunc(100*M*h*n)/100,m=[r[y*f+0]/Ie.landmarks[0],r[y*f+1]/Ie.landmarks[1],r[y*f+2]+0],p=[Math.trunc(o[0]*m[0]),Math.trunc(o[1]*m[1]),m[2]],k=[s[y*f+0],s[y*f+1],s[y*f+2]+0];a.push({part:k5[f],positionRaw:m,position:p,distance:k,score:P})}if(n<(t.body.minConfidence||0))return null;j2(a);let c=z2(a,o),i=c.map(f=>f.position),l=ee(i,[o[0],o[1]]),x={};for(let[f,M]of Object.entries(E5)){let h=[];for(let P=0;P<M.length-1;P++){let m=c.find(k=>k.part===M[P]),p=c.find(k=>k.part===M[P+1]);m&&p&&h.push([m.position,p.position])}x[f]=h}return{id:0,score:Math.trunc(100*n)/100,box:l.box,boxRaw:l.boxRaw,keypoints:c,annotations:x}}async function j5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],A=(t.body.skipTime||0)>g()-HA,n=z5<(t.body.skipFrames||0);if(t.skipAllowed&&A&&n&&Wt!==null)z5++;else{let r={};r.landmarks=await E2(e,256),Wt=await S2(r.landmarks,t,o),Object.keys(r).forEach(s=>k0.dispose(r[s])),HA=g(),z5=0}return Wt?[Wt]:[]}var b0=V(F());var Ne=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var te,ve=0,S5=[],qA=0,W5=Number.MAX_SAFE_INTEGER;async function UA(e){if(T.initial&&(te=null),te)e.debug&&b("cached model:",te.modelUrl);else{te=await G(e.object.modelPath);let t=Object.values(te.modelSignature.inputs);ve=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return te}async function W2(e,t,o){if(!e)return[];let A={},n=[],r=await e.array();A.squeeze=b0.squeeze(e);let s=b0.split(A.squeeze,6,1);A.stack=b0.stack([s[1],s[0],s[3],s[2]],1),A.boxes=b0.squeeze(A.stack),A.scores=b0.squeeze(s[4]),A.classes=b0.squeeze(s[5]),b0.dispose([e,...s]),A.nms=await b0.image.nonMaxSuppressionAsync(A.boxes,A.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await A.nms.data(),y=0;for(let c of Array.from(a)){let i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Ne[l].label,[d,u]=[r[0][c][0]/ve,r[0][c][1]/ve],f=[d,u,r[0][c][2]/ve-d,r[0][c][3]/ve-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function C5(e,t){let o=(t.object.skipTime||0)>g()-qA,A=W5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&S5.length>0?(W5++,S5):(W5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[ve,ve]),a=t.object.enabled?te==null?void 0:te.execute(s,["tower_0/detections"]):null;qA=g(),b0.dispose(s);let y=await W2(a,r,t);S5=y,n(y)}))}var K=V(F());var Ct={};mt(Ct,{connected:()=>I5,kpt:()=>O5});var 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a=g0.keypoints.map(x=>x.position[0]),y=g0.keypoints.map(x=>x.position[1]);g0.box=[Math.min(...a),Math.min(...y),Math.max(...a)-Math.min(...a),Math.max(...y)-Math.min(...y)];let c=g0.keypoints.map(x=>x.positionRaw[0]),i=g0.keypoints.map(x=>x.positionRaw[1]);g0.boxRaw=[Math.min(...c),Math.min(...i),Math.max(...c)-Math.min(...c),Math.max(...i)-Math.min(...i)];for(let[x,d]of Object.entries(I5)){let u=[];for(let f=0;f<d.length-1;f++){let M=g0.keypoints.find(P=>P.part===d[f]),h=g0.keypoints.find(P=>P.part===d[f+1]);M&&h&&M.score>(t.body.minConfidence||0)&&h.score>(t.body.minConfidence||0)&&u.push([M.position,h.position])}g0.annotations[x]=u}n([g0])}))}var B0=V(F());var O2=["angry","disgust","fear","happy","sad","surprise","neutral"],O0,Ot=[],_A=0,$A=0,B5=Number.MAX_SAFE_INTEGER;async function en(e){var t;return T.initial&&(O0=null),O0?e.debug&&b("cached model:",O0.modelUrl):O0=await G((t=e.face.emotion)==null?void 0:t.modelPath),O0}async function G5(e,t,o,A){var s,a;if(!O0)return[];let n=B5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-$A;return t.skipAllowed&&r&&n&&_A===A&&Ot[o]&&Ot[o].length>0?(B5++,Ot[o]):(B5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),$A=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:O2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Ot[o]=c,_A=A,y(c)}))}var rn=V(F());var E0,H5=[],on=0,An=0,nn=Number.MAX_SAFE_INTEGER;async function sn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function F5(e,t,o,A){var s,a;if(!E0)return[];let n=nn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&n&&on===A&&H5[o]?(nn++,H5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=rn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}H5[o]=c,on=A,An=g(),y(c)})}var He=V(F());var G0=V(F());var oe,de=0,I2=2.3,V5=L0.leftEyeLower0,Z5=L0.rightEyeLower0,Le={leftBounds:[V5[0],V5[V5.length-1]],rightBounds:[Z5[0],Z5[Z5.length-1]]},Be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function cn(e){var t;return T.initial&&(oe=null),oe?e.debug&&b("cached model:",oe.modelUrl):oe=await G((t=e.face.iris)==null?void 0:t.modelPath),de=oe.inputs[0].shape?oe.inputs[0].shape[2]:0,de===-1&&(de=64),oe}function It(e,t,o,A){for(let n=0;n<g5.length;n++){let{key:r,indices:s}=g5[n],a=L0[`${o}${r}`];if(!A||A.includes(r))for(let y=0;y<s.length;y++){let c=s[y];e[a[y]]=[t[c][0],t[c][1],(t[c][2]+e[a[y]][2])/2]}}}var N2=e=>{let t=e[Le.leftBounds[0]][2],o=e[Le.rightBounds[0]][2];return t-o},ln=(e,t,o,A,n,r=!1)=>{let s=wt(Rt(wA([e[o],e[A]]),I2)),a=Oe(s),y=G0.image.cropAndResize(t,[[s.startPoint[1]/n,s.startPoint[0]/n,s.endPoint[1]/n,s.endPoint[0]/n]],[0],[de,de]);if(r&&T.kernels.includes("flipleftright")){let c=G0.image.flipLeftRight(y);G0.dispose(y),y=c}return{box:s,boxSize:a,crop:y}},yn=(e,t,o,A=!1)=>{let n=[];for(let r=0;r<Be.numCoordinates;r++){let s=e[r*3],a=e[r*3+1],y=e[r*3+2];n.push([(A?1-s/de:s/de)*o[0]+t.startPoint[0],a/de*o[1]+t.startPoint[1],y])}return{rawCoords:n,iris:n.slice(Be.index)}},xn=(e,t,o)=>{let A=e[L0[`${o}EyeUpper0`][Be.upperCenter]][2],n=e[L0[`${o}EyeLower0`][Be.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function dn(e,t,o,A){if(!oe)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=ln(e,t,Le.leftBounds[0],Le.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=ln(e,t,Le.rightBounds[0],Le.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=oe.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,Be.numCoordinates*3),{rawCoords:u,iris:f}=yn(d,n,r,!0),M=x.slice(Be.numCoordinates*3),{rawCoords:h,iris:P}=yn(M,a,y),m=N2(e);Math.abs(m)<30?(It(e,u,"left",null),It(e,h,"right",null)):m<1?It(e,u,"left",["EyeUpper0","EyeLower0"]):It(e,h,"right",["EyeUpper0","EyeLower0"]);let p=xn(e,f,"left"),k=xn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Ae=null,Ge=0;async function mn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await IA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=jA((c=t.face.detector)==null?void 0:c.rotation,h,e,(i=t.face.mesh)!=null&&i.enabled?Ge:kt()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let k=await pt(p.tensor);He.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!Ae)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=Ae.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=He.reshape(O,[-1,3]),J=await 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it(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Rn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Tn.image.cropAndResize(t,r,[0],o)}function wn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Bt(e,t=1.5){let o=it(e),A=Lt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Gt(e){let t=it(e),o=Lt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function L2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function kn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return L2(o)}var Mn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function fe(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function vn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(fe(e[n],B2(t,r)))}return o}function Y5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Mn(t[0],t[1]),s=vn(r,n),a=Mn(-t[0],-t[1]);return vn(s,a)}function En(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-fe(t[0],o),-fe(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function K5(e,t){return[fe(e,t[0]),fe(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=wn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var F2=5,Sn=1.65,Wn=[0,5,9,13,17,1,2],V2=0,Z2=2,Cn=0,_5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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a=e.keypoints[A]?[Math.abs(e.keypoints[r].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0])]:[0,0],y=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[r].position[0]-e.keypoints[n].position[0])]:[0,0];if(a[0]>a[1]||y[0]>y[1]){let c=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=c}}}function or(e){for(let t=0;t<e.length;t++)if(e[t]&&S0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-S0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-S0.keypoints[t].positionRaw[1])];o[0]<tr&&o[1]<tr?e[t]=S0.keypoints[t]:S0.keypoints[t]=e[t]}else S0.keypoints[t]=e[t];return e}function Ar(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;S0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ge.pad(e,S0.padding),o.resize=ge.image.resizeBilinear(o.pad,[t,t]);let A=ge.cast(o.resize,"int32");return Object.keys(o).forEach(n=>ge.dispose(o[n])),A}function nr(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+S0.padding[2][0]+S0.padding[2][1])/t[0]-S0.padding[2][0],A.position[1]*(t[1]+S0.padding[1][0]+S0.padding[1][1])/t[1]-S0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=ee(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,Ut=0,co=Number.MAX_SAFE_INTEGER,Ee={boxes:[],bodies:[],last:0};async function sr(e){return T.initial&&(W0=null),W0?e.debug&&b("cached model:",W0.modelUrl):(Vt(["size"],e),W0=await G(e.body.modelPath)),Ut=W0.inputs[0].shape?W0.inputs[0].shape[2]:0,Ut<64&&(Ut=256),W0}async function ns(e,t,o){let A=e[0][0],n=[],r=0;for(let i=0;i<A.length;i++)if(r=A[i][2],r>t.body.minConfidence){let l=[A[i][1],A[i][0]];n.push({score:Math.round(100*r)/100,part:Dt[i],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}r=n.reduce((i,l)=>l.score>i?l.score:i,0);let s=[],a=ee(n.map(i=>i.position),[o.shape[2],o.shape[1]]),y={};for(let[i,l]of Object.entries(qt)){let x=[];for(let d=0;d<l.length-1;d++){let u=n.find(M=>M.part===l[d]),f=n.find(M=>M.part===l[d+1]);u&&f&&u.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&x.push([u.position,f.position])}y[i]=x}let c={id:0,score:r,box:a.box,boxRaw:a.boxRaw,keypoints:n,annotations:y};return xo(c),s.push(c),s}async function rs(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let r=e[0][n],s=Math.round(100*r[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Dt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=ee(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(qt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};xo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function fo(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(Ee.boxes.length=0),co++;let o=(t.body.skipTime||0)>g()-Ee.last,A=co<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ee.bodies:new Promise(async n=>{let r={};co=0,r.input=Ar(e,Ut),r.res=W0==null?void 0:W0.execute(r.input),Ee.last=g();let s=await r.res.array();Ee.bodies=r.res.shape[2]===17?await ns(s,t,e):await rs(s,t,e);for(let a of Ee.bodies)nr(a,[e.shape[2]||1,e.shape[1]||1]),or(a.keypoints);Object.keys(r).forEach(a=>rr.dispose(r[a])),n(Ee.bodies)})}var M0=V(F());var qe,Jt=[],ir=0,mo=Number.MAX_SAFE_INTEGER,Kt=0,Yt=2.5;async function lr(e){if(!qe||T.initial){qe=await G(e.object.modelPath);let t=Object.values(qe.modelSignature.inputs);Kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",qe.modelUrl);return qe}async function ss(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ne.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ne.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/Kt)),[k,C]=[P-Yt/y*p[0],m-Yt/y*p[1]],[O,j]=[P+Yt/y*p[2]-k,m+Yt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ne[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function po(e,t){let o=(t.object.skipTime||0)>g()-ir,A=mo<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Jt.length>0?(mo++,Jt):(mo=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Jt:new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=M0.image.resizeBilinear(e,[Kt,Kt],!1),a=M0.div(s,H.tf255),y=a.transpose([0,3,1,2]);M0.dispose(a),M0.dispose(s);let c;t.object.enabled&&(c=qe.execute(y)),ir=g(),M0.dispose(y);let i=await ss(c,r,t);Jt=i,n(i)}))}var v0=V(F());var xt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],as=xt.length,yt=xt.reduce((e,t,o)=>(e[t]=o,e),{}),is=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=is.map(([e,t])=>[yt[e],yt[t]]),xr=[["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"]];function cr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var uo=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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l=Math.abs(i.annotations.leftEyeIris[3][0]-i.annotations.leftEyeIris[1][0])/2,x=Math.abs(i.annotations.leftEyeIris[4][1]-i.annotations.leftEyeIris[2][1])/2;n.ellipse(i.annotations.leftEyeIris[0][0],i.annotations.leftEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(i.annotations&&i.annotations.rightEyeIris&&i.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let l=Math.abs(i.annotations.rightEyeIris[3][0]-i.annotations.rightEyeIris[1][0])/2,x=Math.abs(i.annotations.rightEyeIris[4][1]-i.annotations.rightEyeIris[2][1])/2;n.ellipse(i.annotations.rightEyeIris[0][0],i.annotations.rightEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((r=i.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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@@ -108,7 +108,7 @@ var Dr=Object.create;var $e=Object.defineProperty;var qr=Object.getOwnPropertyDe
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Description:"),e.config.async?i=((q=e.config.face.description)==null?void 0:q.enabled)?U5(x[w].tensor||A0.tensor([]),e.config,w,x.length):null:(e.state="run:description",o=g(),i=((x0=e.config.face.description)==null?void 0:x0.enabled)?await U5(x[w].tensor||A0.tensor([]),e.config,w,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([A,r,s,a,i,n,y,c]=await Promise.all([A,r,s,a,i,n,y,c])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&A&&r&&(i={...i,age:A.age,gender:r.gender,genderScore:r.genderScore}),((C0=e.config.face.gear)==null?void 0:C0.enabled)&&n&&(i={...i,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((ie=e.config.face.mobilefacenet)==null?void 0:ie.enabled)&&a&&(i.descriptor=a),!((v=e.config.face.iris)==null?void 0:v.enabled);let n0=x[w].annotations&&x[w].annotations.leftEyeIris&&x[w].annotations.leftEyeIris[0]&&x[w].annotations.rightEyeIris&&x[w].annotations.rightEyeIris[0]&&x[w].annotations.leftEyeIris.length>0&&x[w].annotations.rightEyeIris.length>0&&x[w].annotations.leftEyeIris[0]!==null&&x[w].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[w].annotations.leftEyeIris[3][0]-x[w].annotations.leftEyeIris[1][0]),Math.abs(x[w].annotations.rightEyeIris[4][1]-x[w].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=((a0=e.config.face.detector)==null?void 0:a0.return)?A0.squeeze(x[w].tensor):null;A0.dispose(x[w].tensor),x[w].tensor&&delete x[w].tensor;let I={...x[w],id:w};(i==null?void 0:i.age)&&(I.age=i.age),(i==null?void 0:i.gender)&&(I.gender=i.gender),(i==null?void 0:i.genderScore)&&(I.genderScore=i==null?void 0:i.genderScore),(i==null?void 0:i.descriptor)&&(I.embedding=i==null?void 0:i.descriptor),(i==null?void 0:i.race)&&(I.race=i==null?void 0:i.race),s&&(I.emotion=s),y&&(I.real=y),c&&(I.live=c),n0&&n0!==0&&(I.iris=Math.trunc(500/n0/11.7)/100),Q&&(I.rotation=Q),y0&&(I.tensor=y0),l.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Or=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(y=>y.part==="leftWrist"),n=e[o].keypoints.find(y=>y.part==="rightWrist"),r=e[o].keypoints.find(y=>y.part==="nose");r&&A&&n&&A.position[1]<r.position[1]&&n.position[1]<r.position[1]?t.push({body:o,gesture:"i give up"}):r&&A&&A.position[1]<r.position[1]?t.push({body:o,gesture:"raise left hand"}):r&&n&&n.position[1]<r.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(y=>y.part==="leftShoulder"),a=e[o].keypoints.find(y=>y.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Ir=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let y=e[o].mesh[152][2]||0;Math.abs(y)>10&&t.push({face:o,gesture:`head ${y<0?"up":"down"}`})}return t},Nr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],r=Math.abs(A*n),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],y=Math.abs(s*a),c=!1;Math.abs(r-y)/Math.max(r,y)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(c=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let d=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||d<.01||u>.022||d>.022)&&(c=!1),(u<.01||d<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||d>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},Lr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,r]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(r)&&r[0]&&A.push({name:n.toLowerCase(),position:r[0]});if(A&&A.length>0){let n=A.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${n.name} forward`});let r=A.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${r.name} up`})}if(e[o].keypoints){let n=Fn(e[o].keypoints);for(let r of n)t.push({hand:o,gesture:r.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},No=0;function Br(e,t){var s,a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie;let o=g();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let A=Date.now()-e.timestamp,n=A<1e3?8-Math.log(A+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let a0=e.body[v].box.map((I,U)=>((n-1)*z.body[v].box[U]+I)/n),w=e.body[v].boxRaw.map((I,U)=>((n-1)*z.body[v].boxRaw[U]+I)/n),Q=e.body[v].keypoints.map((I,U)=>{var 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Object.keys(e.hand[v].annotations))n0[y0]=e.hand[v].annotations[y0]&&e.hand[v].annotations[y0][0]?e.hand[v].annotations[y0].map((I,U)=>I.map((Y0,K0)=>((n-1)*z.hand[v].annotations[y0][U][K0]+Y0)/n)):null;z.hand[v]={...e.hand[v],box:a0,boxRaw:w,keypoints:Q,annotations:n0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let a0=e.face[v].box.map((Q,n0)=>((n-1)*z.face[v].box[n0]+Q)/n),w=e.face[v].boxRaw.map((Q,n0)=>((n-1)*z.face[v].boxRaw[n0]+Q)/n);if(e.face[v].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(x=e.face[v].rotation)==null?void 0:x.matrix,Q.angle={roll:((n-1)*(((u=(d=z.face[v].rotation)==null?void 0:d.angle)==null?void 0:u.roll)||0)+(((M=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:M.roll)||0))/n,yaw:((n-1)*(((P=(h=z.face[v].rotation)==null?void 0:h.angle)==null?void 0:P.yaw)||0)+(((p=(m=e.face[v].rotation)==null?void 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n0=x[w].annotations&&x[w].annotations.leftEyeIris&&x[w].annotations.leftEyeIris[0]&&x[w].annotations.rightEyeIris&&x[w].annotations.rightEyeIris[0]&&x[w].annotations.leftEyeIris.length>0&&x[w].annotations.rightEyeIris.length>0&&x[w].annotations.leftEyeIris[0]!==null&&x[w].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[w].annotations.leftEyeIris[3][0]-x[w].annotations.leftEyeIris[1][0]),Math.abs(x[w].annotations.rightEyeIris[4][1]-x[w].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=(a0=e.config.face.detector)!=null&&a0.return?A0.squeeze(x[w].tensor):null;A0.dispose(x[w].tensor),x[w].tensor&&delete x[w].tensor;let I={...x[w],id:w};i!=null&&i.age&&(I.age=i.age),i!=null&&i.gender&&(I.gender=i.gender),i!=null&&i.genderScore&&(I.genderScore=i==null?void 0:i.genderScore),i!=null&&i.descriptor&&(I.embedding=i==null?void 0:i.descriptor),i!=null&&i.race&&(I.race=i==null?void 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A.kernels)n[a.name]?n[a.name]+=a.kernelTimeMs:n[a.name]=a.kernelTimeMs;let r=[];Object.entries(n).forEach(a=>r.push({name:a[0],ms:a[1]})),r.sort((a,y)=>y.ms-a.ms),r.length=20;let s={};for(let a of r)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async A=>{var M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie,v,a0,w,Q,n0;this.state="config";let n;this.config=c0(this.config,o),this.state="check";let r=et(this,o5).call(this,t);r&&(b(r,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:r}));let s=g();await _t(this),await this.load(),n=g(),this.state="image";let a=await We(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=g(),this.config.skipAllowed=await oA(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Check Changed:");let y=[],c=[],i=[],l=[];this.state="detect:face",this.config.async?(y=this.config.face.enabled?Io(this,a.tensor):[],this.performance.face&&delete this.performance.face):(n=g(),y=this.config.face.enabled?await Io(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(y=await y),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?c0(this.config,{body:{maxDetected:this.config.face.enabled?1*y.length:1}}):this.config;this.config.async?(((M=this.config.body.modelPath)==null?void 0:M.includes("posenet"))?c=this.config.body.enabled?Mo(a.tensor,x):[]:((h=this.config.body.modelPath)==null?void 0:h.includes("blazepose"))?c=this.config.body.enabled?j5(a.tensor,x):[]:((P=this.config.body.modelPath)==null?void 0:P.includes("efficientpose"))?c=this.config.body.enabled?L5(a.tensor,x):[]:((m=this.config.body.modelPath)==null?void 0:m.includes("movenet"))&&(c=this.config.body.enabled?fo(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(n=g(),((p=this.config.body.modelPath)==null?void 0:p.includes("posenet"))?c=this.config.body.enabled?await Mo(a.tensor,x):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?c=this.config.body.enabled?await j5(a.tensor,x):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("efficientpose"))?c=this.config.body.enabled?await L5(a.tensor,x):[]:((O=this.config.body.modelPath)==null?void 0:O.includes("movenet"))&&(c=this.config.body.enabled?await fo(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?c0(this.config,{hand:{maxDetected:this.config.face.enabled?2*y.length:1}}):this.config;this.config.async?(((L=(j=this.config.hand.detector)==null?void 0:j.modelPath)==null?void 0:L.includes("handdetect"))?i=this.config.hand.enabled?to(a.tensor,d):[]:((Z=(J=this.config.hand.detector)==null?void 0:J.modelPath)==null?void 0:Z.includes("handtrack"))&&(i=this.config.hand.enabled?ro(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),((x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)==null?void 0:x0.includes("handdetect"))?i=this.config.hand.enabled?await to(a.tensor,d):[]:((C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:C0.includes("handtrack"))&&(i=this.config.hand.enabled?await ro(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((ie=this.config.object.modelPath)==null?void 0:ie.includes("nanodet"))?l=this.config.object.enabled?po(a.tensor,this.config):[]:((v=this.config.object.modelPath)==null?void 0:v.includes("centernet"))&&(l=this.config.object.enabled?C5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),((a0=this.config.object.modelPath)==null?void 0:a0.includes("nanodet"))?l=this.config.object.enabled?await po(a.tensor,this.config):[]:((w=this.config.object.modelPath)==null?void 0:w.includes("centernet"))&&(l=this.config.object.enabled?await C5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Ir(y),...Or(c),...Lr(i),...Nr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Vr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Ke=new WeakMap,dt=new WeakMap,ft=new WeakMap,o5=new WeakMap;module.exports=_r(Is);0&&(module.exports={Human,defaults,env});
+2Q==`;var Lo=V(F());async function Ss(e){let t=(n,r="application/octet-stream")=>fetch(`data:${r};base64,${n}`).then(s=>s.blob()),o,A;switch(e.config.warmup){case"face":o=await t(e5);break;case"body":case"full":o=await t(t5);break;default:o=null}if(o){let n=await createImageBitmap(o);A=await e.detect(n,e.config),n.close()}return A}async function Ws(e){return new Promise(t=>{let o;switch(e.config.warmup){case"face":o="data:image/jpeg;base64,"+e5;break;case"full":case"body":o="data:image/jpeg;base64,"+t5;break;default:o=null}let A;if(typeof Image!="undefined")A=new Image;else if(T.Image)A=new T.Image;else return;A.onload=async()=>{let n=h0(A.naturalWidth,A.naturalHeight);if(!n)b("Warmup: Canvas not found"),t(void 0);else{let r=n.getContext("2d");r&&r.drawImage(A,0,0);let s=await e.image(n),a=await e.detect(s.tensor,e.config);t(a)}},o?A.src=o:t(void 0)})}async function Cs(e){let t=n=>Buffer.from(n,"base64"),o;e.config.warmup==="face"?o=t(e5):o=t(t5);let A;if("node"in Lo){let n=Lo.node.decodeJpeg(o),r=n.expandDims(0);e.tf.dispose(n),A=await e.detect(r,e.config),e.tf.dispose(r)}else e.config.debug&&b("Warmup tfjs-node not loaded");return A}async function Zr(e,t){let o=g();if(e.state="warmup",t&&(e.config=c0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:g(),persons:[],error:null};let A;return new Promise(async n=>{typeof createImageBitmap=="function"?A=await Ss(e):typeof Image!="undefined"||T.Canvas!==void 0?A=await Ws(e):A=await Cs(e);let r=g();e.config.debug&&b("Warmup",e.config.warmup,Math.round(r-o),"ms"),e.emit("warmup"),n(A)})}var Ke,dt,ft,o5,Xr=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");tt(this,Ke,void 0);tt(this,dt,void 0);tt(this,ft,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!et(this,dt))return;let o=this.tf.engine().state.numTensors,A=et(this,Ke);ot(this,Ke,o);let n=o-A;n!==0&&b(...t,n)});tt(this,o5,t=>{if(!et(this,ft))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof D0.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});R(this,"similarity",Hr);R(this,"distance",$t);R(this,"match",Fr);R(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});this.env=T,le.wasmPath=D0.version["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${D0.version_core}/dist/`,le.modelBasePath=T.browser?"../models/":"file://models/",le.backend=T.browser?"humangl":"tensorflow",this.version=i5,Object.defineProperty(this,"version",{value:i5}),this.config=JSON.parse(JSON.stringify(le)),Object.seal(this.config),t&&(this.config=c0(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",rA(this.config),this.tf=D0,this.state="idle",ot(this,Ke,0),ot(this,dt,!1),ot(this,ft,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Ro,this.draw={options:ae,canvas:(o,A)=>zr(o,A),face:(o,A,n)=>jo(o,A,n),body:(o,A,n)=>So(o,A,n),hand:(o,A,n)=>Wo(o,A,n),gesture:(o,A,n)=>zo(o,A,n),object:(o,A,n)=>Co(o,A,n),person:(o,A,n)=>Er(o,A,n),all:(o,A,n)=>jr(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=un,this.faceUVMap=hn,this.gl=$,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(le)),this.config.backend=t}validate(t){return A5(le,t||this.config)}now(){return g()}image(t,o=!0){return We(t,this.config,o)}async segmentation(t,o){return br(t,o,this.config)}enhance(t){return q5(t)}compare(t,o){return AA(this.config,t,o)}async init(){await _t(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=g(),A=Object.values(this.models).filter(s=>s).length;t&&(this.config=c0(this.config,t)),this.env.initial&&(this.config.debug&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`),await _t(this)||b("error: backend check failed"),await D0.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("environment:",this.env),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await Pr(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==A&&(await Mr(this),this.emit("load"));let r=Math.trunc(g()-o);r>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+r:r)}next(t=this.result){return Br(t,this.config)}async warmup(t){let o=g(),A=await Zr(this,t),n=g();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let a of A.kernels)n[a.name]?n[a.name]+=a.kernelTimeMs:n[a.name]=a.kernelTimeMs;let r=[];Object.entries(n).forEach(a=>r.push({name:a[0],ms:a[1]})),r.sort((a,y)=>y.ms-a.ms),r.length=20;let s={};for(let a of r)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async A=>{var M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie,v,a0,w,Q,n0;this.state="config";let n;this.config=c0(this.config,o),this.state="check";let r=et(this,o5).call(this,t);r&&(b(r,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:r}));let s=g();await _t(this),await this.load(),n=g(),this.state="image";let a=await We(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=g(),this.config.skipAllowed=await oA(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Check Changed:");let y=[],c=[],i=[],l=[];this.state="detect:face",this.config.async?(y=this.config.face.enabled?Io(this,a.tensor):[],this.performance.face&&delete this.performance.face):(n=g(),y=this.config.face.enabled?await Io(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(y=await y),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?c0(this.config,{body:{maxDetected:this.config.face.enabled?1*y.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?c=this.config.body.enabled?Mo(a.tensor,x):[]:(h=this.config.body.modelPath)!=null&&h.includes("blazepose")?c=this.config.body.enabled?j5(a.tensor,x):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?L5(a.tensor,x):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?fo(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(n=g(),(p=this.config.body.modelPath)!=null&&p.includes("posenet")?c=this.config.body.enabled?await Mo(a.tensor,x):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await j5(a.tensor,x):[]:(C=this.config.body.modelPath)!=null&&C.includes("efficientpose")?c=this.config.body.enabled?await L5(a.tensor,x):[]:(O=this.config.body.modelPath)!=null&&O.includes("movenet")&&(c=this.config.body.enabled?await fo(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?c0(this.config,{hand:{maxDetected:this.config.face.enabled?2*y.length:1}}):this.config;this.config.async?((L=(j=this.config.hand.detector)==null?void 0:j.modelPath)!=null&&L.includes("handdetect")?i=this.config.hand.enabled?to(a.tensor,d):[]:(Z=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&Z.includes("handtrack")&&(i=this.config.hand.enabled?ro(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),(x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&x0.includes("handdetect")?i=this.config.hand.enabled?await to(a.tensor,d):[]:(C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&C0.includes("handtrack")&&(i=this.config.hand.enabled?await ro(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((ie=this.config.object.modelPath)!=null&&ie.includes("nanodet")?l=this.config.object.enabled?po(a.tensor,this.config):[]:(v=this.config.object.modelPath)!=null&&v.includes("centernet")&&(l=this.config.object.enabled?C5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),(a0=this.config.object.modelPath)!=null&&a0.includes("nanodet")?l=this.config.object.enabled?await po(a.tensor,this.config):[]:(w=this.config.object.modelPath)!=null&&w.includes("centernet")&&(l=this.config.object.enabled?await C5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Ir(y),...Or(c),...Lr(i),...Nr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Vr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Ke=new WeakMap,dt=new WeakMap,ft=new WeakMap,o5=new WeakMap;module.exports=_r(Is);0&&(module.exports={Human,defaults,env});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/human.node.js b/dist/human.node.js
index e3c38e21..7ce93bdf 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -96,7 +96,7 @@ var Dr=Object.create;var $e=Object.defineProperty;var qr=Object.getOwnPropertyDe
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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function OA(e){var t;return T.initial&&(q0=null),q0?e.debug&&b("cached model:",q0.modelUrl):q0=await G((t=e.face.detector)==null?void 0:t.modelPath),xe=q0.inputs[0].shape?q0.inputs[0].shape[2]:0,st=N.scalar(xe,"int32"),CA=N.tensor2d(EA(xe)),q0}function v2(e){let t={};t.boxStarts=N.slice(e,[0,1],[-1,2]),t.centers=N.add(t.boxStarts,CA),t.boxSizes=N.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=N.div(t.boxSizes,st),t.centersNormalized=N.div(t.centers,st),t.halfBoxSize=N.div(t.boxSizesNormalized,H.tf2),t.starts=N.sub(t.centersNormalized,t.halfBoxSize),t.ends=N.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=N.mul(t.starts,st),t.endNormalized=N.mul(t.ends,st);let o=N.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(A=>N.dispose(t[A])),o}async function IA(e,t){var a,y,c,i;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=N.image.resizeBilinear(e,[xe,xe]),o.div=N.div(o.resized,H.tf127),o.normalized=N.sub(o.div,H.tf05);let A=q0==null?void 0:q0.execute(o.normalized);if(Array.isArray(A)){let l=A.sort((x,d)=>x.size-d.size);o.concat384=N.concat([l[0],l[2]],2),o.concat512=N.concat([l[1],l[3]],2),o.concat=N.concat([o.concat512,o.concat384],1),o.batch=N.squeeze(o.concat,0)}else o.batch=N.squeeze(A);N.dispose(A),o.boxes=v2(o.batch),o.logits=N.slice(o.batch,[0,0],[-1,1]),o.sigmoid=N.sigmoid(o.logits),o.scores=N.squeeze(o.sigmoid),o.nms=await N.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let n=await o.nms.array(),r=[],s=await o.scores.data();for(let l=0;l<n.length;l++){let x=s[n[l]];if(x>(((i=t.face.detector)==null?void 0:i.minConfidence)||0)){let d={};d.bbox=N.slice(o.boxes,[n[l],0],[1,-1]),d.slice=N.slice(o.batch,[n[l],WA-1],[1,-1]),d.squeeze=N.squeeze(d.slice),d.landmarks=N.reshape(d.squeeze,[WA,-1]);let u=await d.bbox.data(),f={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await d.landmarks.array(),confidence:x},M=RA(f,[(e.shape[2]||0)/xe,(e.shape[1]||0)/xe]),h=Rt(M,t.face.scale||M2),P=wt(h);r.push(P),Object.keys(d).forEach(m=>N.dispose(d[m]))}}return Object.keys(o).forEach(l=>N.dispose(o[l])),r}var k0=V(F());var Et={};mt(Et,{connected:()=>w5,kpt:()=>R5});var R5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],w5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var R0=V(F()),LA=224,T2,R2=5,zt=[8,16,32,32,32];async function BA(){let e=[],t=0;for(;t<R2;){let o=0,A=t;for(;A<zt.length&&zt[A]===zt[t];)o+=2,A++;let n=zt[t],r=Math.ceil(LA/n),s=Math.ceil(LA/n);for(let a=0;a<r;++a)for(let y=0;y<s;++y)for(let c=0;c<o;++c)e.push({x:(y+.5)/s,y:(a+.5)/r});t=A}T2={x:R0.tensor1d(e.map(o=>o.x)),y:R0.tensor1d(e.map(o=>o.y))}}function ee(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[A[0],A[1],n[0]-A[0],n[1]-A[1]],s=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{box:r,boxRaw:s}}function GA(e,t=[1,1]){let o=[e.map(c=>c[0]),e.map(c=>c[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[(A[0]+n[0])/2,(A[1]+n[1])/2],s=Math.max(r[0]-A[0],r[1]-A[1],-r[0]+n[0],-r[1]+n[1]),a=[Math.trunc(r[0]-s),Math.trunc(r[1]-s),Math.trunc(2*s),Math.trunc(2*s)],y=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:y}}function jt(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var VA={initial:!0},w0={detector:null,landmarks:null},Ie={detector:[224,224],landmarks:[256,256]},k5=Number.MAX_SAFE_INTEGER,k2={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Wt=null,at,ce=[[0,0],[0,0],[0,0],[0,0]],HA=0,FA=e=>1-1/(1+Math.exp(e));async function ZA(e){if(VA.initial&&(w0.detector=null),!w0.detector&&e.body.detector&&e.body.detector.modelPath){w0.detector=await G(e.body.detector.modelPath);let t=Object.values(w0.detector.modelSignature.inputs);Ie.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&w0.detector&&b("cached model:",w0.detector.modelUrl);return await BA(),w0.detector}async function XA(e){if(VA.initial&&(w0.landmarks=null),w0.landmarks)e.debug&&b("cached model:",w0.landmarks.modelUrl);else{w0.landmarks=await G(e.body.modelPath);let t=Object.values(w0.landmarks.modelSignature.inputs);Ie.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return w0.landmarks}async function E2(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let A;if(at&&(o.cropped=k0.image.cropAndResize(e,[at],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],r=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ce=[[0,0],n,r,[0,0]],o.pad=k0.pad(o.cropped||e,ce),o.resize=k0.image.resizeBilinear(o.pad,[t,t]),A=k0.div(o.resize,H.tf255)}else e.shape[1]!==t?(o.resize=k0.image.resizeBilinear(o.cropped||e,[t,t]),A=k0.div(o.resize,H.tf255)):A=k0.div(o.cropped||e,H.tf255);return Object.keys(o).forEach(n=>k0.dispose(o[n])),A}function z2(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ce[2][0]+ce[2][1])/t[0]-ce[2][0]),Math.trunc(o.position[1]*(t[1]+ce[1][0]+ce[1][1])/t[1]-ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(at)for(let o of e)o.positionRaw=[o.positionRaw[0]+at[1],o.positionRaw[1]+at[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function j2(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),A=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(A.position[2]||0))/2;let n=e.find(a=>a.part==="rightPalm"),r=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");n.position[2]=((r.position[2]||0)+(s.position[2]||0))/2}async function S2(e,t,o){var u;let A={};[A.ld,A.segmentation,A.heatmap,A.world,A.poseflag]=(u=w0.landmarks)==null?void 0:u.execute(e,k2.landmarks);let n=(await A.poseflag.data())[0],r=await A.ld.data(),s=await A.world.data();Object.keys(A).forEach(f=>k0.dispose(A[f]));let a=[],y=5;for(let f=0;f<r.length/y;f++){let M=FA(r[y*f+3]),h=FA(r[y*f+4]),P=Math.trunc(100*M*h*n)/100,m=[r[y*f+0]/Ie.landmarks[0],r[y*f+1]/Ie.landmarks[1],r[y*f+2]+0],p=[Math.trunc(o[0]*m[0]),Math.trunc(o[1]*m[1]),m[2]],k=[s[y*f+0],s[y*f+1],s[y*f+2]+0];a.push({part:R5[f],positionRaw:m,position:p,distance:k,score:P})}if(n<(t.body.minConfidence||0))return null;j2(a);let c=z2(a,o),i=c.map(f=>f.position),l=ee(i,[o[0],o[1]]),x={};for(let[f,M]of Object.entries(w5)){let h=[];for(let P=0;P<M.length-1;P++){let m=c.find(k=>k.part===M[P]),p=c.find(k=>k.part===M[P+1]);m&&p&&h.push([m.position,p.position])}x[f]=h}return{id:0,score:Math.trunc(100*n)/100,box:l.box,boxRaw:l.boxRaw,keypoints:c,annotations:x}}async function E5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],A=(t.body.skipTime||0)>g()-HA,n=k5<(t.body.skipFrames||0);if(t.skipAllowed&&A&&n&&Wt!==null)k5++;else{let r={};r.landmarks=await E2(e,256),Wt=await S2(r.landmarks,t,o),Object.keys(r).forEach(s=>k0.dispose(r[s])),HA=g(),k5=0}return Wt?[Wt]:[]}var b0=V(F());var Ne=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var te,ve=0,z5=[],qA=0,j5=Number.MAX_SAFE_INTEGER;async function UA(e){if(T.initial&&(te=null),te)e.debug&&b("cached model:",te.modelUrl);else{te=await G(e.object.modelPath);let t=Object.values(te.modelSignature.inputs);ve=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return te}async function W2(e,t,o){if(!e)return[];let A={},n=[],r=await e.array();A.squeeze=b0.squeeze(e);let s=b0.split(A.squeeze,6,1);A.stack=b0.stack([s[1],s[0],s[3],s[2]],1),A.boxes=b0.squeeze(A.stack),A.scores=b0.squeeze(s[4]),A.classes=b0.squeeze(s[5]),b0.dispose([e,...s]),A.nms=await b0.image.nonMaxSuppressionAsync(A.boxes,A.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await A.nms.data(),y=0;for(let c of Array.from(a)){let i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Ne[l].label,[d,u]=[r[0][c][0]/ve,r[0][c][1]/ve],f=[d,u,r[0][c][2]/ve-d,r[0][c][3]/ve-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function S5(e,t){let o=(t.object.skipTime||0)>g()-qA,A=j5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&z5.length>0?(j5++,z5):(j5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[ve,ve]),a=t.object.enabled?te==null?void 0:te.execute(s,["tower_0/detections"]):null;qA=g(),b0.dispose(s);let y=await W2(a,r,t);z5=y,n(y)}))}var K=V(F());var Ct={};mt(Ct,{connected:()=>C5,kpt:()=>W5});var 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n=N5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-$A;return t.skipAllowed&&r&&n&&_A===A&&Ot[o]&&Ot[o].length>0?(N5++,Ot[o]):(N5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)==null?void 0:i.enabled){let x={},d=(O0==null?void 0:O0.inputs[0].shape)?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),$A=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:O2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Ot[o]=c,_A=A,y(c)}))}var rn=V(F());var E0,B5=[],on=0,An=0,nn=Number.MAX_SAFE_INTEGER;async function sn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function G5(e,t,o,A){var s,a;if(!E0)return[];let n=nn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&n&&on===A&&B5[o]?(nn++,B5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=rn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}B5[o]=c,on=A,An=g(),y(c)})}var He=V(F());var G0=V(F());var oe,de=0,I2=2.3,H5=L0.leftEyeLower0,F5=L0.rightEyeLower0,Le={leftBounds:[H5[0],H5[H5.length-1]],rightBounds:[F5[0],F5[F5.length-1]]},Be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function cn(e){var t;return T.initial&&(oe=null),oe?e.debug&&b("cached model:",oe.modelUrl):oe=await G((t=e.face.iris)==null?void 0:t.modelPath),de=oe.inputs[0].shape?oe.inputs[0].shape[2]:0,de===-1&&(de=64),oe}function It(e,t,o,A){for(let n=0;n<h5.length;n++){let{key:r,indices:s}=h5[n],a=L0[`${o}${r}`];if(!A||A.includes(r))for(let y=0;y<s.length;y++){let c=s[y];e[a[y]]=[t[c][0],t[c][1],(t[c][2]+e[a[y]][2])/2]}}}var N2=e=>{let t=e[Le.leftBounds[0]][2],o=e[Le.rightBounds[0]][2];return t-o},ln=(e,t,o,A,n,r=!1)=>{let s=wt(Rt(wA([e[o],e[A]]),I2)),a=Oe(s),y=G0.image.cropAndResize(t,[[s.startPoint[1]/n,s.startPoint[0]/n,s.endPoint[1]/n,s.endPoint[0]/n]],[0],[de,de]);if(r&&T.kernels.includes("flipleftright")){let c=G0.image.flipLeftRight(y);G0.dispose(y),y=c}return{box:s,boxSize:a,crop:y}},yn=(e,t,o,A=!1)=>{let n=[];for(let r=0;r<Be.numCoordinates;r++){let s=e[r*3],a=e[r*3+1],y=e[r*3+2];n.push([(A?1-s/de:s/de)*o[0]+t.startPoint[0],a/de*o[1]+t.startPoint[1],y])}return{rawCoords:n,iris:n.slice(Be.index)}},xn=(e,t,o)=>{let A=e[L0[`${o}EyeUpper0`][Be.upperCenter]][2],n=e[L0[`${o}EyeLower0`][Be.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function dn(e,t,o,A){if(!oe)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=ln(e,t,Le.leftBounds[0],Le.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=ln(e,t,Le.rightBounds[0],Le.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=oe.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,Be.numCoordinates*3),{rawCoords:u,iris:f}=yn(d,n,r,!0),M=x.slice(Be.numCoordinates*3),{rawCoords:h,iris:P}=yn(M,a,y),m=N2(e);Math.abs(m)<30?(It(e,u,"left",null),It(e,h,"right",null)):m<1?It(e,u,"left",["EyeUpper0","EyeLower0"]):It(e,h,"right",["EyeUpper0","EyeLower0"]);let p=xn(e,f,"left"),k=xn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Ae=null,Ge=0;async function mn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await IA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=jA((c=t.face.detector)==null?void 0:c.rotation,h,e,((i=t.face.mesh)==null?void 0:i.enabled)?Ge:kt()),(l=t==null?void 0:t.filter)==null?void 0:l.equalization){let k=await pt(p.tensor);He.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)==null?void 0:x.enabled)if(!Ae)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=Ae.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=He.reshape(O,[-1,3]),J=await 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it(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Rn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Tn.image.cropAndResize(t,r,[0],o)}function wn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Bt(e,t=1.5){let o=it(e),A=Lt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Gt(e){let t=it(e),o=Lt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function L2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function kn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return L2(o)}var Mn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function fe(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function vn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(fe(e[n],B2(t,r)))}return o}function U5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Mn(t[0],t[1]),s=vn(r,n),a=Mn(-t[0],-t[1]);return vn(s,a)}function En(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-fe(t[0],o),-fe(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function J5(e,t){return[fe(e,t[0]),fe(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=wn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var F2=5,Sn=1.65,Wn=[0,5,9,13,17,1,2],V2=0,Z2=2,Cn=0,K5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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Object.keys(Un))A.annotations[l]=Un[l].map(x=>A.landmarks&&A.keypoints[x]?A.keypoints[x]:null)}Object.keys(n).forEach(y=>Y.dispose(n[y]))}return A}async function Ao(e,t){var n,r;if(!l0[0]||!l0[1]||!((n=l0[0])==null?void 0:n.inputs[0].shape)||!((r=l0[1])==null?void 0:r.inputs[0].shape))return[];se=[e.shape[2]||0,e.shape[1]||0],Ft++;let o=(t.hand.skipTime||0)>g()-to,A=Ft<(t.hand.skipFrames||0);return t.skipAllowed&&o&&A?o0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>g()-to,y=Ft<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(i=>oo(e,i,t))):t.skipAllowed&&a&&y&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(i=>oo(e,i,t))):(o0.boxes=await os(e,t),to=g(),o0.hands=await Promise.all(o0.boxes.map(i=>oo(e,i,t))),Ft=0);let c=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let i=0;i<o0.hands.length;i++){let 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a=e.keypoints[A]?[Math.abs(e.keypoints[r].position[0]-e.keypoints[A].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[A].position[0])]:[0,0],y=e.keypoints[n]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[n].position[0]),Math.abs(e.keypoints[r].position[0]-e.keypoints[n].position[0])]:[0,0];if(a[0]>a[1]||y[0]>y[1]){let c=e.keypoints[A];e.keypoints[A]=e.keypoints[n],e.keypoints[n]=c}}}function or(e){for(let t=0;t<e.length;t++)if(e[t]&&S0.keypoints[t]){let o=[Math.abs(e[t].positionRaw[0]-S0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-S0.keypoints[t].positionRaw[1])];o[0]<tr&&o[1]<tr?e[t]=S0.keypoints[t]:S0.keypoints[t]=e[t]}else S0.keypoints[t]=e[t];return e}function Ar(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;S0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ge.pad(e,S0.padding),o.resize=ge.image.resizeBilinear(o.pad,[t,t]);let A=ge.cast(o.resize,"int32");return Object.keys(o).forEach(n=>ge.dispose(o[n])),A}function nr(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+S0.padding[2][0]+S0.padding[2][1])/t[0]-S0.padding[2][0],A.position[1]*(t[1]+S0.padding[1][0]+S0.padding[1][1])/t[1]-S0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=ee(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,Ut=0,yo=Number.MAX_SAFE_INTEGER,Ee={boxes:[],bodies:[],last:0};async function sr(e){return T.initial&&(W0=null),W0?e.debug&&b("cached model:",W0.modelUrl):(Vt(["size"],e),W0=await G(e.body.modelPath)),Ut=W0.inputs[0].shape?W0.inputs[0].shape[2]:0,Ut<64&&(Ut=256),W0}async function ns(e,t,o){let A=e[0][0],n=[],r=0;for(let i=0;i<A.length;i++)if(r=A[i][2],r>t.body.minConfidence){let l=[A[i][1],A[i][0]];n.push({score:Math.round(100*r)/100,part:Dt[i],positionRaw:l,position:[Math.round((o.shape[2]||0)*l[0]),Math.round((o.shape[1]||0)*l[1])]})}r=n.reduce((i,l)=>l.score>i?l.score:i,0);let s=[],a=ee(n.map(i=>i.position),[o.shape[2],o.shape[1]]),y={};for(let[i,l]of Object.entries(qt)){let x=[];for(let d=0;d<l.length-1;d++){let u=n.find(M=>M.part===l[d]),f=n.find(M=>M.part===l[d+1]);u&&f&&u.score>(t.body.minConfidence||0)&&f.score>(t.body.minConfidence||0)&&x.push([u.position,f.position])}y[i]=x}let c={id:0,score:r,box:a.box,boxRaw:a.boxRaw,keypoints:n,annotations:y};return lo(c),s.push(c),s}async function rs(e,t,o){let A=[];for(let n=0;n<e[0].length;n++){let r=e[0][n],s=Math.round(100*r[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Dt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=ee(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(qt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};lo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function xo(e,t){if(!W0||!(W0==null?void 0:W0.inputs[0].shape))return[];t.skipAllowed||(Ee.boxes.length=0),yo++;let o=(t.body.skipTime||0)>g()-Ee.last,A=yo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ee.bodies:new Promise(async n=>{let r={};yo=0,r.input=Ar(e,Ut),r.res=W0==null?void 0:W0.execute(r.input),Ee.last=g();let s=await r.res.array();Ee.bodies=r.res.shape[2]===17?await ns(s,t,e):await rs(s,t,e);for(let a of Ee.bodies)nr(a,[e.shape[2]||1,e.shape[1]||1]),or(a.keypoints);Object.keys(r).forEach(a=>rr.dispose(r[a])),n(Ee.bodies)})}var M0=V(F());var qe,Jt=[],ir=0,co=Number.MAX_SAFE_INTEGER,Kt=0,Yt=2.5;async function lr(e){if(!qe||T.initial){qe=await G(e.object.modelPath);let t=Object.values(qe.modelSignature.inputs);Kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",qe.modelUrl);return qe}async function ss(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ne.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ne.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/Kt)),[k,C]=[P-Yt/y*p[0],m-Yt/y*p[1]],[O,j]=[P+Yt/y*p[2]-k,m+Yt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ne[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function fo(e,t){let o=(t.object.skipTime||0)>g()-ir,A=co<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Jt.length>0?(co++,Jt):(co=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Jt:new Promise(async 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xt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],as=xt.length,yt=xt.reduce((e,t,o)=>(e[t]=o,e),{}),is=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=is.map(([e,t])=>[yt[e],yt[t]]),xr=[["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"]];function cr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var mo=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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function OA(e){var t;return T.initial&&(q0=null),q0?e.debug&&b("cached model:",q0.modelUrl):q0=await G((t=e.face.detector)==null?void 0:t.modelPath),xe=q0.inputs[0].shape?q0.inputs[0].shape[2]:0,st=N.scalar(xe,"int32"),CA=N.tensor2d(EA(xe)),q0}function v2(e){let t={};t.boxStarts=N.slice(e,[0,1],[-1,2]),t.centers=N.add(t.boxStarts,CA),t.boxSizes=N.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=N.div(t.boxSizes,st),t.centersNormalized=N.div(t.centers,st),t.halfBoxSize=N.div(t.boxSizesNormalized,H.tf2),t.starts=N.sub(t.centersNormalized,t.halfBoxSize),t.ends=N.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=N.mul(t.starts,st),t.endNormalized=N.mul(t.ends,st);let o=N.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(A=>N.dispose(t[A])),o}async function IA(e,t){var a,y,c,i;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let o={};o.resized=N.image.resizeBilinear(e,[xe,xe]),o.div=N.div(o.resized,H.tf127),o.normalized=N.sub(o.div,H.tf05);let A=q0==null?void 0:q0.execute(o.normalized);if(Array.isArray(A)){let l=A.sort((x,d)=>x.size-d.size);o.concat384=N.concat([l[0],l[2]],2),o.concat512=N.concat([l[1],l[3]],2),o.concat=N.concat([o.concat512,o.concat384],1),o.batch=N.squeeze(o.concat,0)}else o.batch=N.squeeze(A);N.dispose(A),o.boxes=v2(o.batch),o.logits=N.slice(o.batch,[0,0],[-1,1]),o.sigmoid=N.sigmoid(o.logits),o.scores=N.squeeze(o.sigmoid),o.nms=await N.image.nonMaxSuppressionAsync(o.boxes,o.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let n=await o.nms.array(),r=[],s=await o.scores.data();for(let l=0;l<n.length;l++){let x=s[n[l]];if(x>(((i=t.face.detector)==null?void 0:i.minConfidence)||0)){let d={};d.bbox=N.slice(o.boxes,[n[l],0],[1,-1]),d.slice=N.slice(o.batch,[n[l],WA-1],[1,-1]),d.squeeze=N.squeeze(d.slice),d.landmarks=N.reshape(d.squeeze,[WA,-1]);let u=await d.bbox.data(),f={startPoint:[u[0],u[1]],endPoint:[u[2],u[3]],landmarks:await d.landmarks.array(),confidence:x},M=RA(f,[(e.shape[2]||0)/xe,(e.shape[1]||0)/xe]),h=Rt(M,t.face.scale||M2),P=wt(h);r.push(P),Object.keys(d).forEach(m=>N.dispose(d[m]))}}return Object.keys(o).forEach(l=>N.dispose(o[l])),r}var k0=V(F());var Et={};mt(Et,{connected:()=>w5,kpt:()=>R5});var R5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],w5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var R0=V(F()),LA=224,T2,R2=5,zt=[8,16,32,32,32];async function BA(){let e=[],t=0;for(;t<R2;){let o=0,A=t;for(;A<zt.length&&zt[A]===zt[t];)o+=2,A++;let n=zt[t],r=Math.ceil(LA/n),s=Math.ceil(LA/n);for(let a=0;a<r;++a)for(let y=0;y<s;++y)for(let c=0;c<o;++c)e.push({x:(y+.5)/s,y:(a+.5)/r});t=A}T2={x:R0.tensor1d(e.map(o=>o.x)),y:R0.tensor1d(e.map(o=>o.y))}}function ee(e,t=[1,1]){let o=[e.map(a=>a[0]),e.map(a=>a[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[A[0],A[1],n[0]-A[0],n[1]-A[1]],s=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{box:r,boxRaw:s}}function GA(e,t=[1,1]){let o=[e.map(c=>c[0]),e.map(c=>c[1])],A=[Math.min(...o[0]),Math.min(...o[1])],n=[Math.max(...o[0]),Math.max(...o[1])],r=[(A[0]+n[0])/2,(A[1]+n[1])/2],s=Math.max(r[0]-A[0],r[1]-A[1],-r[0]+n[0],-r[1]+n[1]),a=[Math.trunc(r[0]-s),Math.trunc(r[1]-s),Math.trunc(2*s),Math.trunc(2*s)],y=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:y}}function jt(e,t){let o=[e[2]*t,e[3]*t];return[e[0]-(o[0]-e[2])/2,e[1]-(o[1]-e[3])/2,o[0],o[1]]}var VA={initial:!0},w0={detector:null,landmarks:null},Ie={detector:[224,224],landmarks:[256,256]},k5=Number.MAX_SAFE_INTEGER,k2={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},Wt=null,at,ce=[[0,0],[0,0],[0,0],[0,0]],HA=0,FA=e=>1-1/(1+Math.exp(e));async function ZA(e){if(VA.initial&&(w0.detector=null),!w0.detector&&e.body.detector&&e.body.detector.modelPath){w0.detector=await G(e.body.detector.modelPath);let t=Object.values(w0.detector.modelSignature.inputs);Ie.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&w0.detector&&b("cached model:",w0.detector.modelUrl);return await BA(),w0.detector}async function XA(e){if(VA.initial&&(w0.landmarks=null),w0.landmarks)e.debug&&b("cached model:",w0.landmarks.modelUrl);else{w0.landmarks=await G(e.body.modelPath);let t=Object.values(w0.landmarks.modelSignature.inputs);Ie.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ie.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return w0.landmarks}async function E2(e,t){let o={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let A;if(at&&(o.cropped=k0.image.cropAndResize(e,[at],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let n=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],r=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ce=[[0,0],n,r,[0,0]],o.pad=k0.pad(o.cropped||e,ce),o.resize=k0.image.resizeBilinear(o.pad,[t,t]),A=k0.div(o.resize,H.tf255)}else e.shape[1]!==t?(o.resize=k0.image.resizeBilinear(o.cropped||e,[t,t]),A=k0.div(o.resize,H.tf255)):A=k0.div(o.cropped||e,H.tf255);return Object.keys(o).forEach(n=>k0.dispose(o[n])),A}function z2(e,t){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+ce[2][0]+ce[2][1])/t[0]-ce[2][0]),Math.trunc(o.position[1]*(t[1]+ce[1][0]+ce[1][1])/t[1]-ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(at)for(let o of e)o.positionRaw=[o.positionRaw[0]+at[1],o.positionRaw[1]+at[0],o.positionRaw[2]],o.position=[Math.trunc(o.positionRaw[0]*t[0]),Math.trunc(o.positionRaw[1]*t[1]),o.positionRaw[2]];return e}async function j2(e){let t=e.find(a=>a.part==="leftPalm"),o=e.find(a=>a.part==="leftWrist"),A=e.find(a=>a.part==="leftIndex");t.position[2]=((o.position[2]||0)+(A.position[2]||0))/2;let n=e.find(a=>a.part==="rightPalm"),r=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");n.position[2]=((r.position[2]||0)+(s.position[2]||0))/2}async function S2(e,t,o){var u;let A={};[A.ld,A.segmentation,A.heatmap,A.world,A.poseflag]=(u=w0.landmarks)==null?void 0:u.execute(e,k2.landmarks);let n=(await A.poseflag.data())[0],r=await A.ld.data(),s=await A.world.data();Object.keys(A).forEach(f=>k0.dispose(A[f]));let a=[],y=5;for(let f=0;f<r.length/y;f++){let M=FA(r[y*f+3]),h=FA(r[y*f+4]),P=Math.trunc(100*M*h*n)/100,m=[r[y*f+0]/Ie.landmarks[0],r[y*f+1]/Ie.landmarks[1],r[y*f+2]+0],p=[Math.trunc(o[0]*m[0]),Math.trunc(o[1]*m[1]),m[2]],k=[s[y*f+0],s[y*f+1],s[y*f+2]+0];a.push({part:R5[f],positionRaw:m,position:p,distance:k,score:P})}if(n<(t.body.minConfidence||0))return null;j2(a);let c=z2(a,o),i=c.map(f=>f.position),l=ee(i,[o[0],o[1]]),x={};for(let[f,M]of Object.entries(w5)){let h=[];for(let P=0;P<M.length-1;P++){let m=c.find(k=>k.part===M[P]),p=c.find(k=>k.part===M[P+1]);m&&p&&h.push([m.position,p.position])}x[f]=h}return{id:0,score:Math.trunc(100*n)/100,box:l.box,boxRaw:l.boxRaw,keypoints:c,annotations:x}}async function E5(e,t){let o=[e.shape[2]||0,e.shape[1]||0],A=(t.body.skipTime||0)>g()-HA,n=k5<(t.body.skipFrames||0);if(t.skipAllowed&&A&&n&&Wt!==null)k5++;else{let r={};r.landmarks=await E2(e,256),Wt=await S2(r.landmarks,t,o),Object.keys(r).forEach(s=>k0.dispose(r[s])),HA=g(),k5=0}return Wt?[Wt]:[]}var b0=V(F());var Ne=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var te,ve=0,z5=[],qA=0,j5=Number.MAX_SAFE_INTEGER;async function UA(e){if(T.initial&&(te=null),te)e.debug&&b("cached model:",te.modelUrl);else{te=await G(e.object.modelPath);let t=Object.values(te.modelSignature.inputs);ve=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return te}async function W2(e,t,o){if(!e)return[];let A={},n=[],r=await e.array();A.squeeze=b0.squeeze(e);let s=b0.split(A.squeeze,6,1);A.stack=b0.stack([s[1],s[0],s[3],s[2]],1),A.boxes=b0.squeeze(A.stack),A.scores=b0.squeeze(s[4]),A.classes=b0.squeeze(s[5]),b0.dispose([e,...s]),A.nms=await b0.image.nonMaxSuppressionAsync(A.boxes,A.scores,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence||0);let a=await A.nms.data(),y=0;for(let c of Array.from(a)){let i=Math.trunc(100*r[0][c][4])/100,l=r[0][c][5],x=Ne[l].label,[d,u]=[r[0][c][0]/ve,r[0][c][1]/ve],f=[d,u,r[0][c][2]/ve-d,r[0][c][3]/ve-u],M=[Math.trunc(f[0]*t[0]),Math.trunc(f[1]*t[1]),Math.trunc(f[2]*t[0]),Math.trunc(f[3]*t[1])];n.push({id:y++,score:i,class:l,label:x,box:M,boxRaw:f})}return Object.keys(A).forEach(c=>b0.dispose(A[c])),n}async function S5(e,t){let o=(t.object.skipTime||0)>g()-qA,A=j5<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&z5.length>0?(j5++,z5):(j5=0,new Promise(async n=>{let r=[e.shape[2]||0,e.shape[1]||0],s=b0.image.resizeBilinear(e,[ve,ve]),a=t.object.enabled?te==null?void 0:te.execute(s,["tower_0/detections"]):null;qA=g(),b0.dispose(s);let y=await W2(a,r,t);z5=y,n(y)}))}var K=V(F());var Ct={};mt(Ct,{connected:()=>C5,kpt:()=>W5});var 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n=N5<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),r=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>g()-$A;return t.skipAllowed&&r&&n&&_A===A&&Ot[o]&&Ot[o].length>0?(N5++,Ot[o]):(N5=0,new Promise(async y=>{var i,l;let c=[];if((i=t.face.emotion)!=null&&i.enabled){let x={},d=O0!=null&&O0.inputs[0].shape?O0.inputs[0].shape[2]:0;x.resize=B0.image.resizeBilinear(e,[d,d],!1),x.channels=B0.mul(x.resize,H.rgb),x.grayscale=B0.sum(x.channels,3,!0),x.grayscaleSub=B0.sub(x.grayscale,H.tf05),x.grayscaleMul=B0.mul(x.grayscaleSub,H.tf2),x.emotion=O0==null?void 0:O0.execute(x.grayscaleMul),$A=g();let u=await x.emotion.data();for(let f=0;f<u.length;f++)u[f]>(((l=t.face.emotion)==null?void 0:l.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*u[f])/100),emotion:O2[f]});c.sort((f,M)=>M.score-f.score),Object.keys(x).forEach(f=>B0.dispose(x[f]))}Ot[o]=c,_A=A,y(c)}))}var rn=V(F());var E0,B5=[],on=0,An=0,nn=Number.MAX_SAFE_INTEGER;async function sn(e){return T.initial&&(E0=null),E0?e.debug&&b("cached model:",E0.modelUrl):E0=await G(e.face.mobilefacenet.modelPath),E0}async function G5(e,t,o,A){var s,a;if(!E0)return[];let n=nn<(((s=t.face.embedding)==null?void 0:s.skipFrames)||0),r=(((a=t.face.embedding)==null?void 0:a.skipTime)||0)>g()-An;return t.skipAllowed&&r&&n&&on===A&&B5[o]?(nn++,B5[o]):new Promise(async y=>{var i;let c=[];if(((i=t.face.embedding)==null?void 0:i.enabled)&&(E0==null?void 0:E0.inputs[0].shape)){let l={};l.crop=rn.image.resizeBilinear(e,[E0.inputs[0].shape[2],E0.inputs[0].shape[1]],!1),l.data=E0==null?void 0:E0.execute(l.crop);let x=await l.data.data();c=Array.from(x)}B5[o]=c,on=A,An=g(),y(c)})}var He=V(F());var G0=V(F());var oe,de=0,I2=2.3,H5=L0.leftEyeLower0,F5=L0.rightEyeLower0,Le={leftBounds:[H5[0],H5[H5.length-1]],rightBounds:[F5[0],F5[F5.length-1]]},Be={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function cn(e){var t;return T.initial&&(oe=null),oe?e.debug&&b("cached model:",oe.modelUrl):oe=await G((t=e.face.iris)==null?void 0:t.modelPath),de=oe.inputs[0].shape?oe.inputs[0].shape[2]:0,de===-1&&(de=64),oe}function It(e,t,o,A){for(let n=0;n<h5.length;n++){let{key:r,indices:s}=h5[n],a=L0[`${o}${r}`];if(!A||A.includes(r))for(let y=0;y<s.length;y++){let c=s[y];e[a[y]]=[t[c][0],t[c][1],(t[c][2]+e[a[y]][2])/2]}}}var N2=e=>{let t=e[Le.leftBounds[0]][2],o=e[Le.rightBounds[0]][2];return t-o},ln=(e,t,o,A,n,r=!1)=>{let s=wt(Rt(wA([e[o],e[A]]),I2)),a=Oe(s),y=G0.image.cropAndResize(t,[[s.startPoint[1]/n,s.startPoint[0]/n,s.endPoint[1]/n,s.endPoint[0]/n]],[0],[de,de]);if(r&&T.kernels.includes("flipleftright")){let c=G0.image.flipLeftRight(y);G0.dispose(y),y=c}return{box:s,boxSize:a,crop:y}},yn=(e,t,o,A=!1)=>{let n=[];for(let r=0;r<Be.numCoordinates;r++){let s=e[r*3],a=e[r*3+1],y=e[r*3+2];n.push([(A?1-s/de:s/de)*o[0]+t.startPoint[0],a/de*o[1]+t.startPoint[1],y])}return{rawCoords:n,iris:n.slice(Be.index)}},xn=(e,t,o)=>{let A=e[L0[`${o}EyeUpper0`][Be.upperCenter]][2],n=e[L0[`${o}EyeLower0`][Be.lowerCenter]][2],r=(A+n)/2;return t.map((s,a)=>{let y=r;return a===2?y=A:a===4&&(y=n),[s[0],s[1],y]})};async function dn(e,t,o,A){if(!oe)return o.debug&&b("face mesh iris detection requested, but model is not loaded"),e;let{box:n,boxSize:r,crop:s}=ln(e,t,Le.leftBounds[0],Le.leftBounds[1],A,!0),{box:a,boxSize:y,crop:c}=ln(e,t,Le.rightBounds[0],Le.rightBounds[1],A,!0),i=G0.concat([s,c]);G0.dispose(s),G0.dispose(c);let l=oe.execute(i);G0.dispose(i);let x=await l.data();G0.dispose(l);let d=x.slice(0,Be.numCoordinates*3),{rawCoords:u,iris:f}=yn(d,n,r,!0),M=x.slice(Be.numCoordinates*3),{rawCoords:h,iris:P}=yn(M,a,y),m=N2(e);Math.abs(m)<30?(It(e,u,"left",null),It(e,h,"right",null)):m<1?It(e,u,"left",["EyeUpper0","EyeLower0"]):It(e,h,"right",["EyeUpper0","EyeLower0"]);let p=xn(e,f,"left"),k=xn(e,P,"right");return e.concat(p).concat(k)}var U0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Ae=null,Ge=0;async function mn(e,t){var a,y,c,i,l,x,d,u,f;let o=(((a=t.face.detector)==null?void 0:a.skipTime)||0)>g()-U0.timestamp,A=U0.skipped<(((y=t.face.detector)==null?void 0:y.skipFrames)||0);!t.skipAllowed||!o||!A||U0.boxes.length===0?(U0.boxes=await IA(e,t),U0.timestamp=g(),U0.skipped=0):U0.skipped++;let n=[],r=[],s=0;for(let M=0;M<U0.boxes.length;M++){let h=U0.boxes[M],P=0,m,p={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([P,m,p.tensor]=jA((c=t.face.detector)==null?void 0:c.rotation,h,e,(i=t.face.mesh)!=null&&i.enabled?Ge:kt()),(l=t==null?void 0:t.filter)!=null&&l.equalization){let k=await pt(p.tensor);He.dispose(p.tensor),p.tensor=k}if(p.boxScore=Math.round(100*h.confidence)/100,(x=t.face.mesh)!=null&&x.enabled)if(!Ae)t.debug&&b("face mesh detection requested, but model is not loaded");else{let[k,C,O]=Ae.execute(p.tensor),j=await C.data();p.faceScore=Math.round(100*j[0])/100;let L=He.reshape(O,[-1,3]),J=await 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it(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function Rn(e,t,o){let A=t.shape[1],n=t.shape[2],r=[[e.startPoint[1]/A,e.startPoint[0]/n,e.endPoint[1]/A,e.endPoint[0]/n]];return Tn.image.cropAndResize(t,r,[0],o)}function wn(e,t){let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],A=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],n=e.palmLandmarks.map(r=>[r[0]*t[0],r[1]*t[1]]);return{startPoint:o,endPoint:A,palmLandmarks:n,confidence:e.confidence}}function Bt(e,t=1.5){let o=it(e),A=Lt(e),n=[t*A[0]/2,t*A[1]/2],r=[o[0]-n[0],o[1]-n[1]],s=[o[0]+n[0],o[1]+n[1]];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function Gt(e){let t=it(e),o=Lt(e),n=Math.max(...o)/2,r=[t[0]-n,t[1]-n],s=[t[0]+n,t[1]+n];return{startPoint:r,endPoint:s,palmLandmarks:e.palmLandmarks}}function L2(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function kn(e,t){let o=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return L2(o)}var Mn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function fe(e,t){let o=0;for(let A=0;A<e.length;A++)o+=e[A]*t[A];return o}function B2(e,t){let o=[];for(let A=0;A<e.length;A++)o.push(e[A][t]);return o}function vn(e,t){let o=[],A=e.length;for(let n=0;n<A;n++){o.push([]);for(let r=0;r<A;r++)o[n].push(fe(e[n],B2(t,r)))}return o}function U5(e,t){let o=Math.cos(e),A=Math.sin(e),n=[[o,-A,0],[A,o,0],[0,0,1]],r=Mn(t[0],t[1]),s=vn(r,n),a=Mn(-t[0],-t[1]);return vn(s,a)}function En(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],o=[e[0][2],e[1][2]],A=[-fe(t[0],o),-fe(t[1],o)];return[t[0].concat(A[0]),t[1].concat(A[1]),[0,0,1]]}function J5(e,t){return[fe(e,t[0]),fe(e,t[1])]}var 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Object.keys(o).forEach(n=>B.dispose(o[n])),A}normalizeLandmarks(t,o){let A={};A.reshape=B.reshape(t,[-1,7,2]),A.div=B.div(A.reshape,this.inputSizeTensor),A.landmarks=B.add(A.div,this.anchors[o]);let n=B.mul(A.landmarks,this.inputSizeTensor);return Object.keys(A).forEach(r=>B.dispose(A[r])),n}async predict(t,o){let A={};A.resize=B.image.resizeBilinear(t,[this.inputSize,this.inputSize]),A.div=B.div(A.resize,H.tf127),A.image=B.sub(A.div,H.tf1),A.batched=this.model.execute(A.image),A.predictions=B.squeeze(A.batched),A.slice=B.slice(A.predictions,[0,0],[-1,1]),A.sigmoid=B.sigmoid(A.slice),A.scores=B.squeeze(A.sigmoid);let n=await A.scores.data();A.boxes=B.slice(A.predictions,[0,1],[-1,4]),A.norm=this.normalizeBoxes(A.boxes),A.nms=await B.image.nonMaxSuppressionAsync(A.norm,A.scores,3*o.hand.maxDetected,o.hand.iouThreshold,o.hand.minConfidence);let r=await A.nms.array(),s=[];for(let a of r){let y={};y.box=B.slice(A.norm,[a,0],[1,-1]),y.slice=B.slice(A.predictions,[a,5],[1,14]),y.norm=this.normalizeLandmarks(y.slice,a),y.palmLandmarks=B.reshape(y.norm,[-1,2]);let c=await y.box.data(),i=c.slice(0,2),l=c.slice(2,4),x=await y.palmLandmarks.array(),d={startPoint:i,endPoint:l,palmLandmarks:x,confidence:n[a]},u=wn(d,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);s.push(u),Object.keys(y).forEach(f=>B.dispose(y[f]))}return Object.keys(A).forEach(a=>B.dispose(A[a])),s}};var j0=V(F());var F2=5,Sn=1.65,Wn=[0,5,9,13,17,1,2],V2=0,Z2=2,Cn=0,K5=class{constructor(t,o){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");this.handDetector=t,this.handPoseModel=o,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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e;S0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],o.pad=ge.pad(e,S0.padding),o.resize=ge.image.resizeBilinear(o.pad,[t,t]);let A=ge.cast(o.resize,"int32");return Object.keys(o).forEach(n=>ge.dispose(o[n])),A}function nr(e,t){e.keypoints=e.keypoints.filter(A=>A&&A.position);for(let A of e.keypoints)A.position=[A.position[0]*(t[0]+S0.padding[2][0]+S0.padding[2][1])/t[0]-S0.padding[2][0],A.position[1]*(t[1]+S0.padding[1][0]+S0.padding[1][1])/t[1]-S0.padding[1][0]],A.positionRaw=[A.position[0]/t[0],A.position[1]/t[1]];let o=ee(e.keypoints.map(A=>A.position),t);return e.box=o.box,e.boxRaw=o.boxRaw,e}var W0,Ut=0,yo=Number.MAX_SAFE_INTEGER,Ee={boxes:[],bodies:[],last:0};async function sr(e){return T.initial&&(W0=null),W0?e.debug&&b("cached 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a=[];for(let l=0;l<17;l++){let x=r[3*l+2];if(x>t.body.minConfidence){let d=[r[3*l+1],r[3*l+0]];a.push({part:Dt[l],score:Math.round(100*x)/100,positionRaw:d,position:[Math.round((o.shape[2]||0)*d[0]),Math.round((o.shape[1]||0)*d[1])]})}}let y=ee(a.map(l=>l.position),[o.shape[2],o.shape[1]]),c={};for(let[l,x]of Object.entries(qt)){let d=[];for(let u=0;u<x.length-1;u++){let f=a.find(h=>h.part===x[u]),M=a.find(h=>h.part===x[u+1]);f&&M&&f.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&d.push([f.position,M.position])}c[l]=d}let i={id:n,score:s,box:y.box,boxRaw:y.boxRaw,keypoints:[...a],annotations:c};lo(i),A.push(i)}}return A.sort((n,r)=>r.score-n.score),A.length>t.body.maxDetected&&(A.length=t.body.maxDetected),A}async function xo(e,t){if(!W0||!(W0!=null&&W0.inputs[0].shape))return[];t.skipAllowed||(Ee.boxes.length=0),yo++;let o=(t.body.skipTime||0)>g()-Ee.last,A=yo<(t.body.skipFrames||0);return t.skipAllowed&&o&&A?Ee.bodies:new Promise(async n=>{let r={};yo=0,r.input=Ar(e,Ut),r.res=W0==null?void 0:W0.execute(r.input),Ee.last=g();let s=await r.res.array();Ee.bodies=r.res.shape[2]===17?await ns(s,t,e):await rs(s,t,e);for(let a of Ee.bodies)nr(a,[e.shape[2]||1,e.shape[1]||1]),or(a.keypoints);Object.keys(r).forEach(a=>rr.dispose(r[a])),n(Ee.bodies)})}var M0=V(F());var qe,Jt=[],ir=0,co=Number.MAX_SAFE_INTEGER,Kt=0,Yt=2.5;async function lr(e){if(!qe||T.initial){qe=await G(e.object.modelPath);let t=Object.values(qe.modelSignature.inputs);Kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}else e.debug&&b("cached model:",qe.modelUrl);return qe}async function ss(e,t,o){let A=0,n=[];for(let y of[1,2,4])M0.tidy(async()=>{let c=y*13,i=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)===Ne.length)),l=M0.squeeze(e.find(f=>f.shape[1]===c**2&&(f.shape[2]||0)<Ne.length)),d=await l.reshape([-1,4,l.shape[1]/4]).argMax(2).array(),u=await i.array();for(let f=0;f<i.shape[0];f++)for(let M=0;M<i.shape[1];M++){let h=u[f][M];if(h>(o.object.minConfidence||0)&&M!==61){let P=(.5+Math.trunc(f%c))/c,m=(.5+Math.trunc(f/c))/c,p=d[f].map(q=>q*(c/y/Kt)),[k,C]=[P-Yt/y*p[0],m-Yt/y*p[1]],[O,j]=[P+Yt/y*p[2]-k,m+Yt/y*p[3]-C],L=[k,C,O,j];L=L.map(q=>Math.max(0,Math.min(q,1)));let J=[L[0]*t[0],L[1]*t[1],L[2]*t[0],L[3]*t[1]],Z={id:A++,score:Math.round(100*h)/100,class:M+1,label:Ne[M].label,box:J.map(q=>Math.trunc(q)),boxRaw:L};n.push(Z)}}});e.forEach(y=>M0.dispose(y));let r=n.map(y=>[y.boxRaw[1],y.boxRaw[0],y.boxRaw[3],y.boxRaw[2]]),s=n.map(y=>y.score),a=[];if(r&&r.length>0){let y=await M0.image.nonMaxSuppressionAsync(r,s,o.object.maxDetected,o.object.iouThreshold,o.object.minConfidence);a=await y.data(),M0.dispose(y)}return n=n.filter((y,c)=>a.includes(c)).sort((y,c)=>c.score-y.score),n}async function fo(e,t){let o=(t.object.skipTime||0)>g()-ir,A=co<(t.object.skipFrames||0);return t.skipAllowed&&o&&A&&Jt.length>0?(co++,Jt):(co=0,!T.kernels.includes("mod")||!T.kernels.includes("sparsetodense")?Jt:new Promise(async 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xt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],as=xt.length,yt=xt.reduce((e,t,o)=>(e[t]=o,e),{}),is=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],T1=is.map(([e,t])=>[yt[e],yt[t]]),xr=[["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"]];function cr(e){let t=e.reduce(({maxX:o,maxY:A,minX:n,minY:r},{position:{x:s,y:a}})=>({maxX:Math.max(o,s),maxY:Math.max(A,a),minX:Math.min(n,s),minY:Math.min(r,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function dr(e,[t,o],[A,n]){let r=t/A,s=o/n,a=(c,i)=>({id:i,score:c.score,boxRaw:[c.box[0]/n,c.box[1]/A,c.box[2]/n,c.box[3]/A],box:[Math.trunc(c.box[0]*s),Math.trunc(c.box[1]*r),Math.trunc(c.box[2]*s),Math.trunc(c.box[3]*r)],keypoints:c.keypoints.map(({score:l,part:x,position:d})=>({score:l,part:x,position:[Math.trunc(d.x*s),Math.trunc(d.y*r)],positionRaw:[d.x/A,d.y/A]})),annotations:{}});return e.map((c,i)=>a(c,i))}var mo=class{constructor(t,o){R(this,"priorityQueue");R(this,"numberOfElements");R(this,"getElementValue");this.priorityQueue=new 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l=Math.abs(i.annotations.leftEyeIris[3][0]-i.annotations.leftEyeIris[1][0])/2,x=Math.abs(i.annotations.leftEyeIris[4][1]-i.annotations.leftEyeIris[2][1])/2;n.ellipse(i.annotations.leftEyeIris[0][0],i.annotations.leftEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(i.annotations&&i.annotations.rightEyeIris&&i.annotations.rightEyeIris[0]){n.strokeStyle=A.useDepth?"rgba(255, 200, 255, 0.3)":A.color,n.beginPath();let l=Math.abs(i.annotations.rightEyeIris[3][0]-i.annotations.rightEyeIris[1][0])/2,x=Math.abs(i.annotations.rightEyeIris[4][1]-i.annotations.rightEyeIris[2][1])/2;n.ellipse(i.annotations.rightEyeIris[0][0],i.annotations.rightEyeIris[0][1],l,x,0,0,2*Math.PI),n.stroke(),A.fillPolygons&&(n.fillStyle=A.useDepth?"rgba(255, 255, 200, 0.3)":A.color,n.fill())}if(A.drawGaze&&((r=i.rotation)==null?void 0:r.angle)&&typeof Path2D!="undefined"){n.strokeStyle="pink";let 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             C
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@@ -108,7 +108,7 @@ var Dr=Object.create;var $e=Object.defineProperty;var qr=Object.getOwnPropertyDe
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Description:"),e.config.async?i=((q=e.config.face.description)==null?void 0:q.enabled)?D5(x[w].tensor||A0.tensor([]),e.config,w,x.length):null:(e.state="run:description",o=g(),i=((x0=e.config.face.description)==null?void 0:x0.enabled)?await D5(x[w].tensor||A0.tensor([]),e.config,w,x.length):null,e.performance.description=T.perfadd?(e.performance.description||0)+Math.trunc(g()-o):Math.trunc(g()-o)),e.analyze("End Description:"),e.config.async&&([A,r,s,a,i,n,y,c]=await Promise.all([A,r,s,a,i,n,y,c])),e.analyze("Finish Face:"),((E=e.config.face.ssrnet)==null?void 0:E.enabled)&&A&&r&&(i={...i,age:A.age,gender:r.gender,genderScore:r.genderScore}),((C0=e.config.face.gear)==null?void 0:C0.enabled)&&n&&(i={...i,age:n.age,gender:n.gender,genderScore:n.genderScore,race:n.race}),((ie=e.config.face.mobilefacenet)==null?void 0:ie.enabled)&&a&&(i.descriptor=a),!((v=e.config.face.iris)==null?void 0:v.enabled);let n0=x[w].annotations&&x[w].annotations.leftEyeIris&&x[w].annotations.leftEyeIris[0]&&x[w].annotations.rightEyeIris&&x[w].annotations.rightEyeIris[0]&&x[w].annotations.leftEyeIris.length>0&&x[w].annotations.rightEyeIris.length>0&&x[w].annotations.leftEyeIris[0]!==null&&x[w].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[w].annotations.leftEyeIris[3][0]-x[w].annotations.leftEyeIris[1][0]),Math.abs(x[w].annotations.rightEyeIris[4][1]-x[w].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=((a0=e.config.face.detector)==null?void 0:a0.return)?A0.squeeze(x[w].tensor):null;A0.dispose(x[w].tensor),x[w].tensor&&delete x[w].tensor;let I={...x[w],id:w};(i==null?void 0:i.age)&&(I.age=i.age),(i==null?void 0:i.gender)&&(I.gender=i.gender),(i==null?void 0:i.genderScore)&&(I.genderScore=i==null?void 0:i.genderScore),(i==null?void 0:i.descriptor)&&(I.embedding=i==null?void 0:i.descriptor),(i==null?void 0:i.race)&&(I.race=i==null?void 0:i.race),s&&(I.emotion=s),y&&(I.real=y),c&&(I.live=c),n0&&n0!==0&&(I.iris=Math.trunc(500/n0/11.7)/100),Q&&(I.rotation=Q),y0&&(I.tensor=y0),l.push(I),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),l};var Or=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=e[o].keypoints.find(y=>y.part==="leftWrist"),n=e[o].keypoints.find(y=>y.part==="rightWrist"),r=e[o].keypoints.find(y=>y.part==="nose");r&&A&&n&&A.position[1]<r.position[1]&&n.position[1]<r.position[1]?t.push({body:o,gesture:"i give up"}):r&&A&&A.position[1]<r.position[1]?t.push({body:o,gesture:"raise left hand"}):r&&n&&n.position[1]<r.position[1]&&t.push({body:o,gesture:"raise right hand"});let s=e[o].keypoints.find(y=>y.part==="leftShoulder"),a=e[o].keypoints.find(y=>y.part==="rightShoulder");s&&a&&Math.abs(s.positionRaw[1]-a.positionRaw[1])>.1&&t.push({body:o,gesture:`leaning ${s.position[1]>a.position[1]?"left":"right"}`})}return t},Ir=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++)if(e[o].mesh&&e[o].mesh.length>450){let A=(e[o].mesh[33][2]||0)-(e[o].mesh[263][2]||0),n=e[o].mesh[33][0]-e[o].mesh[263][0];Math.abs(A/n)<=.15?t.push({face:o,gesture:"facing center"}):t.push({face:o,gesture:`facing ${A<0?"left":"right"}`}),Math.abs(e[o].mesh[374][1]-e[o].mesh[386][1])/Math.abs(e[o].mesh[443][1]-e[o].mesh[450][1])<.2&&t.push({face:o,gesture:"blink left eye"}),Math.abs(e[o].mesh[145][1]-e[o].mesh[159][1])/Math.abs(e[o].mesh[223][1]-e[o].mesh[230][1])<.2&&t.push({face:o,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[o].mesh[13][1]-e[o].mesh[14][1])/Math.abs(e[o].mesh[10][1]-e[o].mesh[152][1]));a>10&&t.push({face:o,gesture:`mouth ${Math.trunc(a)}% open`});let y=e[o].mesh[152][2]||0;Math.abs(y)>10&&t.push({face:o,gesture:`head ${y<0?"up":"down"}`})}return t},Nr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!e[o].annotations||!e[o].annotations.leftEyeIris||!e[o].annotations.leftEyeIris[0]||!e[o].annotations.rightEyeIris||!e[o].annotations.rightEyeIris[0])continue;let A=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],n=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],r=Math.abs(A*n),s=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],a=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],y=Math.abs(s*a),c=!1;Math.abs(r-y)/Math.max(r,y)<.25&&(c=!0,t.push({iris:o,gesture:"facing center"}));let l=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],x=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(l>.06||x>.06)&&(c=!1),l>x?l>.05&&t.push({iris:o,gesture:"looking right"}):x>.05&&t.push({iris:o,gesture:"looking left"});let d=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],u=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(u<.01||d<.01||u>.022||d>.022)&&(c=!1),(u<.01||d<.01)&&t.push({iris:o,gesture:"looking down"}),(u>.022||d>.022)&&t.push({iris:o,gesture:"looking up"}),c&&t.push({iris:o,gesture:"looking center"})}return t},Lr=e=>{if(!e)return[];let t=[];for(let o=0;o<e.length;o++){let A=[];if(e[o].annotations)for(let[n,r]of Object.entries(e[o].annotations))n!=="palmBase"&&Array.isArray(r)&&r[0]&&A.push({name:n.toLowerCase(),position:r[0]});if(A&&A.length>0){let n=A.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:o,gesture:`${n.name} forward`});let r=A.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:o,gesture:`${r.name} up`})}if(e[o].keypoints){let n=Fn(e[o].keypoints);for(let r of n)t.push({hand:o,gesture:r.name})}}return t};var z={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},Oo=0;function Br(e,t){var s,a,y,c,i,l,x,d,u,f,M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie;let o=g();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let A=Date.now()-e.timestamp,n=A<1e3?8-Math.log(A+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let v=0;v<e.body.length;v++){let a0=e.body[v].box.map((I,U)=>((n-1)*z.body[v].box[U]+I)/n),w=e.body[v].boxRaw.map((I,U)=>((n-1)*z.body[v].boxRaw[U]+I)/n),Q=e.body[v].keypoints.map((I,U)=>{var 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Object.keys(e.hand[v].annotations))n0[y0]=e.hand[v].annotations[y0]&&e.hand[v].annotations[y0][0]?e.hand[v].annotations[y0].map((I,U)=>I.map((Y0,K0)=>((n-1)*z.hand[v].annotations[y0][U][K0]+Y0)/n)):null;z.hand[v]={...e.hand[v],box:a0,boxRaw:w,keypoints:Q,annotations:n0}}if(!z.face||e.face.length!==z.face.length)z.face=JSON.parse(JSON.stringify(e.face));else for(let v=0;v<e.face.length;v++){let a0=e.face[v].box.map((Q,n0)=>((n-1)*z.face[v].box[n0]+Q)/n),w=e.face[v].boxRaw.map((Q,n0)=>((n-1)*z.face[v].boxRaw[n0]+Q)/n);if(e.face[v].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(x=e.face[v].rotation)==null?void 0:x.matrix,Q.angle={roll:((n-1)*(((u=(d=z.face[v].rotation)==null?void 0:d.angle)==null?void 0:u.roll)||0)+(((M=(f=e.face[v].rotation)==null?void 0:f.angle)==null?void 0:M.roll)||0))/n,yaw:((n-1)*(((P=(h=z.face[v].rotation)==null?void 0:h.angle)==null?void 0:P.yaw)||0)+(((p=(m=e.face[v].rotation)==null?void 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v=e.persons;if(!z.persons||v.length!==z.persons.length)z.persons=JSON.parse(JSON.stringify(v));else for(let a0=0;a0<v.length;a0++)z.persons[a0].box=v[a0].box.map((w,Q)=>((n-1)*z.persons[a0].box[Q]+w)/n)}e.gesture&&(z.gesture=e.gesture);let r=g();return Oo=T.perfadd?Oo+Math.round(r-o):Math.round(r-o),e.performance&&(z.performance={...e.performance,interpolate:Oo}),z}function $t(e,t,o={order:2,multiplier:25}){let A=0;for(let n=0;n<e.length;n++){let r=!o.order||o.order===2?e[n]-t[n]:Math.abs(e[n]-t[n]);A+=!o.order||o.order===2?r*r:r**o.order}return(o.multiplier||20)*A}var Gr=(e,t,o,A)=>{if(e===0)return 1;let n=t===2?Math.sqrt(e):e**(1/t),r=(1-n/100-o)/(A-o);return Math.max(Math.min(r,1),0)};function Hr(e,t,o={order:2,multiplier:25,min:.2,max:.8}){let A=$t(e,t,o);return Gr(A,o.order||2,o.min||0,o.max||1)}function 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n0=x[w].annotations&&x[w].annotations.leftEyeIris&&x[w].annotations.leftEyeIris[0]&&x[w].annotations.rightEyeIris&&x[w].annotations.rightEyeIris[0]&&x[w].annotations.leftEyeIris.length>0&&x[w].annotations.rightEyeIris.length>0&&x[w].annotations.leftEyeIris[0]!==null&&x[w].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(x[w].annotations.leftEyeIris[3][0]-x[w].annotations.leftEyeIris[1][0]),Math.abs(x[w].annotations.rightEyeIris[4][1]-x[w].annotations.rightEyeIris[2][1]))/t.shape[2]:0,y0=(a0=e.config.face.detector)!=null&&a0.return?A0.squeeze(x[w].tensor):null;A0.dispose(x[w].tensor),x[w].tensor&&delete x[w].tensor;let I={...x[w],id:w};i!=null&&i.age&&(I.age=i.age),i!=null&&i.gender&&(I.gender=i.gender),i!=null&&i.genderScore&&(I.genderScore=i==null?void 0:i.genderScore),i!=null&&i.descriptor&&(I.embedding=i==null?void 0:i.descriptor),i!=null&&i.race&&(I.race=i==null?void 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vo,this.draw={options:ae,canvas:(o,A)=>zr(o,A),face:(o,A,n)=>Eo(o,A,n),body:(o,A,n)=>zo(o,A,n),hand:(o,A,n)=>jo(o,A,n),gesture:(o,A,n)=>ko(o,A,n),object:(o,A,n)=>So(o,A,n),person:(o,A,n)=>Er(o,A,n),all:(o,A,n)=>jr(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=un,this.faceUVMap=hn,this.gl=$,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(le)),this.config.backend=t}validate(t){return A5(le,t||this.config)}now(){return g()}image(t,o=!0){return We(t,this.config,o)}async segmentation(t,o){return br(t,o,this.config)}enhance(t){return X5(t)}compare(t,o){return AA(this.config,t,o)}async init(){await _t(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=g(),A=Object.values(this.models).filter(s=>s).length;t&&(this.config=c0(this.config,t)),this.env.initial&&(this.config.debug&&b(`version: 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A.kernels)n[a.name]?n[a.name]+=a.kernelTimeMs:n[a.name]=a.kernelTimeMs;let r=[];Object.entries(n).forEach(a=>r.push({name:a[0],ms:a[1]})),r.sort((a,y)=>y.ms-a.ms),r.length=20;let s={};for(let a of r)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async A=>{var M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie,v,a0,w,Q,n0;this.state="config";let n;this.config=c0(this.config,o),this.state="check";let r=et(this,o5).call(this,t);r&&(b(r,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:r}));let s=g();await _t(this),await this.load(),n=g(),this.state="image";let a=await We(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=g(),this.config.skipAllowed=await oA(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Check Changed:");let y=[],c=[],i=[],l=[];this.state="detect:face",this.config.async?(y=this.config.face.enabled?Co(this,a.tensor):[],this.performance.face&&delete this.performance.face):(n=g(),y=this.config.face.enabled?await Co(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(y=await y),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?c0(this.config,{body:{maxDetected:this.config.face.enabled?1*y.length:1}}):this.config;this.config.async?(((M=this.config.body.modelPath)==null?void 0:M.includes("posenet"))?c=this.config.body.enabled?go(a.tensor,x):[]:((h=this.config.body.modelPath)==null?void 0:h.includes("blazepose"))?c=this.config.body.enabled?E5(a.tensor,x):[]:((P=this.config.body.modelPath)==null?void 0:P.includes("efficientpose"))?c=this.config.body.enabled?I5(a.tensor,x):[]:((m=this.config.body.modelPath)==null?void 0:m.includes("movenet"))&&(c=this.config.body.enabled?xo(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(n=g(),((p=this.config.body.modelPath)==null?void 0:p.includes("posenet"))?c=this.config.body.enabled?await go(a.tensor,x):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("blazepose"))?c=this.config.body.enabled?await E5(a.tensor,x):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("efficientpose"))?c=this.config.body.enabled?await I5(a.tensor,x):[]:((O=this.config.body.modelPath)==null?void 0:O.includes("movenet"))&&(c=this.config.body.enabled?await xo(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?c0(this.config,{hand:{maxDetected:this.config.face.enabled?2*y.length:1}}):this.config;this.config.async?(((L=(j=this.config.hand.detector)==null?void 0:j.modelPath)==null?void 0:L.includes("handdetect"))?i=this.config.hand.enabled?$5(a.tensor,d):[]:((Z=(J=this.config.hand.detector)==null?void 0:J.modelPath)==null?void 0:Z.includes("handtrack"))&&(i=this.config.hand.enabled?Ao(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),((x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)==null?void 0:x0.includes("handdetect"))?i=this.config.hand.enabled?await $5(a.tensor,d):[]:((C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:C0.includes("handtrack"))&&(i=this.config.hand.enabled?await Ao(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((ie=this.config.object.modelPath)==null?void 0:ie.includes("nanodet"))?l=this.config.object.enabled?fo(a.tensor,this.config):[]:((v=this.config.object.modelPath)==null?void 0:v.includes("centernet"))&&(l=this.config.object.enabled?S5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),((a0=this.config.object.modelPath)==null?void 0:a0.includes("nanodet"))?l=this.config.object.enabled?await fo(a.tensor,this.config):[]:((w=this.config.object.modelPath)==null?void 0:w.includes("centernet"))&&(l=this.config.object.enabled?await S5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Ir(y),...Or(c),...Lr(i),...Nr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Vr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Ke=new WeakMap,dt=new WeakMap,ft=new WeakMap,o5=new WeakMap;module.exports=_r(Is);0&&(module.exports={Human,defaults,env});
+2Q==`;var Io=V(F());async function Ss(e){let t=(n,r="application/octet-stream")=>fetch(`data:${r};base64,${n}`).then(s=>s.blob()),o,A;switch(e.config.warmup){case"face":o=await t(e5);break;case"body":case"full":o=await t(t5);break;default:o=null}if(o){let n=await createImageBitmap(o);A=await e.detect(n,e.config),n.close()}return A}async function Ws(e){return new Promise(t=>{let o;switch(e.config.warmup){case"face":o="data:image/jpeg;base64,"+e5;break;case"full":case"body":o="data:image/jpeg;base64,"+t5;break;default:o=null}let A;if(typeof Image!="undefined")A=new Image;else if(T.Image)A=new T.Image;else return;A.onload=async()=>{let n=h0(A.naturalWidth,A.naturalHeight);if(!n)b("Warmup: Canvas not found"),t(void 0);else{let r=n.getContext("2d");r&&r.drawImage(A,0,0);let s=await e.image(n),a=await e.detect(s.tensor,e.config);t(a)}},o?A.src=o:t(void 0)})}async function Cs(e){let t=n=>Buffer.from(n,"base64"),o;e.config.warmup==="face"?o=t(e5):o=t(t5);let A;if("node"in Io){let n=Io.node.decodeJpeg(o),r=n.expandDims(0);e.tf.dispose(n),A=await e.detect(r,e.config),e.tf.dispose(r)}else e.config.debug&&b("Warmup tfjs-node not loaded");return A}async function Zr(e,t){let o=g();if(e.state="warmup",t&&(e.config=c0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:g(),persons:[],error:null};let A;return new Promise(async n=>{typeof createImageBitmap=="function"?A=await Ss(e):typeof Image!="undefined"||T.Canvas!==void 0?A=await Ws(e):A=await Cs(e);let r=g();e.config.debug&&b("Warmup",e.config.warmup,Math.round(r-o),"ms"),e.emit("warmup"),n(A)})}var Ke,dt,ft,o5,Xr=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");tt(this,Ke,void 0);tt(this,dt,void 0);tt(this,ft,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!et(this,dt))return;let o=this.tf.engine().state.numTensors,A=et(this,Ke);ot(this,Ke,o);let n=o-A;n!==0&&b(...t,n)});tt(this,o5,t=>{if(!et(this,ft))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof D0.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(o){return"backend not loaded"}return null});R(this,"similarity",Hr);R(this,"distance",$t);R(this,"match",Fr);R(this,"emit",t=>{var o;this.events&&this.events.dispatchEvent&&((o=this.events)==null||o.dispatchEvent(new Event(t)))});this.env=T,le.wasmPath=D0.version["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${D0.version_core}/dist/`,le.modelBasePath=T.browser?"../models/":"file://models/",le.backend=T.browser?"humangl":"tensorflow",this.version=s5,Object.defineProperty(this,"version",{value:s5}),this.config=JSON.parse(JSON.stringify(le)),Object.seal(this.config),t&&(this.config=c0(this.config,t)),this.config.cacheModels=typeof indexedDB!="undefined",rA(this.config),this.tf=D0,this.state="idle",ot(this,Ke,0),ot(this,dt,!1),ot(this,ft,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new vo,this.draw={options:ae,canvas:(o,A)=>zr(o,A),face:(o,A,n)=>Eo(o,A,n),body:(o,A,n)=>zo(o,A,n),hand:(o,A,n)=>jo(o,A,n),gesture:(o,A,n)=>ko(o,A,n),object:(o,A,n)=>So(o,A,n),person:(o,A,n)=>Er(o,A,n),all:(o,A,n)=>jr(o,A,n)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=un,this.faceUVMap=hn,this.gl=$,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(le)),this.config.backend=t}validate(t){return A5(le,t||this.config)}now(){return g()}image(t,o=!0){return We(t,this.config,o)}async segmentation(t,o){return br(t,o,this.config)}enhance(t){return X5(t)}compare(t,o){return AA(this.config,t,o)}async init(){await _t(this,!0),await this.tf.ready()}async load(t){this.state="load";let o=g(),A=Object.values(this.models).filter(s=>s).length;t&&(this.config=c0(this.config,t)),this.env.initial&&(this.config.debug&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`),await _t(this)||b("error: backend check failed"),await D0.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("environment:",this.env),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await Pr(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==A&&(await Mr(this),this.emit("load"));let r=Math.trunc(g()-o);r>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+r:r)}next(t=this.result){return Br(t,this.config)}async warmup(t){let o=g(),A=await Zr(this,t),n=g();return this.performance.warmup=Math.trunc(n-o),A}async profile(t,o){let A=await this.tf.profile(()=>this.detect(t,o)),n={};for(let a of A.kernels)n[a.name]?n[a.name]+=a.kernelTimeMs:n[a.name]=a.kernelTimeMs;let r=[];Object.entries(n).forEach(a=>r.push({name:a[0],ms:a[1]})),r.sort((a,y)=>y.ms-a.ms),r.length=20;let s={};for(let a of r)s[a.name]=a.ms;return s}async detect(t,o){return this.state="detect",new Promise(async A=>{var M,h,P,m,p,k,C,O,j,L,J,Z,q,x0,E,C0,ie,v,a0,w,Q,n0;this.state="config";let n;this.config=c0(this.config,o),this.state="check";let r=et(this,o5).call(this,t);r&&(b(r,t),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:r}));let s=g();await _t(this),await this.load(),n=g(),this.state="image";let a=await We(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),A({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:g(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),n=g(),this.config.skipAllowed=await oA(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(g()-n):Math.trunc(g()-n),this.analyze("Check Changed:");let y=[],c=[],i=[],l=[];this.state="detect:face",this.config.async?(y=this.config.face.enabled?Co(this,a.tensor):[],this.performance.face&&delete this.performance.face):(n=g(),y=this.config.face.enabled?await Co(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(y=await y),this.analyze("Start Body:"),this.state="detect:body";let x=this.config.body.maxDetected===-1?c0(this.config,{body:{maxDetected:this.config.face.enabled?1*y.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?c=this.config.body.enabled?go(a.tensor,x):[]:(h=this.config.body.modelPath)!=null&&h.includes("blazepose")?c=this.config.body.enabled?E5(a.tensor,x):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?I5(a.tensor,x):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?xo(a.tensor,x):[]),this.performance.body&&delete this.performance.body):(n=g(),(p=this.config.body.modelPath)!=null&&p.includes("posenet")?c=this.config.body.enabled?await go(a.tensor,x):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await E5(a.tensor,x):[]:(C=this.config.body.modelPath)!=null&&C.includes("efficientpose")?c=this.config.body.enabled?await I5(a.tensor,x):[]:(O=this.config.body.modelPath)!=null&&O.includes("movenet")&&(c=this.config.body.enabled?await xo(a.tensor,x):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?c0(this.config,{hand:{maxDetected:this.config.face.enabled?2*y.length:1}}):this.config;this.config.async?((L=(j=this.config.hand.detector)==null?void 0:j.modelPath)!=null&&L.includes("handdetect")?i=this.config.hand.enabled?$5(a.tensor,d):[]:(Z=(J=this.config.hand.detector)==null?void 0:J.modelPath)!=null&&Z.includes("handtrack")&&(i=this.config.hand.enabled?Ao(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(n=g(),(x0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&x0.includes("handdetect")?i=this.config.hand.enabled?await $5(a.tensor,d):[]:(C0=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&C0.includes("handtrack")&&(i=this.config.hand.enabled?await Ao(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((ie=this.config.object.modelPath)!=null&&ie.includes("nanodet")?l=this.config.object.enabled?fo(a.tensor,this.config):[]:(v=this.config.object.modelPath)!=null&&v.includes("centernet")&&(l=this.config.object.enabled?S5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(n=g(),(a0=this.config.object.modelPath)!=null&&a0.includes("nanodet")?l=this.config.object.enabled?await fo(a.tensor,this.config):[]:(w=this.config.object.modelPath)!=null&&w.includes("centernet")&&(l=this.config.object.enabled?await S5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([y,c,i,l]=await Promise.all([y,c,i,l])),this.state="detect:gesture";let u=[];this.config.gesture.enabled&&(n=g(),u=[...Ir(y),...Or(c),...Lr(i),...Nr(y)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(g()-n):Math.trunc(g()-n)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(g()-s):Math.trunc(g()-s);let f=((n0=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:n0.shape)||[];this.result={face:y,body:c,hand:i,gesture:u,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return Vr(y,c,i,u,f)}},D0.dispose(a.tensor),this.emit("detect"),this.state="idle",A(this.result)})}};Ke=new WeakMap,dt=new WeakMap,ft=new WeakMap,o5=new WeakMap;module.exports=_r(Is);0&&(module.exports={Human,defaults,env});
 /**
  * Human main module
  * @default Human Library
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index 9f978ea2..124a3a43 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -4,65 +4,66 @@
   author: <https://github.com/vladmandic>'
 */
 
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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Jf.nextTensorId++}nextVariableId(){return Jf.nextVariableId++}clone(e){let t=M.runKernel(Ma,{x:e}),n={x:e},s=a=>({x:()=>{let i="float32",o={x:a},u={dtype:i};return M.runKernel(Sa,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Yf(e,this.backendName)!=null))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){let s=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=s-t-r-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=Df(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(Df(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Yf(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();o=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let y=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,b,y);let v=y.map(x=>{if(x.rank!=null)return x;let{dataId:k,shape:C,dtype:T}=x;return this.makeTensorFromDataId(k,C,T)});if(s){let x=this.getTensorsForGradient(h,f,v);n=this.saveTensorsForBackwardMode(x)}return v}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};i=()=>{let m=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,f));let g=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,m,g),g}}let{inputs:l,attrs:c}=e,p=Df(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(u,l,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=Jv(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],i;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>a[l]);return i.concat(o)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&ir(e[0])&&(r=e.map(o=>El(o)));let a=s.write(r,t,n),i=new et(t,n,a,this.nextTensorId());if(this.trackTensor(i,s),n==="string"){let o=this.state.tensorInfo.get(a),u=Uw(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r=new et(t,n,e,this.nextTensorId());return this.trackTensor(r,s),r}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new md(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*jf(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 md||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;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*jf(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let 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,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let 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(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=Jv(e);o!=null&&(s=o.gradFunc),s!=null&&(i.gradient=u=>(u=u.map((l,c)=>{if(l==null){let p=n[c],d=Gd(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return l}),s(u.length>1?u:u[0],r,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Ag(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof et,()=>"The result y returned by f() must be a tensor.");let a=P$(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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2 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),qu(i,o,u,c,l,a)}var vE=L({batchNorm2d_:yE});function xE(e,t,n,s,r,a){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(i.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`),O(o.rank===3||o.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`),O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),qu(i,o,u,c,l,a)}var wE=L({batchNorm3d_:xE});function kE(e,t,n,s,r,a){let i=_(e,"x","batchNorm"),o=_(t,"mean","batchNorm"),u=_(n,"variance","batchNorm"),l;r!=null&&(l=_(r,"scale","batchNorm"));let c;return s!=null&&(c=_(s,"offset","batchNorm")),O(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),O(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),qu(i,o,u,c,l,a)}var IE=L({batchNorm4d_:kE});function SE(e,t,n){let s=_(e,"x","bincount"),r=_(t,"weights","bincount");O(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got 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+          ${a.shape}`);let i={data:s,indices:r,segmentIds:a};return M.runKernel(sp,i)}var wO=L({sparseSegmentMean_:xO});function kO(e,t,n){let s=_(e,"data","sparseSegmentSum"),r=_(t,"indices","sparseSegmentSum","int32"),a=_(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
          ${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
-         ${a.shape}`);let i={data:s,indices:r,segmentIds:a};return M.runKernel(ap,i)}var IO=L({sparseSegmentSum_:kO});function SO(e,t,n,s,r,a,i,o){let u=_(e,"data","stringNGrams","string");if(u.dtype!=="string")throw new Error("Data must be of datatype string");if(u.shape.length!==1)throw new Error(`Data must be a vector, saw: ${u.shape}`);let l=_(t,"dataSplits","stringNGrams");if(l.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let c={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:i,preserveShortSequences:o},p={data:u,dataSplits:l},d=M.runKernel(op,p,c);return{nGrams:d[0],nGramsSplits:d[1]}}var CO=L({stringNGrams_:SO});function NO(e,t,n=!0){let s=_(e,"input","stringSplit","string"),r=_(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},i={input:s,delimiter:r},o=M.runKernel(Tg,i,a);return{indices:o[0],values:o[1],shape:o[2]}}var TO=L({stringSplit_:NO});function $O(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return M.runKernel($g,r,s)}var _O=L({stringToHashBucketFast_:$O}),ope={fft:ub,ifft:Id,rfft:lb,irfft:fI},upe={hammingWindow:rF,hannWindow:SI,frame:CI,stft:uF},ds={flipLeftRight:pF,grayscaleToRGB:fF,resizeNearestNeighbor:MF,resizeBilinear:PF,rotateWithOffset:gF,cropAndResize:cF,nonMaxSuppression:yF,nonMaxSuppressionAsync:NF,nonMaxSuppressionWithScore:$F,nonMaxSuppressionWithScoreAsync:AF,nonMaxSuppressionPadded:RF,nonMaxSuppressionPaddedAsync:FF,threshold:VF,transform:UF},AO={bandPart:HF,gramSchmidt:jF,qr:XF},lpe={absoluteDifference:JF,computeWeightedLoss:Ys,cosineDistance:tO,hingeLoss:sO,huberLoss:aO,logLoss:oO,meanSquaredError:lO,sigmoidCrossEntropy:pO,softmaxCrossEntropy:mO},Wc={sparseFillEmptyRows:bO,sparseReshape:vO,sparseSegmentMean:wO,sparseSegmentSum:IO},zf={stringNGrams:CO,stringSplit:TO,stringToHashBucketFast:_O},$r=class extends Ok{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else this.applyGradients(r);return Re(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return MR(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty($r,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var hb=class extends $r{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;j(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(ln(ie(u,this.epsilon)),ln(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};hb.className="Adadelta";Nr(hb);var fb=class extends $r{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Ol(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;j(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,ln(ie(o,M.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(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);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};fb.className="Adagrad";Nr(fb);var mb=class extends $r{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(ln(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(ca(this.beta1,this.iterations_+1)),this.accBeta2.assign(ca(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};mb.className="Adam";Nr(mb);var gb=class extends $r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=Tr(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(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)}};gb.className="Adamax";Nr(gb);var Ip=class extends $r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Ip.className="SGD";Nr(Ip);var bb=class extends Ip{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Ie(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulations[s]==null&&(this.accumulations[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(!1))});let a=this.accumulations[s].variable,i=Array.isArray(e)?e[s].tensor:e[n];i!=null&&j(()=>{let o,u=ie(V(this.m,a),i);this.useNesterov?o=ie(V(this.c,ie(i,V(u,this.m))),r):o=ie(V(this.c,u),r),a.assign(u),r.assign(o)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Re(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);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};bb.className="Momentum";Nr(bb);var yb=class extends $r{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=M.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:j(()=>je(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:j(()=>je(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedMeanSquares[s].variable,u=this.accumulatedMoments[s].variable;j(()=>{let l=ie(V(o,this.decay),V(ct(i),1-this.decay));if(this.centered){let c=this.accumulatedMeanGrads[s].variable,p=ie(V(c,this.decay),V(i,1-this.decay)),d=xe(V(i,this.learningRate),ln(ge(l,ie(ct(p),this.epsilon)))),h=ie(V(u,this.momentum),d);o.assign(l),c.assign(p),u.assign(h);let f=ge(r,h);r.assign(f)}else{let c=ie(V(o,this.decay),V(ct(i),1-this.decay)),p=ie(V(u,this.momentum),xe(V(i,this.learningRate),ln(ie(c,this.epsilon))));o.assign(c),u.assign(p);let d=ge(r,p);r.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Re(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Re(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Re(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.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)}};yb.className="RMSProp";Nr(yb);var Vr=class{static sgd(e){return new Ip(e)}static momentum(e,t,n=!1){return new bb(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new yb(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new mb(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new hb(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new gb(e,t,n,s,r)}static adagrad(e,t=.1){return new fb(e,t)}},Fi={sgd:Vr.sgd,momentum:Vr.momentum,adadelta:Vr.adadelta,adagrad:Vr.adagrad,rmsprop:Vr.rmsprop,adamax:Vr.adamax,adam:Vr.adam},EO=(()=>typeof requestAnimationFrame!="undefined"?requestAnimationFrame:typeof setImmediate!="undefined"?setImmediate:e=>e())();function RO(){return new Promise(e=>EO(()=>e()))}var N={};Ae(N,{ERF_A1:()=>UO,ERF_A2:()=>GO,ERF_A3:()=>HO,ERF_A4:()=>qO,ERF_A5:()=>jO,ERF_P:()=>WO,PARALLELIZE_THRESHOLD:()=>vb,SELU_SCALE:()=>AI,SELU_SCALEALPHA:()=>_I,applyActivation:()=>wp,assertAndGetBroadcastShape:()=>it,assertAxesAreInnerMostDims:()=>qR,assertParamsConsistent:()=>DO,assignToTypedArray:()=>JO,axesAreInnerMostDims:()=>Yg,calculateShapes:()=>Ck,checkEinsumDimSizes:()=>aP,checkPadOnDimRoundingMode:()=>pn,combineLocations:()=>tI,complexWithEvenIndex:()=>YO,complexWithOddIndex:()=>QO,computeConv2DInfo:()=>Fl,computeConv3DInfo:()=>Wk,computeDefaultPad:()=>Wg,computeDilation2DInfo:()=>tE,computeOptimalWindowSize:()=>OO,computeOutAndReduceShapes:()=>nI,computeOutShape:()=>FO,computePool2DInfo:()=>Vk,computePool3DInfo:()=>nE,convertConv2DDataFormat:()=>Uk,decodeEinsumEquation:()=>sP,eitherStridesOrDilationsAreOne:()=>Ps,expandShapeToKeepDim:()=>la,exponent:()=>tP,exponents:()=>eP,fromStringArrayToUint8:()=>NP,fromUint8ToStringArray:()=>CP,getAxesPermutation:()=>sI,getBroadcastDims:()=>vk,getComplexWithIndex:()=>ZO,getEinsumComputePath:()=>iP,getEinsumPermutation:()=>rP,getFusedBiasGradient:()=>xp,getFusedDyActivation:()=>vp,getImageCenter:()=>PO,getInnerMostAxes:()=>jR,getPermuted:()=>MO,getReductionAxes:()=>_t,getReshaped:()=>zO,getReshapedPermuted:()=>LO,getSliceBeginCoords:()=>BO,getSliceSize:()=>VO,getSparseFillEmptyRowsIndicesDenseShapeMismatch:()=>cP,getSparseFillEmptyRowsNegativeIndexErrorMessage:()=>dP,getSparseFillEmptyRowsOutOfRangeIndexErrorMessage:()=>pP,getSparseReshapeEmptyTensorZeroOutputDimErrorMessage:()=>mP,getSparseReshapeInputOutputMismatchErrorMessage:()=>bP,getSparseReshapeInputOutputMultipleErrorMessage:()=>gP,getSparseReshapeMultipleNegativeOneOutputDimErrorMessage:()=>hP,getSparseReshapeNegativeOutputDimErrorMessage:()=>fP,getSparseSegmentReductionIndicesOutOfRangeErrorMessage:()=>wP,getSparseSegmentReductionNegativeSegmentIdsErrorMessage:()=>yP,getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage:()=>vP,getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage:()=>xP,getUndoAxesPermutation:()=>Qg,isIdentityPermutation:()=>oP,log:()=>I$,mergeRealAndImagArrays:()=>KO,prepareAndValidate:()=>Ik,prepareSplitSize:()=>lP,segment_util:()=>EI,shouldFuse:()=>kp,slice_util:()=>wt,splitRealAndImagArrays:()=>XO,tupleValuesAreOne:()=>fr,upcastType:()=>yn,validateInput:()=>Lg,validateUpdateShape:()=>Mg,warn:()=>ar});function DO(e,t){let n=e[0].length;e.forEach((r,a)=>{O(r.length===n,()=>`Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`)}),O(t>=0&&t<n,()=>`Error in concat${n}D: axis must be between 0 and ${n-1}.`);let s=e[0];e.forEach((r,a)=>{for(let i=0;i<n;i++)O(i===t||r[i]===s[i],()=>`Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`)})}function FO(e,t){let n=e[0].slice();for(let s=1;s<e.length;s++)n[t]+=e[s][t];return n}var vb=30;function OO(e){return e<=vb?e:hd(e,Math.floor(Math.sqrt(e)))}function PO(e,t,n){let s=n*(typeof e=="number"?e:e[0]),r=t*(typeof e=="number"?e:e[1]);return[s,r]}function zO(e,t,n,s=!0){let r=[];if(s)r=r.concat(t.slice(0)),r.push(e[0]/n),r=r.concat(e.slice(1));else{r=r.concat(e[0]);let a=t.length;for(let i=0;i<a;++i)r=r.concat([e[i+1]/t[i],t[i]]);r=r.concat(e.slice(a+1))}return r}function MO(e,t,n=!0){let s=[];if(n){s.push(t);for(let 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Support for duplicate axes in input is not implemented yet.`);u[d]=[];for(let h=0;h<a[d].length;++h)u[d].push(o.indexOf(a[d][h]))}let l=o.length,c=r.length,p=[];for(let d=c;d<l;++d)p.push(d);return{allDims:o,summedDims:p,idDims:u}}function rP(e,t){let n=new Array(e);n.fill(-1);for(let r=0;r<t.length;++r)n[t[r]]=r;let s=[];for(let r=0;r<e;++r)n[r]===-1&&s.push(r);return n=n.filter(r=>r!==-1),{permutationIndices:n,expandDims:s}}function aP(e,t,n){let s=new Array(e);for(let r=0;r<n.length;++r){let a=n[r].shape;for(let i=0;i<t[r].length;++i)s[t[r][i]]===void 0?s[t[r][i]]=a[i]:O(s[t[r][i]]===a[i],()=>`Expected dimension ${s[t[r][i]]} at axis ${i} of input shaped ${JSON.stringify(a)}, but got dimension ${a[i]}`)}}function iP(e,t){let n=e,s=[],r=0;e.length===0&&n.push(-1),r=e.length+1;for(let i=0;i<r;++i)s.push([]);let a=[];for(let i=0;i<n.length;++i){let o=n[i],u=uP(t,o);for(let l of u)a.indexOf(l)===-1&&(s[i].push(l),a.push(l))}return{path:n,steps:s}}function oP(e){return e.every((t,n)=>t===n)}function uP(e,t){let n=[];for(let s=0;s<e.length;++s)(e[s].length===0||e[s].indexOf(t)!==-1||t===-1)&&n.push(s);return n}function lP(e,t,n=0){let s=[];if(typeof t=="number")O(e.shape[n]%t===0,()=>"Number of splits must evenly divide the axis."),s=new Array(t).fill(e.shape[n]/t);else{let r=t.reduce((i,o)=>(o===-1&&(i+=1),i),0);O(r<=1,()=>"There should be only one negative value in split array.");let a=t.indexOf(-1);if(a!==-1){let i=t.reduce((o,u)=>u>0?o+u:o);t[a]=e.shape[n]-i}O(e.shape[n]===t.reduce((i,o)=>i+o),()=>"The sum of sizes must match the size of the axis dimension."),s=t}return s}function cP(e){return`Received SparseTensor with denseShape[0] = 0 but
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a={skipEmpty:n},i={input:s,delimiter:r},o=M.runKernel(Ng,i,a);return{indices:o[0],values:o[1],shape:o[2]}}var TO=L({stringSplit_:NO});function $O(e,t){let n=_(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return M.runKernel(Tg,r,s)}var _O=L({stringToHashBucketFast_:$O}),ope={fft:ob,ifft:kd,rfft:ub,irfft:fI},upe={hammingWindow:rF,hannWindow:SI,frame:CI,stft:uF},ds={flipLeftRight:pF,grayscaleToRGB:fF,resizeNearestNeighbor:MF,resizeBilinear:PF,rotateWithOffset:gF,cropAndResize:cF,nonMaxSuppression:yF,nonMaxSuppressionAsync:NF,nonMaxSuppressionWithScore:$F,nonMaxSuppressionWithScoreAsync:AF,nonMaxSuppressionPadded:RF,nonMaxSuppressionPaddedAsync:FF,threshold:VF,transform:UF},AO={bandPart:HF,gramSchmidt:jF,qr:XF},lpe={absoluteDifference:JF,computeWeightedLoss:Ys,cosineDistance:tO,hingeLoss:sO,huberLoss:aO,logLoss:oO,meanSquaredError:lO,sigmoidCrossEntropy:pO,softmaxCrossEntropy:mO},Vc={sparseFillEmptyRows:bO,sparseReshape:vO,sparseSegmentMean:wO,sparseSegmentSum:IO},Pf={stringNGrams:CO,stringSplit:TO,stringToHashBucketFast:_O},$r=class extends Ok{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(i=>({name:i.name,tensor:r[i.name]}));this.applyGradients(a)}else this.applyGradients(r);return Re(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return MR(e,t)}dispose(){this.iterations_!=null&&Re(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ie(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 ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty($r,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var pb=class extends $r{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:j(()=>je(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:j(()=>je(r).variable(a))});let i=Array.isArray(e)?e[s].tensor:e[n];if(i==null)return;let o=this.accumulatedGrads[s].variable,u=this.accumulatedUpdates[s].variable;j(()=>{let l=ie(V(o,this.rho),V(ct(i),1-this.rho)),c=V(xe(ln(ie(u,this.epsilon)),ln(ie(o,this.epsilon))),i),p=ie(V(u,this.rho),V(ct(c),1-this.rho));o.assign(l),u.assign(p);let d=ie(V(c,-this.learningRate),r);r.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Re(this.accumulatedGrads.map(e=>e.variable)),Re(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};pb.className="Adadelta";Nr(pb);var hb=class extends $r{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=M.registeredVariables[n];this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:j(()=>Fl(r.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let i=this.accumulatedGrads[s].variable;j(()=>{let o=ie(i,ct(a));i.assign(o);let u=ie(V(xe(a,ln(ie(o,M.backend.epsilon()))),-this.learningRate),r);r.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Re(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);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};hb.className="Adagrad";Nr(hb);var fb=class extends $r{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],j(()=>{this.accBeta1=Ie(t).variable(),this.accBeta2=Ie(n).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=ge(1,this.accBeta2);t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:j(()=>je(i).variable(o))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:j(()=>je(i).variable(o))});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedSecondMoment[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=ie(V(c,this.beta2),V(ct(u),1-this.beta2)),h=xe(p,n),f=xe(d,s);l.assign(p),c.assign(d);let m=ie(V(xe(h,ie(ln(f),this.epsilon)),-this.learningRate),i);i.assign(m)}),this.accBeta1.assign(V(this.accBeta1,this.beta1)),this.accBeta2.assign(V(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Re(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),j(()=>{this.accBeta1.assign(ca(this.beta1,this.iterations_+1)),this.accBeta2.assign(ca(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.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)}};fb.className="Adam";Nr(fb);var mb=class extends $r{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],j(()=>{this.iteration=Ie(0).variable(),this.accBeta1=Ie(t).variable()}),s==null&&(this.epsilon=M.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);j(()=>{let n=ge(1,this.accBeta1),s=xe(-this.learningRate,ie(V(this.iteration,this.decay),1));t.forEach((r,a)=>{let i=M.registeredVariables[r],o=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:je(i).variable(o)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:je(i).variable(o)});let u=Array.isArray(e)?e[a].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[a].variable,c=this.accumulatedWeightedInfNorm[a].variable,p=ie(V(l,this.beta1),V(u,1-this.beta1)),d=V(c,this.beta2),h=Mt(u),f=Tr(d,h);l.assign(p),c.assign(f);let m=ie(V(xe(s,n),xe(p,ie(f,this.epsilon))),i);i.assign(m)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(V(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Re(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Re(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)}};mb.className="Adamax";Nr(mb);var kp=class extends $r{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=M.registeredVariables[n];j(()=>{let i=ie(V(this.c,r),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Ht(Ie(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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Dt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},$s=class{constructor(e,t,n,s,r,a,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=i,this.id=UI(),a!=null&&(this.originalName=MI(a),this.name=LI(this.originalName)),this.rank=t.length}},kL=0,Fp=class{constructor(e,t){this.callArgs=t,this.id=kL++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},IL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=IL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+"_"+Dp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ps(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return gn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return gn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return Zr(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(gn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=gn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=SL(e),i=this.computeOutputShape(a),o,u=CL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ps(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Cd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return ym(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=ym(t);for(let r=0;r<s.length;++r){let a=s[r],i=t[r],o=e[r];if(!w.arraysEqual(a.shape,o.shape))throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Ob(n)})}addWeight(e,t,n,s,r,a,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new U(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=o!=null?o():ht("zeros"));let u=s.apply(t,n),l=new xL(u,n,e,a,i);return u.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=dt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,i=null){let o=dt(e);t=dt(t),n=dt(n),s=dt(s),r=Sd(r),a=Sd(a);let u=[],l=[],c=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),c.push(p.tensorIndex);new Fp({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function SL(e){e=dt(e);let t=[];for(let n of e)t.push(n.shape);return gn(t)}function CL(e){return"float32"}function GI(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let i=s.inputTensors[a],o=s.inboundLayers[a],u=s.nodeIndices[a],l=GI(i,o,u);for(let c of l)r.indexOf(c)===-1&&r.push(c)}return r}}}var Qo=class extends Ge{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Dp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new U("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new $s(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new U(`Cannot pass any input to an InputLayer's apply() method. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function aB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Dp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,"input layer has >1 nodes"),Cs(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Qo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,C)=>{(x==null||k==null||C==null)&&(x=b.sourceLayer,k=b.nodeIndex,C=b.tensorIndex);let T=x.inboundNodes[k];if(v.indexOf(T)!==-1)throw new ps(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(T)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(T)===-1&&v.push(T);let E=T.inboundLayers.length;for(let A=0;A<E;A++){let P=T.inputTensors[A],R=T.inboundLayers[A],F=T.nodeIndices[A],$=T.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(T);v.indexOf(T)>=0;)v.splice(v.indexOf(T),1);i.push(T)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],C=b.nodeIndices[x],T=k.inboundNodes[C],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(y+1,E),n[T.id]=T}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Uc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],C=a[x.id];return k<C?-1:k>C?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Uc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new ps(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new ps(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Ob(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${t0}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=xm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Xr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return Au(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=pa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Sd(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(gn(c)),d=Sd(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return gn(r)}runInternalGraph(e,t){t==null&&(t=pa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],C=y[v];n[x.id]=[k,C]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],C=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let T=r[x];if(T.inboundNodes.length<=k){i(m,g);return}let E=T.inboundNodes[k];b.push(E.outputTensors[C])}b.length>0&&m.apply(gn(b),y)}function u(m){let g=m.name,b=fs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!AM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function iB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function n0(e,t){return iB(e,t,"classWeight")}async function s0(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Hu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,"float32")}else return null}function oB(e,t){return V(e,t)}var uB=32;function r0(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Fx("input",e.inputNames,n),i=Fx("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Fx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function lB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function cB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Td?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=zx(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=zx(t,this.feedOutputNames,r,!1,"target"),bB(e,t,null),yB(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. 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this.metrics=="string"||typeof this.metrics=="function")return[Ws(qc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(qc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(qc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Zu(e.optimizer_config),n=fs(t),s;if(typeof e.loss=="string")s=qr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>qr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=qr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>qr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=qr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=_n.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await _n.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:xB,generatedBy:`TensorFlow.js tfjs-layers v${t0}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await _n.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=_n.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Ex(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Ex(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className="Model";ae.registerClass(pr);var i0=class extends pr{};i0.className="Functional";ae.registerClass(i0);async function wB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Zu(n),r=fs(s,t);if(e.weightsManifest!=null){let a=await _n.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function kB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return IB(e,void 0,t)}async function IB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=fs(Zu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=SB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function SB(e,t){let n=_n.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Sm=class extends pr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Dp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Sm||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=HI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=GI(this.outputs[0])}this.inboundNodes=[],new Fp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Sm))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=fs(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Wb=Sm;Wb.className="Sequential";ae.registerClass(Wb);function cpe(e){return new pr(e)}function dpe(e){return new Wb(e)}function ppe(e,t){return t==null&&(t={}),kB(e,t)}function CB(e){return HI(e)}function hpe(e,t){Pb.registerCallbackConstructor(e,t)}var wn=class extends ae.Serializable{getConfig(){return{}}},o0=class extends wn{apply(e,t=1){return XM(e,t)}};o0.className="elu";ae.registerClass(o0);var u0=class extends wn{apply(e){return cI(e)}};u0.className="selu";ae.registerClass(u0);var l0=class extends wn{apply(e){return Xs(e)}};l0.className="relu";ae.registerClass(l0);var c0=class extends wn{apply(e){return j(()=>bp(6,Xs(e)))}};c0.className="relu6";ae.registerClass(c0);var d0=class extends wn{apply(e){return e}};d0.className="linear";ae.registerClass(d0);var p0=class extends wn{apply(e){return qs(e)}};p0.className="sigmoid";ae.registerClass(p0);var h0=class extends wn{apply(e){return QM(e)}};h0.className="hardSigmoid";ae.registerClass(h0);var f0=class extends wn{apply(e){return Pl(e)}};f0.className="softplus";ae.registerClass(f0);var m0=class extends wn{apply(e){return YM(e)}};m0.className="softsign";ae.registerClass(m0);var g0=class extends wn{apply(e){return qu(e)}};g0.className="tanh";ae.registerClass(g0);var Ub=class extends wn{apply(e,t=-1){return ob(e,t)}};Ub.className="softmax";ae.registerClass(Ub);var b0=class extends wn{apply(e,t=-1){return eI(e,t)}};b0.className="logSoftmax";ae.registerClass(b0);var y0=class extends wn{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};y0.className="swish";ae.registerClass(y0);var v0=class extends wn{apply(e){return j(()=>V(e,qu(Pl(e))))}};v0.className="mish";ae.registerClass(v0);function br(e){return e.getClassName()}function Uf(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function yr(e){if(e==null){let t={};return t.className="linear",t.config={},Uf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Uf(t)}else return e instanceof wn?e:Uf(e)}function Gb(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var x0=class extends ae.Serializable{},Ul=class extends x0{constructor(e){super();Gb(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ye(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ye(V(this.l2,Vl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Ul.className="L1L2";ae.registerClass(Ul);function NB(e){return Gb(e),new Ul({l1:e!=null?e.l1:null,l2:0})}function TB(e){return Gb(e),new Ul({l2:e!=null?e.l2:null,l1:0})}var Lx={l1l2:"L1L2"};function at(e){return xb(e)}function Bx(e,t={}){return Ml(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Lx?Lx[e]:e,config:{}};return Bx(n)}else return e instanceof x0?e:Bx(e)}var Hb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Bn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Hb.className="ReLU";ae.registerClass(Hb);var qb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return Kg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};qb.className="LeakyReLU";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),nb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:at(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};jb.className="PReLU";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return fp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Kb.className="ELU";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="ThresholdedReLU";ae.registerClass(Xb);var Yb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ub().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Yb.className="Softmax";ae.registerClass(Yb);function Xi(e,t,n){if(typeof e=="number")return pa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!HM(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. 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instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=qk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Vx(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Qb(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=da.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function _B(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=w0(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Kk(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Zb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Zb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Xi(t.kernelSize,e,"kernelSize"),this.strides=Xi(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Xi(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!wb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Gl=class extends Zb{constructor(e,t){super(e,t);this.kernel=null,Gl.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=PI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Vx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$B(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Vx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_B(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=ms(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:at(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},k0=class extends Gl{constructor(e){super(2,e);k0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!wb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},zp=k0;zp.className="Conv2D";ae.registerClass(zp);var I0=class extends Gl{constructor(e){super(3,e);I0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},Mp=I0;Mp.className="Conv3D";ae.registerClass(Mp);var Jb=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jb.className="Conv2DTranspose";ae.registerClass(Jb);var ey=class extends Mp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=qE(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};ey.className="Conv3DTranspose";ae.registerClass(ey);var S0=class extends Gl{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=JD(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.pointwiseRegularizer=at(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};S0.className="SeparableConv";var ty=class extends S0{constructor(e){super(2,e)}};ty.className="SeparableConv2D";ae.registerClass(ty);var C0=class extends Gl{constructor(e){super(1,e);C0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!wb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ny=C0;ny.className="Conv1D";ae.registerClass(ny);var sy=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Gc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Gc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Gc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Gc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sy.className="Cropping2D";ae.registerClass(sy);var ry=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,WM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ry.className="UpSampling2D";ae.registerClass(ry);function AB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=bs()),St(r);let i=Qb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=hp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var ay=class extends Zb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=AB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=ms(t,this.kernelSize[0],this.padding,this.strides[0]),a=ms(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};ay.className="DepthwiseConv2D";ae.registerClass(ay);function N0(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function T0(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=On(r,-1)),r=qe(r,l)),s&&(t=Yn(t,0),r!=null&&(r=Yn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],C=ge(Xn(k),k),T=ie(V(v[0],k),V(d[0],C)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,C)));return{output:T,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Qn(c,1)),[p,g,d]})}var $0=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Vp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){bm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");bm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=T0((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ye(t,[1,2]),t=Bl(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?mm(t,[1,n]):t):this.cell.stateSize>1?[mm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$0.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=fs(s,n);return new e(Object.assign(t,{cell:r}))}},_r=$0;_r.className="RNN";ae.registerClass(_r);var Hl=class extends Ge{},Lp=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Lp.className="SimpleRNNCell";ae.registerClass(Lp);var iy=class extends _r{constructor(e){e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};iy.className="SimpleRNN";ae.registerClass(iy);var Bp=class extends Hl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Ln(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Ln(l,3,l.rank-1),[b,y]=Ln(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Bp.className="GRUCell";ae.registerClass(Bp);var oy=class extends _r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Bp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};oy.className="GRU";ae.registerClass(oy);var ql=class extends Hl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Zi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Zi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends es{apply(i,o){let u=r.apply([a]),l=new Np().apply([a]),c=r.apply([a*2]);return Nx(Nx(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Ln(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};ql.className="LSTMCell";ae.registerClass(ql);var uy=class extends _r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new ql(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};uy.className="LSTM";ae.registerClass(uy);var Vp=class extends Hl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){bm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Zr(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(fs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return ym(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Ob(t)}};Vp.className="StackedRNNCells";ae.registerClass(Vp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):WI(t(),n),o=()=>Wl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _0=class extends _r{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=ms(u,s[0],r,a[0],i[0]),p=ms(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};_0.className="ConvRNN2D";var Wp=class extends ql{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Xi(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Xi(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Un(this.padding),this.dataFormat=a||"channelsLast",St(this.dataFormat),this.dilationRate=Xi(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends es{apply(c,p){let d=u.apply([l]),h=zn([l]),f=u.apply([l*2]);return Tb([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,C]=Ln(this.kernel.read(),i,y),[T,E,A,P]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,T,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,C,P,this.padding);let[R,F,$,z]=Ln(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ua(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ua(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Wp.className="ConvLSTM2DCell";ae.registerClass(Wp);var ly=class extends _0{constructor(e){let t=new Wp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};ly.className="ConvLSTM2D";ae.registerClass(ly);var Up=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Wl(()=>WI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Up.className="Dropout";ae.registerClass(Up);var cy=class extends Up{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};cy.className="SpatialDropout1D";ae.registerClass(cy);var dy=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=PI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};dy.className="Dense";ae.registerClass(dy);var py=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return KM(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};py.className="Flatten";ae.registerClass(py);var hy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};hy.className="Activation";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),qM(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};fy.className="RepeatVector";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};my.className="Reshape";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};gy.className="Permute";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return dm(Xu(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=dm(Xu(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};by.className="Masking";ae.registerClass(by);var yy=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Xu(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Sp(n,"int32"));let s=VI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:at(this.embeddingsRegularizer),activityRegularizer:at(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};yy.className="Embedding";ae.registerClass(yy);var mi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Bl(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:On(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},vy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};vy.className="Add";ae.registerClass(vy);var xy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};xy.className="Multiply";ae.registerClass(xy);var wy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};wy.className="Average";ae.registerClass(wy);var ky=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Tr(t,e[n]);return t})}};ky.className="Maximum";ae.registerClass(ky);var Iy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=bp(t,e[n]);return t})}};Iy.className="Minimum";ae.registerClass(Iy);var Sy=class extends mi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>Tb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Xn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(On(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return Bk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Concatenate";ae.registerClass(Sy);function Cu(e,t){for(;e<0;)e+=t;return e}function EB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ye(V(e,t),a[0]):o=ye(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=On(o,1)),o})}var Cy=class extends mi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Cu(r,e[a].shape.length)):s=[Cu(this.axes,t.shape.length),Cu(this.axes,n.shape.length)],this.normalize&&(t=Nd(t,s[0]),n=Nd(n,s[1])),EB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Cu(this.axes,e.length),Cu(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Cy.className="Dot";ae.registerClass(Cy);var Ny=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Wl(()=>ie(Cp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Ny.className="GaussianNoise";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Wl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Cp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ty.className="GaussianDropout";ae.registerClass(Ty);var $y=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Wl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=Ko(zl(n),this.rate);u=Sp(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};$y.className="AlphaDropout";ae.registerClass($y);function Ju(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=vE(e,t,n,s,r,a);else if(e.rank===3)i=wE(e,t,n,s,r,a);else if(e.rank===4)i=IE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function RB(e,t,n,s,r=.001){return j(()=>{let a=eb(e,s),i=a.mean,o=a.variance;return[Ju(e,i,o,n,t,r),i,o]})}function DB(e,t,n,s,r=.001){return j(()=>{let a=eb(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Ju(e,l,c,d,p,r),i,o]})}function FB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?RB(e,t,n,s,r):DB(e,t,n,s,r)}var _y=class extends Ge{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=pa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Ju(s,b,y,v,x,this.epsilon)}else return Ju(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=FB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),C=V(ge(k,y),x);b.write(ge(k,C))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};_y.className="BatchNormalization";ae.registerClass(_y);var Ay=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=eb(n,this.axis,!0),u=pa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=cs(i,d),o=cs(o,d),c=cs(c,h),p=cs(p,h),Ju(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="LayerNormalization";ae.registerClass(Ay);function OB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=bs()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],pi(e,s)})}var Ey=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>OB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ey.className="ZeroPadding2D";ae.registerClass(Ey);function Gp(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=Qb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=Jg(e,t,n,o):i=Ug(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function A0(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=w0(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=aI(e,t,n,o):i=Gk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var E0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=ms(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Bl(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ry=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,"max")}};Ry.className="MaxPooling1D";ae.registerClass(Ry);var Dy=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,"avg")}};Dy.className="AveragePooling1D";ae.registerClass(Dy);var R0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Fy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,"max")}};Fy.className="MaxPooling2D";ae.registerClass(Fy);var Oy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Gp(e,t,n,s,r,"avg")}};Oy.className="AveragePooling2D";ae.registerClass(Oy);var D0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),s=ms(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Py=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,"max")}};Py.className="MaxPooling3D";ae.registerClass(Py);var zy=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,"avg")}};zy.className="AveragePooling3D";ae.registerClass(zy);var F0=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},My=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return It(n,1)})}};My.className="GlobalAveragePooling1D";ae.registerClass(My);var Ly=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};Ly.className="GlobalMaxPooling1D";ae.registerClass(Ly);var O0=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},By=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};By.className="GlobalAveragePooling2D";ae.registerClass(By);var Vy=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};Vy.className="GlobalMaxPooling2D";ae.registerClass(Vy);var P0=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=fs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Wy=class extends P0{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),T0((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Wy.className="TimeDistributed";ae.registerClass(Wy);function PB(e){hi(VM,"BidirectionalMergeMode",e)}var zB="concat",Uy=class extends P0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=fs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=fs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?zB:e.mergeMode,PB(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):gn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new 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Dt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},$s=class{constructor(e,t,n,s,r,a,i){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=i,this.id=UI(),a!=null&&(this.originalName=MI(a),this.name=LI(this.originalName)),this.rank=t.length}},kL=0,Dp=class{constructor(e,t){this.callArgs=t,this.id=kL++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},IL=0,Ge=class extends ae.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=IL++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=Ws(n)+"_"+Rp(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new ps(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return gn(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return gn(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new U(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let i=s.shape;for(let o in r.axes){let u=Number(o),l=r.axes[o],c=u>=0?i[u]:i[i.length+u];if(l!=null&&[l,null].indexOf(c)===-1)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected axis ${u} of input shape to have value ${l} but got shape ${i}.`)}}if(r.shape!=null)for(let i=0;i<r.shape.length;++i){let o=r.shape[i],u=s.shape[i];if(o!=null&&u!=null&&o!==u)throw new U(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=dt(e),s=!0;for(let a of n)if(!(a instanceof $s)){s=!1;break}let r=!0;for(let a of n)if(a instanceof $s){r=!1;break}if(s===r)throw new U("Arguments to apply() must be all SymbolicTensors or all Tensors");return Zr(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let i of dt(e))a.push(i.shape);this.build(gn(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),i=dt(a),o=[];for(let u of i)n.indexOf(u)!==-1&&(u=u.clone()),o.push(u);if(a=gn(o),this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=SL(e),i=this.computeOutputShape(a),o,u=CL(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),i!=null&&i.length>0&&Array.isArray(i[0])?o=i.map((l,c)=>new $s(u,l,this,dt(e),t,this.name,c)):o=new $s(u,i,this,dt(e),t,this.name),this.addInboundNode(e,o,null,null,a,i,t),this._refCount++,this.activityRegularizer!=null)throw new Fe("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return o}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new Vs(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new Vs(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new ps(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Sd(this.weights)}build(e){this.built=!0}getWeights(e=!1){return bm(e?this.trainableWeights:this.weights)}setWeights(e){j(()=>{let t=this.weights;if(t.length!==e.length)throw new U(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=bm(t);for(let r=0;r<s.length;++r){let a=s[r],i=t[r],o=e[r];if(!w.arraysEqual(a.shape,o.shape))throw new U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);n.push([i,o])}Fb(n)})}addWeight(e,t,n,s,r,a,i,o){if(this._addedWeightNames.indexOf(e)!==-1)throw new U(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=o!=null?o():ht("zeros"));let u=s.apply(t,n),l=new xL(u,n,e,a,i);return u.dispose(),r!=null&&this.addLoss(()=>r.apply(l.read())),a==null&&(a=!0),a?this._trainableWeights.push(l):this._nonTrainableWeights.push(l),l}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=dt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,i=null){let o=dt(e);t=dt(t),n=dt(n),s=dt(s),r=Id(r),a=Id(a);let u=[],l=[],c=[];for(let p of o)u.push(p.sourceLayer),l.push(p.nodeIndex),c.push(p.tensorIndex);new Dp({outboundLayer:this,inboundLayers:u,nodeIndices:l,tensorIndices:c,inputTensors:o,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},i);for(let p=0;p<t.length;p++)t[p].sourceLayer=this,t[p].nodeIndex=this.inboundNodes.length-1,t[p].tensorIndex=p}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount===0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function SL(e){e=dt(e);let t=[];for(let n of e)t.push(n.shape);return gn(t)}function CL(e){return"float32"}function GI(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let i=s.inputTensors[a],o=s.inboundLayers[a],u=s.nodeIndices[a],l=GI(i,o,u);for(let c of l)r.indexOf(c)===-1&&r.push(c)}return r}}}var Yo=class extends Ge{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:Rp("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new U("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new U("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new $s(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new U(`Cannot pass any input to an InputLayer's apply() method. 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Set),r[l.name].add(o.name),!n.has(l.name)&&a.push(l)}}return{sorted:s,recipientMap:r}}function aB(e){let t;if(e.sourceLayer.inboundNodes.length===1)t=e.sourceLayer.output;else{let n=null;for(let s=0;s<e.sourceLayer.inboundNodes.length;++s)for(let r of e.sourceLayer.inboundNodes[s].outputTensors)if(r.id===e.id){n=s;break}t=e.sourceLayer.getOutputAt(n)}return t}var Is=class extends Ge{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let b=this.getClassName().toLowerCase();this.name=Rp(b)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],cr(this.inputs).length!==this.inputs.length)throw new U(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(b=>b.name)}`);cr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;this.outputLayers.push(y),this.outputLayersNodeIndices.push(v),this.outputLayersTensorIndices.push(x)}for(let b of this.inputs){let y=b.sourceLayer,v=b.nodeIndex,x=b.tensorIndex;Cs(v===0,"input layer has >1 nodes"),Cs(x===0,"input layer has >1 tensors"),this.inputLayers.push(y),this.inputLayersNodeIndices.push(v),this.inputLayersTensorIndices.push(x)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let y=this.inputLayers[b];if(!(y instanceof Yo))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${y.getClassName()}.`);this.inputNames.push(y.name),this.feedInputShapes.push(y.batchInputShape),this.feedInputNames.push(y.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},n={},s={},r={},a={},i=[],o=(b,y,v,x,k,C)=>{(x==null||k==null||C==null)&&(x=b.sourceLayer,k=b.nodeIndex,C=b.tensorIndex);let T=x.inboundNodes[k];if(v.indexOf(T)!==-1)throw new ps(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);if(y.indexOf(T)!==-1)return;this.containerNodes.add(Is.nodeKey(x,k)),x.id in a||(a[x.id]=Object.keys(a).length),v.indexOf(T)===-1&&v.push(T);let E=T.inboundLayers.length;for(let A=0;A<E;A++){let P=T.inputTensors[A],R=T.inboundLayers[A],F=T.nodeIndices[A],$=T.tensorIndices[A];o(P,y,v,R,F,$)}for(y.push(T);v.indexOf(T)>=0;)v.splice(v.indexOf(T),1);i.push(T)},u=[],l=[];for(let b of this.outputs)o(b,u,l);let c=i.slice().reverse();for(let b of c){n[b.id]=b,b.id in t||(t[b.id]=0);let y=t[b.id],v=s[b.outboundLayer.id]==null?0:s[b.outboundLayer.id];y=Math.max(y,v),s[b.outboundLayer.id]=y,r[b.outboundLayer.id]=b.outboundLayer,t[b.id]=y;for(let x=0;x<b.inboundLayers.length;x++){let k=b.inboundLayers[x],C=b.nodeIndices[x],T=k.inboundNodes[C],E=t[T.id]==null?0:t[T.id];t[T.id]=Math.max(y+1,E),n[T.id]=T}}let p={};for(let b in t){let y=t[b];y in p||(p[y]=[]),p[y].push(n[b])}let d={};for(let b in s){let y=s[b];y in d||(d[y]=[]),d[y].push(r[b])}let h=Object.keys(d).map(b=>parseInt(b,10)).sort(Wc);this.layers=[];for(let b of h){let y=d[b];y.sort((v,x)=>{let k=a[v.id],C=a[x.id];return k<C?-1:k>C?1:0});for(let v of y)v instanceof Is&&this.internalContainerRefs.push(v),this.layers.push(v)}this.layersByDepth=d,h=Object.keys(p).map(b=>parseInt(b,10)).sort(Wc);let f=this.inputs.slice(),m=[];for(let b of h)for(let y of p[b]){let v=y.outboundLayer;if(v!=null){for(let x of y.inputTensors)if(f.indexOf(x)===-1)throw new ps(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);for(let x of y.outputTensors)f.push(x);m.push(v.name)}}this.nodesByDepth=p;let g=this.layers.map(b=>b.name);for(let b of g){let y=g.filter(v=>v===b).length;if(y!==1)throw new ps(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(b=>null),outputMasks:this.outputs.map(b=>null),inputShapes:this.inputs.map(b=>b.shape),outputShapes:this.outputs.map(b=>b.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount===0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let i of a.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,s++}let r=[];for(let a in e){let i=a;if(n[a]==null){let o=a.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[a]]);else if(t)throw new U(`Provided weight data has no target variable: ${a}`);delete n[i]}if(t){let a=[];for(let i in n)a.push(i);if(a.length>0)throw new U(`${a.length} of ${s} weights are not set: ${a}`)}Fb(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${t0}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=vm(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return j(()=>{e=dt(e);let n=new Xr;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return _u(this.outputs,n,t)})}computeMask(e,t){return j(()=>{e=dt(e);let n;return t==null?n=pa(null,e.length):n=dt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Id(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Wc);if(s.length>1)for(let i of s){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(l.id)!==-1)continue;let c=[];for(let f=0;f<u.inboundLayers.length;f++){let m=u.inboundLayers[f],g=u.nodeIndices[f],b=u.tensorIndices[f],y=`${m.name}_${g}_${b}`,v=n[y];c.push(v)}let p=l.computeOutputShape(gn(c)),d=Id(p),h=l.inboundNodes.indexOf(u);for(let f=0;f<d.length;f++){let m=`${l.name}_${h}_${f}`;n[m]=d[f]}}}let r=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],c=`${o.name}_${u}_${l}`;a.push(c)}for(let i=0;i<a.length;i++){let o=a[i];Cs(o in n),r.push(n[o])}return gn(r)}runInternalGraph(e,t){t==null&&(t=pa(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],c=t[o];n[u.id]=[l,c]}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Wc);for(let o of s){let u=this.nodesByDepth[o];for(let l of u){let c=l.outboundLayer,p=l.inputTensors,d=l.outputTensors,h=new Array;for(let f of p)f.id in n&&h.push(n[f.id]);if(h.length===p.length){let f={},m,g,b,y;if(l.callArgs!=null&&(f=l.callArgs),h.length===1){let[v,x]=h[0];f.mask==null&&(f.mask=x),b=dt(c.call(v,f)),y=dt(c.computeMask(v,x)),m=[v],g=[x]}else m=h.map(v=>v[0]),g=h.map(v=>v[1]),f.mask==null&&(f.mask=g),b=dt(c.call(m,f)),y=dt(c.computeMask(m,g));if(c.activityRegularizer)throw new Fe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let v=0;v<d.length;++v){let x=d[v],k=b[v],C=y[v];n[x.id]=[k,C]}}}}let r=[],a=[],i=[];for(let o of this.outputs){Cs(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),a.push(l)}return[r,a,i]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Is?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Is.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return j(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Is.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let i=a.getClassName(),o=a.getConfig(),u=[];for(let c=0;c<a.inboundNodes.length;c++){let p=a.inboundNodes[c],d=Is.nodeKey(a,c),h={};if(this.containerNodes.has(d)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let f=[];for(let m=0;m<p.inboundLayers.length;m++){let g=p.inboundLayers[m],b=p.nodeIndices[m],y=p.tensorIndices[m],v=Is.nodeKey(g,b),x=t[v];x==null&&(x=0),f.push([g.name,x,y,h])}u.push(f)}}}let l={};l.name=a.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let i=this.inputLayers[a],o=this.inputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.inputLayersTensorIndices[a];s.push([i.name,l,c])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let i=this.outputLayers[a],o=this.outputLayersNodeIndices[a],u=Is.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let c=this.outputLayersTensorIndices[a];r.push([i.name,l,c])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function i(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function o(m,g){let b=[],y;for(let v of g){let x=v[0],k=v[1],C=v[2];if(y=v[3]==null?{}:v[3],!(x in r)){i(m,g);return}let T=r[x];if(T.inboundNodes.length<=k){i(m,g);return}let E=T.inboundNodes[k];b.push(E.outputTensors[C])}b.length>0&&m.apply(gn(b),y)}function u(m){let g=m.name,b=fs(m,t.customObjects!=null?t.customObjects:{});b.setFastWeightInitDuringBuild(s),r[g]=b,m.inboundNodes.forEach(v=>{if(!(v instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${v}`);i(b,v)})}let l=t.name,c=t.layers;for(let m of c)u(m);for(;!AM(a);)for(let m of c){let g=r[m.name];if(g.name in a){let b=a[g.name];delete a[g.name];for(let y of b)o(g,y)}}let p=[],d=[],h=t.inputLayers;for(let m of h){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;p.push(x[y])}let f=t.outputLayers;for(let m of f){let g=m[0],b=m[1],y=m[2];Cs(g in r);let x=r[g].inboundNodes[b].outputTensors;d.push(x[y])}return new e({inputs:p,outputs:d,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){j(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function iB(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function n0(e,t){return iB(e,t,"classWeight")}async function s0(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=j(()=>{if(e.shape.length===1)return lr(e);if(e.shape.length===2){if(e.shape[1]>1)return Gu(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());Re(r);let i=[];return a.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Qt(i,"float32")}else return null}function oB(e,t){return V(e,t)}var uB=32;function r0(e,t){let n,s,r=t;n=r.xs,s=r.ys,w.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${t}`);let a=Dx("input",e.inputNames,n),i=Dx("output",e.outputNames,s),o=a[0].shape[0];w.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs.  (Expected input keys: ${JSON.stringify(e.inputNames)})`),w.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs.  (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<a.length;u++)w.assert(a[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${a[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)w.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected  ${o} based on input ${e.inputNames[0]}.`);return{xs:a,ys:i}}function Dx(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return w.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function lB(e){if(e.length===3)throw new Fe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function cB(e,t,n){let s=n.batchesPerEpoch!=null;if(w.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. 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Use LayersModel.compile(modelCompileArgs).");let r=[];for(let a=0;a<this.feedOutputShapes.length;++a){let i=this.feedOutputShapes[a];this.feedLossFns[a]===Nd?r.push(i.slice(0,i.length-1).concat([1])):r.push(i)}if(e=Px(e,this.feedInputNames,this.feedInputShapes,!1,"input"),t=Px(t,this.feedOutputNames,r,!1,"target"),bB(e,t,null),yB(t,this.feedLossFns,this.feedOutputShapes),this.stateful&&s!=null&&s>0&&e[0].shape[0]%s!==0)throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. 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a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=lm().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-lm().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=Ws(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>Ws(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=Ws(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[Ws(Hc(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>Ws(Hc(e)));{let e={};for(let t in this.metrics)e[t]=Ws(Hc(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Qu(e.optimizer_config),n=fs(t),s;if(typeof e.loss=="string")s=qr(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>qr(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=qr(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>qr(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=qr(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let u=_n.getSaveHandlers(e);if(u.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(u.length>1)throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);e=u[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await _n.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,i={modelTopology:this.toJSON(r,s),format:xB,generatedBy:`TensorFlow.js tfjs-layers v${t0}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){i.trainingConfig=this.getTrainingConfig();let u="optimizer",{data:l,specs:c}=await _n.encodeWeights(await this.optimizer.getWeights(),u);n.specs.push(...c),n.data=_n.concatenateArrayBuffers([n.data,l])}return this.userDefinedMetadata!=null&&(Ax(this.userDefinedMetadata,this.name,!0),i.userDefinedMetadata=this.userDefinedMetadata),i.weightData=n.data,i.weightSpecs=n.specs,e.save(i)}setUserDefinedMetadata(e){Ax(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};pr.className="Model";ae.registerClass(pr);var i0=class extends pr{};i0.className="Functional";ae.registerClass(i0);async function wB(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Qu(n),r=fs(s,t);if(e.weightsManifest!=null){let a=await _n.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=a[o.originalName];r.loadWeights(i),Re(a)}return r}async function kB(e,t){if(t==null&&(t={}),typeof e=="string"){let n=_n.getLoadHandlers(e,t);if(n.length===0)n.push(_n.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return IB(e,void 0,t)}async function IB(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,i=s.weightData!=null&&s.weightSpecs!=null&&a,o=fs(Qu(r),t,i),u=s.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),s.userDefinedMetadata!=null&&o.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:c}=SB(s.weightData,s.weightSpecs);o.loadWeights(l,a),o.optimizer!=null&&c.length>0&&await o.optimizer.setWeights(c),Re(l),Re(c.map(p=>p.tensor))}return o}function SB(e,t){let n=_n.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Im=class extends pr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:Rp("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Im||e instanceof pr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=HI({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=GI(this.outputs[0])}this.inboundNodes=[],new Dp({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:pa(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(nt(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new pr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new ps("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else w.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let i=new e(a);if(!(i instanceof Im))throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let l=fs(o,void 0,s);s&&l.setFastWeightInitDuringBuild(!0),i.add(l)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},Vb=Im;Vb.className="Sequential";ae.registerClass(Vb);function cpe(e){return new pr(e)}function dpe(e){return new Vb(e)}function ppe(e,t){return t==null&&(t={}),kB(e,t)}function CB(e){return HI(e)}function hpe(e,t){Ob.registerCallbackConstructor(e,t)}var wn=class extends ae.Serializable{getConfig(){return{}}},o0=class extends wn{apply(e,t=1){return XM(e,t)}};o0.className="elu";ae.registerClass(o0);var u0=class extends wn{apply(e){return cI(e)}};u0.className="selu";ae.registerClass(u0);var l0=class extends wn{apply(e){return Xs(e)}};l0.className="relu";ae.registerClass(l0);var c0=class extends wn{apply(e){return j(()=>gp(6,Xs(e)))}};c0.className="relu6";ae.registerClass(c0);var d0=class extends wn{apply(e){return e}};d0.className="linear";ae.registerClass(d0);var p0=class extends wn{apply(e){return qs(e)}};p0.className="sigmoid";ae.registerClass(p0);var h0=class extends wn{apply(e){return QM(e)}};h0.className="hardSigmoid";ae.registerClass(h0);var f0=class extends wn{apply(e){return Ol(e)}};f0.className="softplus";ae.registerClass(f0);var m0=class extends wn{apply(e){return YM(e)}};m0.className="softsign";ae.registerClass(m0);var g0=class extends wn{apply(e){return Hu(e)}};g0.className="tanh";ae.registerClass(g0);var Wb=class extends wn{apply(e,t=-1){return ib(e,t)}};Wb.className="softmax";ae.registerClass(Wb);var b0=class extends wn{apply(e,t=-1){return eI(e,t)}};b0.className="logSoftmax";ae.registerClass(b0);var y0=class extends wn{apply(e,t=1){return j(()=>V(qs(V(e,t)),e))}};y0.className="swish";ae.registerClass(y0);var v0=class extends wn{apply(e){return j(()=>V(e,Hu(Ol(e))))}};v0.className="mish";ae.registerClass(v0);function br(e){return e.getClassName()}function Wf(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,"activation")}function yr(e){if(e==null){let t={};return t.className="linear",t.config={},Wf(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},Wf(t)}else return e instanceof wn?e:Wf(e)}function Ub(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var x0=class extends ae.Serializable{},Wl=class extends x0{constructor(e){super();Ub(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return j(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,ye(V(this.l1,Mt(e))))),this.hasL2&&(t=ie(t,ye(V(this.l2,Bl(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Wl.className="L1L2";ae.registerClass(Wl);function NB(e){return Ub(e),new Wl({l1:e!=null?e.l1:null,l2:0})}function TB(e){return Ub(e),new Wl({l2:e!=null?e.l2:null,l1:0})}var Mx={l1l2:"L1L2"};function at(e){return vb(e)}function Lx(e,t={}){return zl(e,ae.SerializationMap.getMap().classNameMap,t,"regularizer")}function ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in Mx?Mx[e]:e,config:{}};return Lx(n)}else return e instanceof x0?e:Lx(e)}var Gb=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Oe(e);let n=Xs(e);return this.maxValue!=null&&(n=Bn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};Gb.className="ReLU";ae.registerClass(Gb);var Hb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return jg(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Hb.className="LeakyReLU";ae.registerClass(Hb);var qb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=ht(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=ft(e.alphaRegularizer),this.alphaConstraint=Pt(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=nt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Dt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Oe(e),tb(e,this.alpha.read())}getConfig(){let e={alphaInitializer:yt(this.alphaInitializer),alphaRegularizer:at(this.alphaRegularizer),alphaConstraint:Ot(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};qb.className="PReLU";ae.registerClass(qb);var jb=class extends Ge{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Fe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Oe(e);return hp(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};jb.className="ELU";ae.registerClass(jb);var Kb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Oe(e);return V(n,ce(Wn(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};Kb.className="ThresholdedReLU";ae.registerClass(Kb);var Xb=class extends Ge{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wb().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Oe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xb.className="Softmax";ae.registerClass(Xb);function Ki(e,t,n){if(typeof e=="number")return pa(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!HM(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function ms(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-a+1,Math.floor((i+s-1)/s)}function Ns(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+gr([n-t,0]);else if(s==="same")e=e*t;else throw new U(`Unsupport padding mode: ${s}.`);return e}function Yb(e,t){return j(()=>(St(t),t==="channelsFirst"?qe(e,[0,2,3,1]):e))}function w0(e,t){return j(()=>(St(t),t==="channelsFirst"?qe(e,[0,2,3,4,1]):e))}function $B(e,t,n,s=1,r="valid",a,i=1){return j(()=>{if(a==null&&(a=bs()),St(a),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=qe(e,[0,2,1])),r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=qk(e,t,s,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=ws(o,n)),o})}function Bx(e,t,n,s=[1,1],r="valid",a,i,o=null){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Yb(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=da.conv2d({x:u,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),a==="channelsFirst"&&(u=qe(u,[0,3,1,2])),u})}function _B(e,t,n,s=[1,1,1],r="valid",a,i){return j(()=>{if(a==null&&(a=bs()),St(a),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=w0(e,a);if(r==="causal")throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=Kk(o,t,s,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=ws(o,n)),a==="channelsFirst"&&(o=qe(o,[0,4,1,2,3])),o})}var Qb=class extends Ge{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Qb.verifyArgs(t),this.rank=e,Bt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Ki(t.kernelSize,e,"kernelSize"),this.strides=Ki(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Un(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,St(this.dataFormat),this.activation=yr(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=ht(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Pt(t.biasConstraint),this.biasRegularizer=ft(t.biasRegularizer),this.activityRegularizer=ft(t.activityRegularizer),this.dilationRate=Ki(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new U(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Cs("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!xb(e.kernelSize,"number",1,3))throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:br(this.activation),useBias:this.useBias,biasInitializer:yt(this.biasInitializer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Ul=class extends Qb{constructor(e,t){super(e,t);this.kernel=null,Ul.verifyArgs(t),this.filters=t.filters,Bt(this.filters,"filters"),this.kernelInitializer=ht(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Pt(t.kernelConstraint),this.kernelRegularizer=ft(t.kernelRegularizer)}build(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n,s=this.bias==null?null:this.bias.read(),r=PI(this.activation.getClassName());if(r!=null&&this.rank===2)n=Bx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=$B(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Bx(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=_B(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Fe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=nt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=ms(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:yt(this.kernelInitializer),kernelRegularizer:at(this.kernelRegularizer),kernelConstraint:Ot(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},k0=class extends Ul{constructor(e){super(2,e);k0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!xb(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},Pp=k0;Pp.className="Conv2D";ae.registerClass(Pp);var I0=class extends Ul{constructor(e){super(3,e);I0.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},zp=I0;zp.className="Conv3D";ae.registerClass(zp);var Zb=class extends Pp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let o=s[a],u=s[i],l=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Ns(o,p,l,this.padding),f=Ns(u,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,1]));let g=jk(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=qe(g,[0,3,1,2])),this.bias!=null&&(g=ws(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[s]=Ns(t[s],o,a,this.padding),t[r]=Ns(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Zb.className="Conv2DTranspose";ae.registerClass(Zb);var Jb=class extends zp{constructor(e){super(e);if(this.inputSpec=[new Dt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=nt(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Dt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{let n=Oe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,i,o;this.dataFormat==="channelsFirst"?(o=2,a=3,i=4):(o=1,a=2,i=3);let u=s[o],l=s[a],c=s[i],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],b=Ns(u,f,p,this.padding),y=Ns(l,m,d,this.padding),v=Ns(c,g,h,this.padding),x=[r,b,y,v,this.filters];this.dataFormat!=="channelsLast"&&(n=qe(n,[0,2,3,4,1]));let k=qE(n,this.kernel.read(),x,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(k=qe(k,[0,4,1,2,3])),this.bias!==null&&(k=ws(k,this.bias.read(),this.dataFormat)),this.activation!==null&&(k=this.activation.apply(k)),k})}computeOutputShape(e){e=nt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Ns(t[s],l,i,this.padding),t[r]=Ns(t[r],c,o,this.padding),t[a]=Ns(t[a],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jb.className="Conv3DTranspose";ae.registerClass(Jb);var S0=class extends Ul{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=ft(t.depthwiseRegularizer),this.depthwiseConstraint=Pt(t.depthwiseConstraint),this.pointwiseInitializer=ht(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=ft(t.pointwiseRegularizer),this.pointwiseConstraint=Pt(t.pointwiseConstraint)}build(e){if(e=nt(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Dt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n;if(this.rank===1)throw new Fe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=qe(e,[0,2,3,1])),n=JD(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.pointwiseInitializer=yt(this.pointwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.pointwiseRegularizer=at(this.pointwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseConstraint),e.pointwiseConstraint=Ot(this.pointwiseConstraint),e}};S0.className="SeparableConv";var ey=class extends S0{constructor(e){super(2,e)}};ey.className="SeparableConv2D";ae.registerClass(ey);var C0=class extends Ul{constructor(e){super(1,e);C0.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!xb(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},ty=C0;ty.className="Conv1D";ae.registerClass(ty);var ny=class extends Ge{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return j(()=>{if(e=Oe(e),this.dataFormat==="channelsLast"){let n=Uc(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Uc(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Uc(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Uc(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ny.className="Cropping2D";ae.registerClass(ny);var sy=class extends Ge{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,WM(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return j(()=>{let n=Oe(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],i=this.interpolation==="nearest"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a]);return qe(i,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?ds.resizeNearestNeighbor(n,[r,a]):ds.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};sy.className="UpSampling2D";ae.registerClass(sy);function AB(e,t,n=[1,1],s="valid",r,a){return j(()=>{r==null&&(r=bs()),St(r);let i=Yb(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=pp(i,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}var ry=class extends Qb{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=ht(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Pt(e.depthwiseConstraint),this.depthwiseRegularizer=ft(e.depthwiseRegularizer)}build(e){if(e=nt(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{e=Oe(e);let n=AB(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=ws(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=ms(t,this.kernelSize[0],this.padding,this.strides[0]),a=ms(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=yt(this.depthwiseInitializer),e.depthwiseRegularizer=at(this.depthwiseRegularizer),e.depthwiseConstraint=Ot(this.depthwiseRegularizer),e}};ry.className="DepthwiseConv2D";ae.registerClass(ry);function N0(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function T0(e,t,n,s=!1,r,a,i=!1,o=!1){return j(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(ys(2,u));if(t=qe(t,l),a!=null)throw new Fe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ce(ce(r,"bool"),"float32"),r.rank===u-1&&(r=On(r,-1)),r=qe(r,l)),s&&(t=Yn(t,0),r!=null&&(r=Yn(r,0)));let c=[],p,d=n,h=t.shape[0],f=Fs(t),m;r!=null&&(m=Fs(r));for(let b=0;b<h;++b){let y=f[b],v=j(()=>e(y,d));if(r==null)p=v[0],d=v[1];else{let x=j(()=>{let k=m[b],C=ge(Xn(k),k),T=ie(V(v[0],k),V(d[0],C)),E=d.map((A,P)=>ie(V(v[1][P],k),V(A,C)));return{output:T,newStates:E}});p=x.output,d=x.newStates}o&&c.push(p)}let g;return o&&(g=Qn(c,1)),[p,g,d]})}var $0=class extends Ge{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Bp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Dt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return ys(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){gm(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return j(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Fe("Constants support is not implemented in RNN yet.");gm(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Dt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!w.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(i=>new Dt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>$t([n,s])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,i=[n,a];if(!w.arraysEqual(r.shape,i))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Ht(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],i=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let u of n)this.stateSpec.push(new Dt({shape:u.shape}));i=i.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof $s){let u=[e].concat(a),l=this.inputSpec.concat(i),c=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return j(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Oe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new U(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:s},u=T0((h,f)=>{let m=this.cell.call([h].concat(f),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),l=u[0],c=u[1],p=u[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:l;return this.returnState?[d].concat(p):d})}getInitialState(e){return j(()=>{let t=$t(e.shape);return t=ye(t,[1,2]),t=Ll(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?fm(t,[1,n]):t):this.cell.stateSize>1?[fm(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===$0.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=fs(s,n);return new e(Object.assign(t,{cell:r}))}},_r=$0;_r.className="RNN";ae.registerClass(_r);var Gl=class extends Ge{},Mp=class extends Gl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?r=Es(V(e,a),this.kernel.read()):r=Es(e,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),i!=null&&(n=V(n,i));let o=ie(r,Es(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};Mp.className="SimpleRNNCell";ae.registerClass(Mp);var ay=class extends _r{constructor(e){e.cell=new Mp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};ay.className="SimpleRNN";ae.registerClass(ay);var Lp=class extends Gl{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=nt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return j(()=>{if(e=e,e.length!==2)throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=V(e,r[0]));let l=Es(e,this.kernel.read());this.useBias&&(l=ws(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Ln(c,[2*this.units,this.units],c.rank-1),h=Es(s,p),[f,m,g]=Ln(l,3,l.rank-1),[b,y]=Ln(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(f,b)),o=this.recurrentActivation.apply(ie(m,y));let v=Es(V(o,s),d);u=this.activation.apply(ie(g,v));let x=ie(V(i,s),V(ie(1,kt(i)),u));return[x,x]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};Lp.className="GRUCell";ae.registerClass(Lp);var iy=class extends _r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Lp(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};iy.className="GRU";ae.registerClass(iy);var Hl=class extends Gl{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=yr(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=ht(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=ft(e.kernelRegularizer),this.recurrentRegularizer=ft(e.recurrentRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.kernelConstraint=Pt(e.kernelConstraint),this.recurrentConstraint=Pt(e.recurrentConstraint),this.biasConstraint=Pt(e.biasConstraint),this.dropout=Qi([1,gr([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Qi([1,gr([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=nt(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends es{apply(i,o){let u=r.apply([a]),l=new Cp().apply([a]),c=r.apply([a*2]);return Cx(Cx(u,l),c)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,c;0<this.dropout&&this.dropout<1&&(e=V(e,a[0]));let p=Es(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=V(s,i[0])),p=ie(p,Es(s,this.recurrentKernel.read())),this.useBias&&(p=ws(p,this.bias.read()));let[d,h,f,m]=Ln(p,4,p.rank-1);o=this.recurrentActivation.apply(d),u=this.recurrentActivation.apply(h),l=ie(V(u,r),V(o,this.activation.apply(f))),c=this.recurrentActivation.apply(m);let g=V(c,this.activation.apply(l));return[g,g,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:br(this.activation),recurrentActivation:br(this.recurrentActivation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),recurrentInitializer:yt(this.recurrentInitializer),biasInitializer:yt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:at(this.kernelRegularizer),recurrentRegularizer:at(this.recurrentRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),recurrentConstraint:Ot(this.recurrentConstraint),biasConstraint:Ot(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Hl.className="LSTMCell";ae.registerClass(Hl);var oy=class extends _r{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Hl(e);super(e)}call(e,t){return j(()=>{this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};oy.className="LSTM";ae.registerClass(oy);var Bp=class extends Gl{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return j(()=>{e=e;let n=e.slice(1),s=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?s.push(n.splice(0,i.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=s[i],i===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=o.call(a,t),r.push(a.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[a[0]].concat(n)})}build(e){gm(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Zr(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(fs(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return bm(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}Fb(t)}};Bp.className="StackedRNNCells";ae.registerClass(Bp);function vr(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,i=()=>a!=null?a(t(),n):WI(t(),n),o=()=>Vl(i,t,s);return!r||r<=1?Ht(o().clone()):Array(r).fill(void 0).map(o).map(l=>Ht(l.clone()))}var _0=class extends _r{constructor(e){if(e.unroll)throw new Fe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Fe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Dt({ndim:5})]}call(e,t){return j(()=>{if(this.cell.dropoutMask!=null&&(Re(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Re(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return j(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=$t(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){j(()=>{if(!this.stateful)throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Re(this.states_),this.keptStates!=null&&(Re(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Re(this.states_);for(let i=0;i<this.states_.length;++i){let o=e[i],u=r;if(!w.arraysEqual(o.shape,u))throw new U(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${o.shape}`);this.states_[i]=o}}this.states_=this.states_.map(i=>Ht(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],c=ms(u,s[0],r,a[0],i[0]),p=ms(l,s[1],r,a[1],i[1]);return[...e.slice(0,2),...o?[n,c,p]:[c,p,n]]}};_0.className="ConvRNN2D";var Vp=class extends Hl{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:i}=e;super({...e,units:t});this.filters=t,Bt(this.filters,"filters"),this.kernelSize=Ki(n,2,"kernelSize"),this.kernelSize.forEach(o=>Bt(o,"kernelSize")),this.strides=Ki(s||1,2,"strides"),this.strides.forEach(o=>Bt(o,"strides")),this.padding=r||"valid",Un(this.padding),this.dataFormat=a||"channelsLast",St(this.dataFormat),this.dilationRate=Ki(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Bt(o,"dilationRate"))}build(e){var t;e=nt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends es{apply(c,p){let d=u.apply([l]),h=zn([l]),f=u.apply([l*2]);return Nb([d,h,f])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return j(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=vr({ones:()=>Xn(s),rate:this.dropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let o=this.dropoutMask,u=(Z,te,ee)=>!te||!te[ee]?Z:V(te[ee],Z),l=u(s,o,0),c=u(s,o,1),p=u(s,o,2),d=u(s,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=vr({ones:()=>Xn(r),rate:this.recurrentDropout,training:n,count:i,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=u(r,h,0),m=u(r,h,1),g=u(r,h,2),b=u(r,h,3),y=3,[v,x,k,C]=Ln(this.kernel.read(),i,y),[T,E,A,P]=this.useBias?Ln(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,v,T,this.padding),c=this.inputConv(c,x,E,this.padding),p=this.inputConv(p,k,A,this.padding),d=this.inputConv(d,C,P,this.padding);let[R,F,$,z]=Ln(this.recurrentKernel.read(),i,y);f=this.recurrentConv(f,R),m=this.recurrentConv(m,F),g=this.recurrentConv(g,$),b=this.recurrentConv(b,z);let W=this.recurrentActivation.apply(ie(l,f)),q=this.recurrentActivation.apply(ie(c,m)),K=ie(V(q,a),V(W,this.activation.apply(ie(p,g)))),Y=V(this.recurrentActivation.apply(ie(d,b)),this.activation.apply(K));return[Y,Y,K]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=ua(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?ws(r,n,this.dataFormat):r}recurrentConv(e,t){return ua(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Vp.className="ConvLSTM2DCell";ae.registerClass(Vp);var uy=class extends _0{constructor(e){let t=new Vp(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};uy.className="ConvLSTM2D";ae.registerClass(uy);var Wp=class extends Ge{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Vl(()=>WI(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};Wp.className="Dropout";ae.registerClass(Wp);var ly=class extends Wp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};ly.className="SpatialDropout1D";ae.registerClass(ly);var cy=class extends Ge{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Bt(this.units,"units"),this.activation=yr(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=ht(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=ht(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Pt(e.kernelConstraint),this.biasConstraint=Pt(e.biasConstraint),this.kernelRegularizer=ft(e.kernelRegularizer),this.biasRegularizer=ft(e.biasRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=nt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=nt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=PI(this.activation.getClassName()),r;return s!=null?r=Es(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Es(n,this.kernel.read()),this.bias!=null&&(r=ws(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:br(this.activation),useBias:this.useBias,kernelInitializer:yt(this.kernelInitializer),biasInitializer:yt(this.biasInitializer),kernelRegularizer:at(this.kernelRegularizer),biasRegularizer:at(this.biasRegularizer),activityRegularizer:at(this.activityRegularizer),kernelConstraint:Ot(this.kernelConstraint),biasConstraint:Ot(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};cy.className="Dense";ae.registerClass(cy);var dy=class extends Ge{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=nt(e);for(let t of e.slice(1))if(t==null)throw new U(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],dr(e,1)]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=qe(n,s)}return KM(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};dy.className="Flatten";ae.registerClass(dy);var py=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.activation=yr(e.activation)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.activation.apply(n)})}getConfig(){let e={activation:br(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};py.className="Activation";ae.registerClass(py);var hy=class extends Ge{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return j(()=>(e=Oe(e),qM(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};hy.className="RepeatVector";ae.registerClass(hy);var fy=class extends Ge{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let o=0;o<s.length;++o){let u=s[o];if(this.isUnknown(u))if(a===null)a=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=dr(e);if(a!==null){if(r===0||i%r!==0)throw new U(n);s[a]=i/r}else if(i!==r)throw new U(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};fy.className="Reshape";ae.registerClass(fy);var my=class extends Ge{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=ys(1,e.dims.length+1);if(!w.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Dt({ndim:this.dims.length+1})]}computeOutputShape(e){e=nt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return qe(Oe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};my.className="Permute";ae.registerClass(my);var gy=class extends Ge{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Oe(e),s=-1;return cm(Ku(n,this.maskValue),s)}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e),s=-1,r=!0,a=cm(Ku(n,this.maskValue),s,r);return V(n,ce(a,n.dtype))})}};gy.className="Masking";ae.registerClass(gy);var by=class extends Ge{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(dt(e.inputLength))}this.inputDim=e.inputDim,Bt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Bt(this.outputDim,"outputDim"),this.embeddingsInitializer=ht(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=ft(e.embeddingsRegularizer),this.activityRegularizer=ft(e.activityRegularizer),this.embeddingsConstraint=Pt(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return j(()=>this.maskZero?(e=Oe(e),Ku(e,je(e))):null)}computeOutputShape(e){if(e=nt(e),this.inputLength==null)return[...e,this.outputDim];let t=dt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);n.dtype!=="int32"&&(n=Ip(n,"int32"));let s=VI(this.embeddings.read(),G(n,[n.size]));return G(s,nt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:yt(this.embeddingsInitializer),embeddingsRegularizer:at(this.embeddingsRegularizer),activityRegularizer:at(this.activityRegularizer),embeddingsConstraint:Ot(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};by.className="Embedding";ae.registerClass(by);var mi=class extends Ge{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Fe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[nt(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=cr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&cr(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return j(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=gr(s);for(let a of e){let i=a.rank;for(let o=0;o<r-i;++o)a=Ll(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,c=l[0],p=l.slice(1).concat([c]),d=G(o,[c].concat(dr(l.slice(1))));d=qe(d,[1,0]),d=G(d,p),n.push(d),r=!0}else if(u>1){let l=ys(1,u).concat([0]);n.push(qe(o,l)),r=!0}else n.push(o)}let a=this.mergeFunction(n),i=a.rank;if(r){if(i==null){let o=a.shape,u=o.length,l=o[u-1],c=[l].concat(o.slice(0,o.length-1));a=G(qe(G(a,[-1,l]),[1,0]),c)}else if(i>1){let o=[i-1].concat(ys(0,i-1));a=qe(a,o)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=cr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return j(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:On(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=Ds(n,t[s]);return n})}},yy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};yy.className="Add";ae.registerClass(yy);var vy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=V(t,e[n]);return t})}};vy.className="Multiply";ae.registerClass(vy);var xy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return V(1/e.length,t)})}};xy.className="Average";ae.registerClass(xy);var wy=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Tr(t,e[n]);return t})}};wy.className="Maximum";ae.registerClass(wy);var ky=class extends mi{constructor(e){super(e)}mergeFunction(e){return j(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=gp(t,e[n]);return t})}};ky.className="Minimum";ae.registerClass(ky);var Iy=class extends mi{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let i of n)if(w.arraysEqual(i,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return j(()=>Nb(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return j(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ce(Xn(e[a]),"bool")):t[a].rank<e[a].rank?s.push(On(t[a],-1)):s.push(t[a]);let r=Ft(s,this.axis);return Bk(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Iy.className="Concatenate";ae.registerClass(Iy);function Su(e,t){for(;e<0;)e+=t;return e}function EB(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Fe("batchDot is not implemented for tensors of 4D or higher rank yet");if(w.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),w.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Fe("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return j(()=>{let i;if(s>r){i=s-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=G(t,t.shape.concat(u))}else if(r>s){i=r-s;let u=[];for(let l=0;l<i;++l)u.push(1);e=G(e,e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?o=ye(V(e,t),a[0]):o=ye(V(qe(e,[1,0]),t),a[1]);else{let u=a[0]!==e.shape.length-1,l=a[1]===t.shape.length-1;o=We(e,t,u,l)}if(i>0){let u;s>r?u=s+r-3:u=s-1;let l=[];for(let c=u;c<u+i;++c)l.push(c);o=mr(o,l)}return o.shape.length===1&&(o=On(o,1)),o})}var Sy=class extends mi{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>Su(r,e[a].shape.length)):s=[Su(this.axes,t.shape.length),Su(this.axes,n.shape.length)],this.normalize&&(t=Cd(t,s[0]),n=Cd(n,s[1])),EB(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Su(this.axes,e.length),Su(this.axes,t.length)],n}computeOutputShape(e){w.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Fe("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};Sy.className="Dot";ae.registerClass(Sy);var Cy=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return Vl(()=>ie(Sp(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};Cy.className="GaussianNoise";ae.registerClass(Cy);var Ny=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{this.invokeCallHook(e,t);let n=Oe(e);return this.rate>0&&this.rate<1?Vl(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return V(n,Sp(n.shape,1,r))},()=>n,t.training||!1):n})}};Ny.className="GaussianDropout";ae.registerClass(Ny);var Ty=class extends Ge{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Oe(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return j(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Vl(()=>{let r=Oe(e),a=1.6732632423543772,i=1.0507009873554805,o=-a*i,u=jo(Pl(n),this.rate);u=Ip(u,"float32");let l=((1-this.rate)*(1+this.rate*o**2))**-.5,c=-l*o*this.rate,p=ie(V(r,u),V(ie(u,-1),o));return ie(V(p,l),c)},()=>Oe(e),t.training||!1)}return e})}};Ty.className="AlphaDropout";ae.registerClass(Ty);function Zu(e,t,n,s,r,a=.001){let i;if(e.rank===2)i=vE(e,t,n,s,r,a);else if(e.rank===3)i=wE(e,t,n,s,r,a);else if(e.rank===4)i=IE(e,t,n,s,r,a);else throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function RB(e,t,n,s,r=.001){return j(()=>{let a=Jg(e,s),i=a.mean,o=a.variance;return[Zu(e,i,o,n,t,r),i,o]})}function DB(e,t,n,s,r=.001){return j(()=>{let a=Jg(e,s),i=a.mean,o=a.variance,u=[];for(let f of ys(0,e.rank))s.indexOf(f)!==-1?u.push(1):u.push(e.shape[f]);let l=G(i,u),c=G(o,u),p=t==null?null:G(t,u),d=n==null?null:G(n,u);return[Zu(e,l,c,d,p,r),i,o]})}function FB(e,t,n,s,r=.001){return w.arraysEqual(s.slice().sort(),ys(0,e.rank-1))?RB(e,t,n,s,r):DB(e,t,n,s,r)}var $y=class extends Ge{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.movingMeanInitializer=ht(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=ht(e.movingVarianceInitializer||"ones"),this.betaConstraint=Pt(e.betaConstraint),this.gammaConstraint=Pt(e.gammaConstraint),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer)}build(e){e=nt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Dt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return j(()=>{let n=t.training==null?!1:t.training,s=Oe(e),r=s.shape,a=r.length,i=ys(0,a),o=this.axis>=0?this.axis:this.axis+a;i.splice(o,1);let u=pa(1,a);u[o]=r[o];let l=i.slice();l.sort();let c=!w.arraysEqual(l,ys(0,a).slice(0,a-1)),p=()=>{if(c){let b=G(this.movingMean.read(),u),y=G(this.movingVariance.read(),u),v=this.center?G(this.beta.read(),u):null,x=this.scale?G(this.gamma.read(),u):null;return Zu(s,b,y,v,x,this.epsilon)}else return Zu(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[d,h,f]=FB(s,this.gamma.read(),this.beta.read(),i,this.epsilon),m=(b,y,v)=>{j(()=>{let x=1-v,k=b.read(),C=V(ge(k,y),x);b.write(ge(k,C))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),d})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),movingMeanInitializer:yt(this.movingMeanInitializer),movingVarianceInitializer:yt(this.movingVarianceInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer),betaConstraint:Ot(this.betaConstraint),gammaConstraint:Ot(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};$y.className="BatchNormalization";ae.registerClass($y);var _y=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=ht(e.betaInitializer||"zeros"),this.gammaInitializer=ht(e.gammaInitializer||"ones"),this.betaRegularizer=ft(e.betaRegularizer),this.gammaRegularizer=ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=nt(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==cr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Oe(e),s=n.shape,r=s.length;return j(()=>{let{mean:i,variance:o}=Jg(n,this.axis,!0),u=pa(1,r);for(let f of this.axis)u[f]=s[f];let l=f=>f!=null&&f.shape.length!==r?G(f,u):f,c=l(this.gamma.read()),p=l(this.beta.read()),d=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(d.push(s[f]),h.push(1)):(d.push(1),h.push(s[f]));return i=cs(i,d),o=cs(o,d),c=cs(c,h),p=cs(p,h),Zu(n,i,o,p,c,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:yt(this.betaInitializer),gammaInitializer:yt(this.gammaInitializer),betaRegularizer:at(this.betaRegularizer),gammaRegularizer:at(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};_y.className="LayerNormalization";ae.registerClass(_y);function OB(e,t,n){return j(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=bs()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],pi(e,s)})}var Ay=class extends Ge{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?bs():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return j(()=>OB(Oe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Ay.className="ZeroPadding2D";ae.registerClass(Ay);function Up(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=Yb(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=Zg(e,t,n,o):i=Wg(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,3,1,2])),i})}function A0(e,t,n,s,r,a){return j(()=>{St(r),zI(a),Un(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=bs()),a==null&&(a="max"),e=w0(e,r);let i,o=s==="same"?"same":"valid";return a==="max"?i=aI(e,t,n,o):i=Gk(e,t,n,o),r==="channelsFirst"&&(i=qe(i,[0,4,1,2,3])),i})}var E0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Bt(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Un(this.padding),this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){e=nt(e);let t=ms(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return j(()=>{this.invokeCallHook(e,t),e=Ll(Oe(e),2);let n=this.poolingFunction(Oe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return mr(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Ey=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"max")}};Ey.className="MaxPooling1D";ae.registerClass(Ey);var Ry=class extends E0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"avg")}};Ry.className="AveragePooling1D";ae.registerClass(Ry);var R0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new U(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Dy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"max")}};Dy.className="MaxPooling2D";ae.registerClass(Dy);var Fy=class extends R0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),Up(e,t,n,s,r,"avg")}};Fy.className="AveragePooling2D";ae.registerClass(Fy);var D0=class extends Ge{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Bt(this.poolSize,"poolSize"),Bt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),Un(this.padding),this.inputSpec=[new Dt({ndim:5})]}computeOutputShape(e){e=nt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=ms(t,this.poolSize[0],this.padding,this.strides[0]),n=ms(n,this.poolSize[1],this.padding,this.strides[1]),s=ms(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return j(()=>(this.invokeCallHook(e,t),this.poolingFunction(Oe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Oy=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,"max")}};Oy.className="MaxPooling3D";ae.registerClass(Oy);var Py=class extends D0{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return St(r),Un(s),A0(e,t,n,s,r,"avg")}};Py.className="AveragePooling3D";ae.registerClass(Py);var F0=class extends Ge{constructor(e){super(e);this.inputSpec=[new Dt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Fe}},zy=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return It(n,1)})}};zy.className="GlobalAveragePooling1D";ae.registerClass(zy);var My=class extends F0{constructor(e){super(e||{})}call(e,t){return j(()=>{let n=Oe(e);return As(n,1)})}};My.className="GlobalMaxPooling1D";ae.registerClass(My);var O0=class extends Ge{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,St(this.dataFormat),this.inputSpec=[new Dt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Fe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Ly=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?It(n,[1,2]):It(n,[2,3])})}};Ly.className="GlobalAveragePooling2D";ae.registerClass(Ly);var By=class extends O0{call(e,t){return j(()=>{let n=Oe(e);return this.dataFormat==="channelsLast"?As(n,[1,2]):As(n,[2,3])})}};By.className="GlobalMaxPooling2D";ae.registerClass(By);var P0=class extends Ge{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=fs(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},Vy=class extends P0{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=nt(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=nt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return j(()=>(e=Oe(e),T0((a,i)=>[Oe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Vy.className="TimeDistributed";ae.registerClass(Vy);function PB(e){hi(VM,"BidirectionalMergeMode",e)}var zB="concat",Wy=class extends P0{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=fs(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=fs(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?zB:e.mergeMode,PB(this.mergeMode),e.weights)throw new Fe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):gn(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=N0(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let l=n.map(c=>new 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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 w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Xx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Yx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>$n(d)[0]),c=[];s!=null&&(c=s.map(d=>$n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((cS(d)||g4(d)||b4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function p4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>$n(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var h4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],f4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],m4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function cS(e){return h4.indexOf(e.op)>=0}function g4(e){return f4.indexOf(e.op)>=0}function b4(e){return m4.indexOf(e.op)>=0}var Pm=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Pm(e.functions[n],this)})}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){let t=Object.keys(e).map(n=>e[n].map(s=>s.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=>{let 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),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Yx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. 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new Error("modelUrl in loadGraphModel() cannot be null. 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-`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Kc='"',Tu=Symbol("out"),Zx=Symbol("field"),Xc=Symbol("quote"),Hf=Symbol("quoteafterquote"),Jx=Symbol("quoteinquote"),kS=class extends Jo{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new wS(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(w.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&w.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(w.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?s[a]=u:n[a]=u}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=Tu;for(let i=0;i<r;i++)switch(a){case Tu:switch(e.charAt(i)){case Kc:s=i+1,a=Xc;break;case this.delimiter:if(s=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=Tu;break;default:a=Zx,s=i;break}break;case Zx:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i)),a=Tu,s=i+1;break;default:}break;case Xc:switch(e.charAt(i)){case Kc:a=Hf;break;default:}break;case Hf:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(s,i-1)),a=Tu,s=i+1;break;case Kc:a=Xc;break;default:a=Jx;break}break;case Jx:switch(e.charAt(i)){case Kc:a=Xc;break;default:}break;default:}if(a===Hf?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},IS=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(X().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new IS(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),hs(n,t)}},SS=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Qt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=Ki([a,r,o,i],[1,4])}else this.cropBox=Ki([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(X().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new SS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=xk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=On(ce(e,"float32"),0),n;n=ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},CS=class{},NS=class extends Ut{split(e){return new X4(this,e)}},X4=class extends NS{constructor(e,t){super();this.upstream=e,this.impl=new Y4(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y4=class extends Yy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Q4=class extends Ut{decodeUTF8(){return new Z4(this)}},Z4=class extends NS{constructor(e){super();this.upstream=e,this.impl=new J4(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},J4=class extends Yy{constructor(e){super();if(this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Pw();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},TS=class extends Q4{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=i=>{let o=r.result;if(o instanceof ArrayBuffer&&(o=new Uint8Array(o)),!(o instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(o)},r.onabort=i=>n(new Error("Aborted")),r.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function eU(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=tU(e));let a=await(n||w.fetch)(s,r);if(a.ok){let i=new Uint8Array(await a.arrayBuffer());return new TS(i,t)}else throw new Error(a.statusText)}var tU=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function $S(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var _S=class extends CS{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if($S(this.input)&&X().get("IS_NODE")){let e=eg();this.input=e.readFileSync(this.input.substr(7))}return new TS(this.input,this.options)}},AS=class extends CS{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return $S(this.url)?new _S(this.url,this.fileOptions).iterator():eU(this.url,this.fileOptions)}};function nU(e,t={}){return new kS(new AS(e),t)}function sU(e){let t=Xy(e);return Tn(async()=>t)}function rU(e){return Tn(async()=>{let t=await e();return Xy(()=>t.next())})}async function aU(e,t){return SS.create(e,t)}async function iU(e){return IS.create(e)}var oU="0.0.0";function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var uU=xs.whereImpl,ES=class extends nl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Ud(this,Ss())}nextDataId(){return ES.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&N.warn(`
+        ${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),i=Fm(a,n),o=s===0?0:e.size/s,u=j(()=>{let c=[];e=G(e,[1,s,o]);for(let p=0;p<t.length;++p){let d=p===0?0:r[p-1],h=[0,d,0],f=[1,t[p],o];c[p]=G(He(e,h,f),i)}return e.dispose(),c}),l=new ql([],n,e.dtype,t.length);for(let c=0;c<u.length;c++)l.setItem(c,u[c]);return l}var KW=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),a=I("cond",e,t,n),i=I("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=I("body",e,t,n),r=I("cond",e,t,n),a=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),o=a.map(c=>c.id),u=await i[0].data();i.forEach(c=>{!c.kept&&o.indexOf(c.id)===-1&&c.dispose()});let l=a;for(;u[0];){let c=l;l=await n.functionMap[s].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let d=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await d[0].data(),d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return l}case"LoopCond":{let s=I("pred",e,t,n);return[Us(s)]}case"Switch":{let s=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Us(r)),(await s.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let s=e.inputNames.find(r=>on(r,t,n)!==void 0);if(s){let r=on(s,t,n);return[Us(r)]}return}case"Enter":{let s=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(s),[Us(r)]}case"Exit":{let s=I("tensor",e,t,n);return n.exitFrame(),[Us(s)]}case"NextIteration":{let s=I("tensor",e,t,n);return n.nextIteration(),[Us(s)]}case"TensorArrayV3":{let s=I("size",e,t,n),r=I("dtype",e,t,n),a=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),u=I("identicalElementShapes",e,t,n),l=I("name",e,t,n),c=new UW(l,r,s,a,u,i,o);return n.addTensorArray(c),[c.idTensor,Ie(1)]}case"TensorArrayWriteV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),i=n.getTensorArray(s.id);return i.write(r,a),[i.idTensor]}case"TensorArrayReadV3":{let s=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(s.id).read(r)]}case"TensorArrayGatherV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("dtype",e,t,n);return[n.getTensorArray(s.id).gather(r,a)]}case"TensorArrayScatterV3":{let s=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),a=I("tensor",e,t,n),i=n.getTensorArray(s.id);return i.scatter(r,a),[i.idTensor]}case"TensorArrayConcatV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id),a=I("dtype",e,t,n);return[r.concat(a)]}case"TensorArraySplitV3":{let s=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),a=I("lengths",e,t,n),i=n.getTensorArray(s.id);return i.split(a,r),[i.idTensor]}case"TensorArraySizeV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return[Ie(r.size(),"int32")]}case"TensorArrayCloseV3":{let s=I("tensorArrayId",e,t,n),r=n.getTensorArray(s.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("tensor",e,t,n),i=n.getTensorList(s.id);return i.setItem(r,a),[i.idTensor]}case"TensorListGetItem":{let s=I("tensorListId",e,t,n),r=I("index",e,t,n),a=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(s.id).getItem(r,a,i)]}case"TensorListScatterV2":case"TensorListScatter":{let s=I("indices",e,t,n),r=I("tensor",e,t,n),a=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=qW(r,s,a,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let s=I("elementShape",e,t,n),r=I("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let i=I(a,e,t,n),o=HW(s,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let s=I("tensorListId",e,t,n),r=I("indices",e,t,n),a=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,i,a)]}case"TensorListStack":{let s=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,i)]}case"TensorListFromTensor":{let s=I("tensor",e,t,n),r=I("elementShape",e,t,n),a=I("elementDType",e,t,n),i=GW(s,r,a);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let s=I("tensorListId",e,t,n),r=n.getTensorList(s.id),a=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(a,i)]}case"TensorListPushBack":{let s=I("tensorListId",e,t,n),r=I("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let 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g=I("leakyreluAlpha",e,t,n);return{stride:c,pad:p,dataFormat:d,dilations:h,biasArg:f,preluArg:m,activationFunc:r,leakyreluAlpha:g}}var XW=(e,t,n)=>{switch(e.op){case"Conv1D":{let s=I("stride",e,t,n),r=I("pad",e,t,n),a=I("dataFormat",e,t,n).toUpperCase(),i=I("dilation",e,t,n);return[qk(I("x",e,t,n),I("filter",e,t,n),s,r,a,i)]}case"Conv2D":{let 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implemented`)}},u4=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:s,outputValues:r,emptyRowIndicator:a,reverseIndexMap:i}=Vc.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[s,r,a,i]}case"SparseReshape":{let{outputIndices:s,outputShape:r}=Vc.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[s,r]}case"SparseSegmentMean":return[Vc.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[Vc.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},l4=(e,t,n)=>{switch(e.op){case"FFT":return[ob(I("x",e,t,n))];case"IFFT":return[kd(I("x",e,t,n))];case"RFFT":return[ub(I("x",e,t,n))];case"IRFFT":return[fI(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},c4=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Pf.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Pf.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Pf.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},d4=(e,t,n)=>{switch(e.op){case"Cast":return[ce(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let s=I("axis",e,t,n);return[On(I("x",e,t,n),s)]}case"Squeeze":{let s=I("axis",e,t,n);return[mr(I("x",e,t,n),s)]}case"Reshape":return[G(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[lD(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[pi(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let s=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[eb(I("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=I("blockShape",e,t,n),r=I("crops",e,t,n);return[Ug(I("x",e,t,n),s,r)]}case"DepthToSpace":{let s=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[JE(I("x",e,t,n),s,r)]}case"BroadcastTo":return[td(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[NE(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function jx(e,t,n,s){let r=((a,i,o)=>{switch(a.category){case"arithmetic":return j(()=>VW(a,i,o));case"basic_math":return j(()=>WW(a,i,o));case"control":return KW(a,i,o);case"convolution":return j(()=>XW(a,i,o));case"creation":return j(()=>YW(a,i,o));case"dynamic":return QW(a,i,o);case"evaluation":return j(()=>ZW(a,i,o));case"image":return j(()=>n4(a,i,o));case"graph":return j(()=>JW(a,i,o));case"logical":return j(()=>s4(a,i,o));case"matrices":return j(()=>r4(a,i,o));case"normalization":return j(()=>a4(a,i,o));case"reduction":return j(()=>i4(a,i,o));case"slice_join":return j(()=>o4(a,i,o));case"sparse":return j(()=>u4(a,i,o));case"spectral":return j(()=>l4(a,i,o));case"string":return j(()=>c4(a,i,o));case"transformation":return j(()=>d4(a,i,o));case"hash_table":return t4(a,i,o,s);case"custom":let u=W0(a.op);if(u&&u.customExecutor)return u.customExecutor(new BW(a,i,o));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.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 w.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var Kx=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,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(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.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)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let 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))}else throw new Error("Cannot increase frame iteration, the context is empty")}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(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function Xx(e,t,n,s){let r=new Set,a=[],i=null,o=null,u=new Set,l=Object.keys(e).map(d=>$n(d)[0]),c=[];s!=null&&(c=s.map(d=>$n(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((cS(d)||g4(d)||b4(d))&&i==null&&(i=d,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&l.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:i,syncInputs:o}}function p4(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],i=Object.keys(r).map(c=>$n(c)[0]).map(c=>e.nodes[c]),o=e.initNodes;i.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),o!=null&&o.forEach(c=>{s.has(c.name)&&a.push(c)});let u=new Set,l=[];for(;a.length>0;){let c=a.pop();u.add(c.name),t[c.name]||l.push(c),c.children.forEach(p=>{!u.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>u.has(d.name))&&a.push(p)})}return l}var h4=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],f4=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],m4=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function cS(e){return h4.indexOf(e.op)>=0}function g4(e){return f4.indexOf(e.op)>=0}function b4(e){return m4.indexOf(e.op)>=0}var Om=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new Om(e.functions[n],this)})}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){let t=Object.keys(e).map(n=>e[n].map(s=>s.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=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=Xx(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${s}]`)}return p4(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[$n(c)[0]]),r=t.map(c=>$n(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let i=this.getCompilationKey(s,a),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,a),this.compiledMap.set(i,o));let u={},l={};return j(()=>{let c=new Kx(this.weightMap,u,l,this.functionExecutorMap),p={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=$n(f),b=[];b[g]=e[f],p[m]=b});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<o.length;f++){let m=o[f];if(!p[m.name]){let g=jx(m,p,c,this._resourceManager);if(w.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>on(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,i){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let u=vW(o.name,n,s);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let c=i[l.id];if(c===1){if(!this.keepTensorForDebug)l.dispose();else{let[p,d]=Ts(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=l:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=l)}delete i[l.id]}else c!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=X().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(l){console.warn(l.message)}this.resetIntermediateTensors();let a=new Kx(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let i=t.map(l=>on(l,this.tensorsMap,a)),o=i.map(l=>l.id),u=Object.keys(e).map(l=>e[l].id);return this.keepIds=new Set([...o,...u,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),i}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,i)=>(r[this.inputs[i].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(y=>this.graph.nodes[$n(y)[0]]),i=n.map(y=>$n(y)[0]),o=i.map(y=>this.graph.nodes[y]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:c,syncInputs:p}=Xx(e,o,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(y=>({node:y,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(y=>{let[v,x]=$n(y),k=[];k[x]=e[y],h[v]=k});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let y=this.processStack(a,d,t,h,g,m,i,f,u);await Promise.all(y)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=o.filter(y=>!cS(y)&&!on(y.name,h,t)).map(y=>y.name);if(b.length>0){let y="";throw c!=null&&(y=`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 [${r}]. Consider providing the following inputs: [${l}]. ${y}`)}return h}processStack(e,t,n,s,r,a,i,o,u){let l=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([p]=Ts(c.node.name,n)),s[c.node.name]==null){let d=jx(c.node,s,n,this._resourceManager);p||([p]=Ts(c.node.name,n));let h=n.currentContext;w.isPromise(d)?l.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,i,o),this.processChildNodes(c.node,t,n,s,r,u))}else this.processChildNodes(c.node,t,n,s,r,u)}return l}processChildNodes(e,t,n,s,r,a){e.children.forEach(i=>{let[o]=Ts(i.name,n);r[o]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!on(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!on(u,s,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=$n(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,i=a.length===n.shape.length&&n.shape.every((o,u)=>a[u]===-1||a[u]===o);w.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&w.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=$n(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=$n(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},y4=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},v4="?tfjs-format=file",x4="model.json",w4=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new y4}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=_n.browserHTTPRequest(e,this.loadOptions);else{let t=_n.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(_n.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=_n.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Om(Ux.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Ux.Instance.transformGraph(e.modelInitializer);this.initializer=new Om(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=_n.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return 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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},IS=class extends Ut{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(X().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new IS(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(w.sizeFromShape(t));return n.set(e,n.length-e.length),hs(n,t)}},SS=class extends Ut{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Qt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,i=r+n,o=s+a;this.cropBox=ji([a,r,o,i],[1,4])}else this.cropBox=ji([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(X().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new SS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&w.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=xk.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return j(()=>{let t=On(ce(e,"float32"),0),n;n=ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},CS=class{},NS=class extends Ut{split(e){return new X4(this,e)}},X4=class extends NS{constructor(e,t){super();this.upstream=e,this.impl=new Y4(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},Y4=class extends Xy{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},Q4=class extends Ut{decodeUTF8(){return new Z4(this)}},Z4=class extends NS{constructor(e){super();this.upstream=e,this.impl=new J4(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},J4=class extends Xy{constructor(e){super();if(this.upstream=e,X().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=Pw();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return X().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},TS=class extends Q4{constructor(e,t={}){super();this.file=e,this.options=t,w.assert(e instanceof Uint8Array||(X().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=i=>{let o=r.result;if(o instanceof ArrayBuffer&&(o=new Uint8Array(o)),!(o instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(o)},r.onabort=i=>n(new Error("Aborted")),r.onerror=i=>n(new Error(i.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function eU(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=tU(e));let a=await(n||w.fetch)(s,r);if(a.ok){let i=new Uint8Array(await a.arrayBuffer());return new TS(i,t)}else throw new Error(a.statusText)}var tU=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function $S(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var _S=class extends CS{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if($S(this.input)&&X().get("IS_NODE")){let e=Jm();this.input=e.readFileSync(this.input.substr(7))}return new TS(this.input,this.options)}},AS=class extends CS{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return $S(this.url)?new _S(this.url,this.fileOptions).iterator():eU(this.url,this.fileOptions)}};function nU(e,t={}){return new kS(new AS(e),t)}function sU(e){let t=Ky(e);return Tn(async()=>t)}function rU(e){return Tn(async()=>{let t=await e();return Ky(()=>t.next())})}async function aU(e,t){return SS.create(e,t)}async function iU(e){return IS.create(e)}var oU="0.0.0";function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&w.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var uU=xs.whereImpl,ES=class extends tl{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Wd(this,Ss())}nextDataId(){return ES.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,X().get("IS_NODE")&&N.warn(`
 ============================
 Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=Vn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],N.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[c,p]=N.computeOutAndReduceShapes(u.shape,i),d=w.sizeFromShape(c),h=w.makeZerosTypedArray(d,"int32"),f=w.sizeFromShape(p),m=n.data.get(u.dataId).values;for(let g=0;g<h.length;++g){let b=g*f,y=m[b],v=0;for(let x=0;x<f;++x){let k=m[b+x];k<y&&(y=k,v=x)}h[g]=v}return l.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(c,"int32",h)}var EG={kernelName:ol,backendName:"cpu",kernelFunc:AG},RG=st(ul,e=>Math.asin(e)),DG={kernelName:ul,backendName:"cpu",kernelFunc:RG},FG=st(ll,e=>Math.asinh(e)),OG={kernelName:ll,backendName:"cpu",kernelFunc:FG},PG=st(cl,e=>Math.atan(e)),zG={kernelName:cl,backendName:"cpu",kernelFunc:PG},MG=At((e,t)=>Math.atan2(e,t)),LG=Gt(pl,MG),BG={kernelName:pl,backendName:"cpu",kernelFunc:LG},VG=st(dl,e=>Math.atanh(e)),WG={kernelName:dl,backendName:"cpu",kernelFunc:VG};function ov(e,t,n,s,r,a){let i=r.strideHeight,o=r.strideWidth,u=r.dilationHeight,l=r.dilationWidth,c=r.effectiveFilterHeight,p=r.effectiveFilterWidth,d=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=De(r.outShape,n),g=m.values,b=r.outShape[1]*r.outShape[2]*r.outShape[3],y=r.outShape[2]*r.outShape[3],v=r.outShape[3];for(let x=0;x<r.batchSize;++x){let k=x*b,C=x*s[0];for(let T=0;T<r.inChannels;++T)for(let E=0;E<r.outHeight;++E){let 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oe=ne*u-b,re=oe;for(;re<0;)re+=p;let le=Math.min(r.inWidth,f+oe),me=se+ne*E,we=y,Se=0,Ee=0;for(let Xe=W;Xe<q;Xe+=l){let Je=R+Xe*s[1];for(let Ye=te;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=re;Ce<le;Ce+=p){let ut=tt+Ce*s[3],rt=e[ut+F];if(a==="max"&&rt>we?we=rt:a==="avg"&&(Se+=rt,Ee++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Pe=me+F;x[Pe]=a==="avg"?Se/Ee:we}}}}return v}function UG(e,t){let n=De(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let C=k*r-h,T=C;for(;T<0;)T+=o;let E=Math.min(t.inHeight,c+C);for(let A=0;A<t.outWidth;++A){let P=A*a-f,R=P;for(;R<0;)R+=u;let 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c=N.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,C=c.effectiveFilterWidth,T=x-1-c.padInfo.front,E=C-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,"float32"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-T,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<C;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var XG={kernelName:ig,backendName:"cpu",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;ve([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,"float32"),C=1/(h*f),T=n.data.get(r.dataId).values,E=De(r.shape,"float32",T);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*C,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var QG={kernelName:ag,backendName:"cpu",kernelFunc:YG};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var JG={kernelName:Pa,backendName:"cpu",kernelFunc:ZG};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;ve([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Vn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=fa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var tH={kernelName:oo,backendName:"cpu",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=Jy(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var sH={kernelName:og,backendName:"cpu",kernelFunc:nH};function rH(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var aH={kernelName:ug,backendName:"cpu",kernelFunc:rH},iH=st(Ir,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),oH={kernelName:Ir,backendName:"cpu",kernelFunc:iH},uH=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(w.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(a.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let c=o[l],p=u[l];s[l]=Math.hypot(c,p)}return n.makeOutput(s,t.shape,"float32")},lH={kernelName:Kd,backendName:"cpu",kernelFunc:uH};function to(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var cH={kernelName:Zd,backendName:"cpu",kernelFunc:to};function no(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(m=>m.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(m=>w.sizeFromShape(m.shape)>0);if(o.length===1)return Os({inputs:{x:o[0]},backend:n});let u=o.map(m=>m.shape);if(N.assertParamsConsistent(u,a),o[0].dtype==="complex64"){let m=o.map(x=>ha({inputs:{input:x},backend:n})),g=o.map(x=>to({inputs:{input:x},backend:n})),b=no({inputs:m,backend:n,attrs:{axis:a}}),y=no({inputs:g,backend:n,attrs:{axis:a}}),v=An({inputs:{real:b,imag:y},backend:n});return 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hH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],"conv2dBackpropFilter");let p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=new Vt(r.shape,r.dtype,k),E=new Vt(a.shape,a.dtype,C);for(let A=0;A<m;++A){let P=Math.max(0,Math.ceil((x-A)/h)),R=Math.min(d.outHeight,(d.inHeight+x-A)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(d.outWidth,(d.inWidth+v-F)/f);for(let W=0;W<d.inChannels;++W)for(let q=0;q<d.outChannels;++q){let K=0;for(let Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=T.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=T.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var fH={kernelName:lg,backendName:"cpu",kernelFunc:hH};function mH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;ve([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=N.convertConv2DDataFormat(l),f=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:C,filterHeight:T,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=T-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let we=0;we<C;++we)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let Pe=Ee-K,Xe=Math.max(0,Math.ceil(Pe/W)),Je=Math.min($,(T+Pe)/W);for(let Ye=0;Ye<R;++Ye){let tt=Ye-Y,Ce=Math.max(0,Math.ceil(tt/q)),ut=Math.min(z,(E+tt)/q),rt=0;for(let Nt=Xe;Nt<Je;++Nt){let In=Nt*W-Pe;for(let Et=Ce;Et<ut;++Et){let Jt=Et*q-tt,Sn=oe*we+re*Nt+le*Et,Cn=v*(T-1-In)+x*(E-1-Jt)+k*Se;for(let Xt=0;Xt<F;++Xt){let Rn=b[Sn+me*Xt],en=y[Cn+Xt];rt+=Rn*en}}}let Zt=te*we+ee*Ee+se*Ye+ne*Se;g[Zt]=rt}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var gH={kernelName:Ta,backendName:"cpu",kernelFunc:mH};function bH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s;ve([r,a],"conv3d");let l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),{filterDepth:c,filterHeight:p,filterWidth:d,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,b=g.front,y=g.left,v=g.top,x=new Vt(l.outShape,r.dtype),k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=x.values,E=w.computeStrides(r.shape),A=w.computeStrides(a.shape);for(let P=0;P<l.batchSize;++P){let R=P*E[0],F=P*x.strides[0];for(let $=0;$<l.outDepth;++$){let z=F+$*x.strides[1],W=$*l.strideDepth-b;for(let q=0;q<c;++q){let K=W+q*h;if(K<0||K>=l.inDepth)continue;let Y=q*A[0],Z=R+K*E[1];for(let te=0;te<l.outHeight;++te){let ee=z+te*x.strides[2],se=te*l.strideHeight-v;for(let ne=0;ne<p;++ne){let oe=se+ne*f;if(oe<0||oe>=l.inHeight)continue;let re=Y+ne*A[1],le=Z+oe*E[2];for(let me=0;me<l.outWidth;++me){let we=ee+me*l.outChannels,Se=me*l.strideWidth-y;for(let Ee=0;Ee<d;++Ee){let Pe=Se+Ee*m;if(Pe<0||Pe>=l.inWidth)continue;let Xe=re+Ee*A[2],Je=le+Pe*l.inChannels,Ye=Xe;for(let tt=0;tt<l.inChannels;++tt){let Ce=k[Je+tt];for(let ut=0;ut<l.outChannels;++ut)T[we+ut]+=Ce*C[Ye+ut];Ye+=l.outChannels}}}}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var yH={kernelName:Xd,backendName:"cpu",kernelFunc:bH};function vH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s;ve([r,a],"conv3dBackpropFilterV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,C,T]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),we=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*C+we;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*T+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let rt=ut*z,Zt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+rt,Jt=Nt*P+Zt;for(let Sn=le;Sn<me;++Sn){let 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TH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=De([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),C=w.computeStrides(b.shape);for(let T=0;T<f;T++){let E=T*4,A=y[E],P=y[E+1],R=y[E+2],F=y[E+3],$=v[T];if($>=c)continue;let z=m>1?(R-A)*(p-1)/(m-1):0,W=g>1?(F-P)*(d-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?A*(p-1)+q*z:.5*(A+R)*(p-1);if(K<0||K>p-1){for(let Y=0;Y<g;Y++)for(let Z=0;Z<h;Z++){let te=Z+Y*C[2]+q*C[1]+T*C[0];b.values[te]=l}continue}if(u==="bilinear"){let Y=Math.floor(K),Z=Math.ceil(K),te=K-Y;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+F)*(d-1);if(se<0||se>d-1){for(let le=0;le<h;le++){let me=le+ee*C[2]+q*C[1]+T*C[0];b.values[me]=l}continue}let ne=Math.floor(se),oe=Math.ceil(se),re=se-ne;for(let le=0;le<h;le++){let me=le+ne*k[2]+Y*k[1]+$*k[0],we=x[me];me=le+oe*k[2]+Y*k[1]+$*k[0];let Se=x[me];me=le+ne*k[2]+Z*k[1]+$*k[0];let Ee=x[me];me=le+oe*k[2]+Z*k[1]+$*k[0];let Pe=x[me],Xe=we+(Se-we)*re,Je=Ee+(Pe-Ee)*re;me=le+ee*C[2]+q*C[1]+T*C[0],b.values[me]=Xe+(Je-Xe)*te}}}else for(let Y=0;Y<g;++Y){let Z=g>1?P*(d-1)+Y*W:.5*(P+F)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let ne=se+Y*C[2]+q*C[1]+T*C[0];b.values[ne]=l}continue}let te=Math.round(Z),ee=Math.round(K);for(let se=0;se<h;se++){let ne=se+te*k[2]+ee*k[1]+$*k[0],oe=se+Y*C[2]+q*C[1]+T*C[0];b.values[oe]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var $H={kernelName:co,backendName:"cpu",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;ve(r,"cumsum");let u=N.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=N.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most 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new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var RH={kernelName:pg,backendName:"cpu",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],"depthwiseConv2dNativeBackpropFilter");let p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),C=n.data.get(a.dataId).values,T=new Vt(a.shape,a.dtype,C);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*T.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var zH={kernelName:hg,backendName:"cpu",kernelFunc:PH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;ve([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,C]=p,T=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let we=me-se,Se=Math.max(0,Math.ceil(we/te)),Ee=Math.min(Y,(F+we)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-we;for(let rt=Je;rt<Ye;++rt){let 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u=o?r:_C({inputs:{logits:r},backend:n,attrs:{dim:-1}}),l=u.shape[0],c=u.shape[1],p=n.data.get(u.dataId).values,d=[l,a],h=w.makeZerosTypedArray(w.sizeFromShape(d),"int32");for(let f=0;f<l;++f){let m=f*c,g=new Float32Array(c-1);g[0]=p[m];for(let v=1;v<g.length;++v)g[v]=g[v-1]+p[m+v];let b=mq.alea(i.toString()),y=f*a;for(let v=0;v<a;++v){let x=b();h[y+v]=g.length;for(let k=0;k<g.length;k++)if(x<g[k]){h[y+v]=k;break}}}return o||n.disposeIntermediateTensorInfo(u),n.makeTensorInfo(d,"int32",h)}var yq={kernelName:Sg,backendName:"cpu",kernelFunc:bq},vq=xs.nonMaxSuppressionV3Impl;function xq(e){let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s;ve(r,"NonMaxSuppression");let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,{selectedIndices:p}=vq(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var wq={kernelName:Co,backendName:"cpu",kernelFunc:xq},kq=xs.nonMaxSuppressionV4Impl;function 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oe=ne*u-b,re=oe;for(;re<0;)re+=p;let le=Math.min(r.inWidth,f+oe),me=se+ne*E,we=y,Se=0,Ee=0;for(let Xe=W;Xe<q;Xe+=l){let Je=R+Xe*s[1];for(let Ye=te;Ye<ee;Ye+=c){let tt=Je+Ye*s[2];for(let Ce=re;Ce<le;Ce+=p){let ut=tt+Ce*s[3],rt=e[ut+F];if(a==="max"&&rt>we?we=rt:a==="avg"&&(Se+=rt,Ee++),isNaN(we))break}if(isNaN(we))break}if(isNaN(we))break}let Pe=me+F;x[Pe]=a==="avg"?Se/Ee:we}}}}return v}function UG(e,t){let n=De(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,i=t.dilationDepth,o=t.dilationHeight,u=t.dilationWidth,l=t.effectiveFilterDepth,c=t.effectiveFilterHeight,p=t.effectiveFilterWidth,d=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let b=0;b<t.outDepth;++b){let y=b*s-d,v=y;for(;v<0;)v+=i;let x=Math.min(t.inDepth,l+y);for(let k=0;k<t.outHeight;++k){let C=k*r-h,T=C;for(;T<0;)T+=o;let E=Math.min(t.inHeight,c+C);for(let A=0;A<t.outWidth;++A){let P=A*a-f,R=P;for(;R<0;)R+=u;let 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c=N.computePool3DInfo(a.shape,i,o,1,u,l),p=c.strideDepth,d=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,b=c.dilationDepth,y=c.dilationHeight,v=c.dilationWidth,x=c.effectiveFilterDepth,k=c.effectiveFilterHeight,C=c.effectiveFilterWidth,T=x-1-c.padInfo.front,E=C-1-c.padInfo.left,A=k-1-c.padInfo.top,P=De(a.shape,"float32"),R=1/(f*m*g),F=n.bufferSync(r);for(let $=0;$<c.batchSize;++$)for(let z=0;z<c.inChannels;++z)for(let W=0;W<c.inDepth;++W)for(let q=0;q<c.inHeight;++q)for(let K=0;K<c.inWidth;++K){let Y=W-T,Z=q-A,te=K-E,ee=0;for(let se=0;se<x;se+=b){let ne=(Y+se)/p;if(!(ne<0||ne>=c.outDepth||Math.floor(ne)!==ne))for(let oe=0;oe<k;oe+=y){let re=(Z+oe)/d;if(!(re<0||re>=c.outHeight||Math.floor(re)!==re))for(let le=0;le<C;le+=v){let me=(te+le)/h;if(me<0||me>=c.outWidth||Math.floor(me)!==me)continue;ee+=F.get($,ne,re,me,z)}}}P.set(ee*R,$,W,q,K,z)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var XG={kernelName:ag,backendName:"cpu",kernelFunc:KG};function YG(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;ve([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=c.strideHeight,d=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,b=c.effectiveFilterHeight,y=c.effectiveFilterWidth,v=y-1-c.padInfo.left,x=b-1-c.padInfo.top,k=De(i.shape,"float32"),C=1/(h*f),T=n.data.get(r.dataId).values,E=De(r.shape,"float32",T);for(let A=0;A<c.batchSize;++A)for(let P=0;P<c.inChannels;++P)for(let R=0;R<c.inHeight;++R)for(let F=0;F<c.inWidth;++F){let $=R-x,z=F-v,W=0;for(let q=0;q<b;q+=m){let K=($+q)/p;if(!(K<0||K>=c.outHeight||Math.floor(K)!==K))for(let Y=0;Y<y;Y+=g){let Z=(z+Y)/d;if(Z<0||Z>=c.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(A,K,Z,P)}}k.set(W*C,A,R,F,P)}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var QG={kernelName:rg,backendName:"cpu",kernelFunc:YG};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var JG={kernelName:Pa,backendName:"cpu",kernelFunc:ZG};function eH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;ve([r],"batchToSpaceND");let o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=mt({inputs:{x:r},backend:n,attrs:{shape:u}}),f=Vn({inputs:{x:h},backend:n,attrs:{perm:l}}),m=mt({inputs:{x:f},backend:n,attrs:{shape:c}}),g=fa({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var tH={kernelName:io,backendName:"cpu",kernelFunc:eH};function nH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.data.get(r.dataId).values,u=n.data.get(a.dataId).values,l=Zy(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var 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hH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],"conv2dBackpropFilter");let p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=d,b=d.dataFormat==="channelsLast",y=new Vt(d.filterShape,"float32"),v=d.padInfo.left,x=d.padInfo.top,k=n.data.get(r.dataId).values,C=n.data.get(a.dataId).values,T=new Vt(r.shape,r.dtype,k),E=new Vt(a.shape,a.dtype,C);for(let A=0;A<m;++A){let P=Math.max(0,Math.ceil((x-A)/h)),R=Math.min(d.outHeight,(d.inHeight+x-A)/h);for(let F=0;F<g;++F){let $=Math.max(0,Math.ceil((v-F)/f)),z=Math.min(d.outWidth,(d.inWidth+v-F)/f);for(let W=0;W<d.inChannels;++W)for(let q=0;q<d.outChannels;++q){let K=0;for(let Y=0;Y<d.batchSize;++Y)for(let Z=P;Z<R;++Z){let te=A+Z*h-x;for(let ee=$;ee<z;++ee){let se=F+ee*f-v;b?K+=T.get(Y,te,se,W)*E.get(Y,Z,ee,q):K+=T.get(Y,W,te,se)*E.get(Y,q,Z,ee)}}y.set(K,A,F,W,q)}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var fH={kernelName:ug,backendName:"cpu",kernelFunc:hH};function mH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s;ve([r,a],"conv2dBackpropInput");let p=w.computeStrides(a.shape),d=w.computeStrides(r.shape),h=N.convertConv2DDataFormat(l),f=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,h),m=new Vt(f.inShape,"float32"),g=m.values,b=n.data.get(r.dataId).values,y=n.data.get(a.dataId).values,[v,x,k]=p,{batchSize:C,filterHeight:T,filterWidth:E,inChannels:A,inHeight:P,inWidth:R,outChannels:F,outHeight:$,outWidth:z,strideHeight:W,strideWidth:q}=f;h=f.dataFormat;let K=T-1-f.padInfo.top,Y=E-1-f.padInfo.left,Z=h==="channelsLast",te=m.strides[0],ee=Z?m.strides[1]:m.strides[2],se=Z?m.strides[2]:1,ne=Z?1:m.strides[1],oe=d[0],re=Z?d[1]:d[2],le=Z?d[2]:1,me=Z?1:d[1];for(let we=0;we<C;++we)for(let Se=0;Se<A;++Se)for(let Ee=0;Ee<P;++Ee){let 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l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(r.shape,u,i,1,o),d=p.strideDepth,h=p.strideHeight,f=p.strideWidth,m=p.filterDepth,g=p.filterHeight,b=p.filterWidth,y=new Vt(p.filterShape,"float32"),v=y.values,[x,k,C,T]=y.strides,E=n.data.get(a.dataId).values,[A,P,R,F]=c,$=n.data.get(r.dataId).values,[z,W,q,K]=l,Y=p.padInfo.front,Z=p.padInfo.left,te=p.padInfo.top;for(let ee=0;ee<m;++ee){let se=Math.max(0,Math.ceil((Y-ee)/d)),ne=Math.min(p.outDepth,(p.inDepth+Y-ee)/d),oe=ee*x;for(let re=0;re<g;++re){let le=Math.max(0,Math.ceil((te-re)/h)),me=Math.min(p.outHeight,(p.inHeight+te-re)/h),we=re*k+oe;for(let Se=0;Se<b;++Se){let Ee=Math.max(0,Math.ceil((Z-Se)/f)),Pe=Math.min(p.outWidth,(p.inWidth+Z-Se)/f),Xe=Se*C+we;for(let Je=0;Je<p.inChannels;++Je){let Ye=Je*T+Xe;for(let tt=0;tt<p.outChannels;++tt){let Ce=0;for(let ut=0;ut<p.batchSize;++ut){let rt=ut*z,Zt=ut*A;for(let Nt=se;Nt<ne;++Nt){let Et=(ee+Nt*d-Y)*W+rt,Jt=Nt*P+Zt;for(let Sn=le;Sn<me;++Sn){let Xt=(re+Sn*h-te)*q+Et,Rn=Sn*R+Jt;for(let en=Ee;en<Pe;++en){let Ls=(Se+en*f-Z)*K+Xt,xi=en*F+Rn;Ce+=$[Ls+Je]*E[xi+tt]}}}}v[Ye+tt]=Ce}}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var xH={kernelName:lg,backendName:"cpu",kernelFunc:vH};function wH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s;ve([r],"conv3dBackpropInputV2");let l=w.computeStrides(r.shape),c=w.computeStrides(a.shape),p=N.computeConv3DInfo(u,a.shape,o,1,i),d=new Vt(p.inShape,"float32"),h=d.values,[f,m,g,b]=d.strides,y=n.data.get(r.dataId).values,[v,x,k,C]=l,T=n.data.get(a.dataId).values,[E,A,P,R]=c,{batchSize:F,filterDepth:$,filterHeight:z,filterWidth:W,inChannels:q,inDepth:K,inHeight:Y,inWidth:Z,outChannels:te,outDepth:ee,outHeight:se,outWidth:ne,strideDepth:oe,strideHeight:re,strideWidth:le}=p,me=$-1-p.padInfo.front,we=z-1-p.padInfo.top,Se=W-1-p.padInfo.left;for(let Ee=0;Ee<F;++Ee)for(let Pe=0;Pe<q;++Pe)for(let Xe=0;Xe<K;++Xe){let 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TH(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,[c,p,d,h]=r.shape,f=a.shape[0],[m,g]=o,b=De([f,m,g,h],"float32"),y=n.data.get(a.dataId).values,v=n.data.get(i.dataId).values,x=n.data.get(r.dataId).values,k=w.computeStrides(r.shape),C=w.computeStrides(b.shape);for(let T=0;T<f;T++){let E=T*4,A=y[E],P=y[E+1],R=y[E+2],F=y[E+3],$=v[T];if($>=c)continue;let z=m>1?(R-A)*(p-1)/(m-1):0,W=g>1?(F-P)*(d-1)/(g-1):0;for(let q=0;q<m;q++){let K=m>1?A*(p-1)+q*z:.5*(A+R)*(p-1);if(K<0||K>p-1){for(let Y=0;Y<g;Y++)for(let Z=0;Z<h;Z++){let te=Z+Y*C[2]+q*C[1]+T*C[0];b.values[te]=l}continue}if(u==="bilinear"){let Y=Math.floor(K),Z=Math.ceil(K),te=K-Y;for(let ee=0;ee<g;ee++){let se=g>1?P*(d-1)+ee*W:.5*(P+F)*(d-1);if(se<0||se>d-1){for(let le=0;le<h;le++){let me=le+ee*C[2]+q*C[1]+T*C[0];b.values[me]=l}continue}let ne=Math.floor(se),oe=Math.ceil(se),re=se-ne;for(let le=0;le<h;le++){let me=le+ne*k[2]+Y*k[1]+$*k[0],we=x[me];me=le+oe*k[2]+Y*k[1]+$*k[0];let Se=x[me];me=le+ne*k[2]+Z*k[1]+$*k[0];let Ee=x[me];me=le+oe*k[2]+Z*k[1]+$*k[0];let Pe=x[me],Xe=we+(Se-we)*re,Je=Ee+(Pe-Ee)*re;me=le+ee*C[2]+q*C[1]+T*C[0],b.values[me]=Xe+(Je-Xe)*te}}}else for(let Y=0;Y<g;++Y){let Z=g>1?P*(d-1)+Y*W:.5*(P+F)*(d-1);if(Z<0||Z>d-1){for(let se=0;se<h;se++){let ne=se+Y*C[2]+q*C[1]+T*C[0];b.values[ne]=l}continue}let te=Math.round(Z),ee=Math.round(K);for(let se=0;se<h;se++){let ne=se+te*k[2]+ee*k[1]+$*k[0],oe=se+Y*C[2]+q*C[1]+T*C[0];b.values[oe]=x[ne]}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var $H={kernelName:lo,backendName:"cpu",kernelFunc:TH};function _H(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s;ve(r,"cumsum");let u=N.getAxesPermutation([a],r.shape.length),l=r;u!=null&&(l=Vn({inputs:{x:r},backend:n,attrs:{perm:u}}));let c=N.getInnerMostAxes(1,r.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most 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new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var RH={kernelName:dg,backendName:"cpu",kernelFunc:EH};function DH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s;w.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${d}'`);let h=N.computeConv2DInfo(r.shape,a.shape,i,d,o,l,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:b,padInfo:y}=h,v=y.left,x=y.top,k=h.outChannels/h.inChannels,C=new Vt(h.outShape,r.dtype),T=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,A=C.values;for(let P=0;P<h.batchSize;++P){let R=P*c[0],F=P*C.strides[0];for(let $=0;$<h.outHeight;++$){let z=F+$*C.strides[1],W=$*h.strideHeight-x;for(let q=0;q<f;++q){let K=W+q*g;if(K<0||K>=h.inHeight)continue;let Y=q*p[0],Z=R+K*c[1];for(let te=0;te<h.outWidth;++te){let ee=z+te*C.strides[2],se=te*h.strideWidth-v;for(let ne=0;ne<m;++ne){let oe=se+ne*b;if(oe<0||oe>=h.inWidth)continue;let re=Y+ne*p[1],le=Z+oe*h.inChannels,me=ee,we=re;for(let Se=0;Se<h.inChannels;++Se){let Ee=T[le+Se];for(let Pe=0;Pe<k;++Pe)A[me+Pe]+=Ee*E[we+Pe];me+=k,we+=k}}}}}}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var OH={kernelName:Aa,backendName:"cpu",kernelFunc:NC};function PH(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s;ve([r,a],"depthwiseConv2dNativeBackpropFilter");let p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),{strideHeight:d,strideWidth:h,filterHeight:f,filterWidth:m}=p,g=new Vt(p.filterShape,"float32"),b=p.padInfo.left,y=p.padInfo.top,v=p.outChannels/p.inChannels,x=n.data.get(r.dataId).values,k=new Vt(r.shape,r.dtype,x),C=n.data.get(a.dataId).values,T=new Vt(a.shape,a.dtype,C);for(let E=0;E<f;++E){let A=Math.max(0,Math.ceil((y-E)/d)),P=Math.min(p.outHeight,(p.inHeight+y-E)/d);for(let R=0;R<m;++R){let F=Math.max(0,Math.ceil((b-R)/h)),$=Math.min(p.outWidth,(p.inWidth+b-R)/h);for(let z=0;z<p.outChannels;++z){let W=Math.trunc(z/v),q=z%v,K=0;for(let Y=0;Y<p.batchSize;++Y)for(let Z=A;Z<P;++Z){let te=E+Z*d-y;for(let ee=F;ee<$;++ee){let se=R+ee*h-b;K+=k.get(Y,te,se,W)*T.get(Y,Z,ee,z)}}g.set(K,E,R,W,q)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var zH={kernelName:pg,backendName:"cpu",kernelFunc:PH};function MH(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s;ve([r,a],"depthwiseConv2DNativeBackpropInput");let p=w.computeStrides(r.shape),d=w.computeStrides(a.shape),h=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),f=new Vt(h.inShape,"float32"),m=f.values,[g,b,y]=f.strides,v=n.data.get(r.dataId).values,[x,k,C]=p,T=n.data.get(a.dataId).values,[E,A,P]=d,{batchSize:R,filterHeight:F,filterWidth:$,inChannels:z,inHeight:W,inWidth:q,outChannels:K,outHeight:Y,outWidth:Z,strideHeight:te,strideWidth:ee}=h,se=F-1-h.padInfo.top,ne=$-1-h.padInfo.left,oe=K/z;for(let re=0;re<R;++re)for(let le=0;le<z;++le)for(let me=0;me<W;++me){let we=me-se,Se=Math.max(0,Math.ceil(we/te)),Ee=Math.min(Y,(F+we)/te);for(let Pe=0;Pe<q;++Pe){let Xe=Pe-ne,Je=Math.max(0,Math.ceil(Xe/ee)),Ye=Math.min(Z,($+Xe)/ee),tt=0;for(let Ce=Se;Ce<Ee;++Ce){let ut=Ce*te-we;for(let rt=Je;rt<Ye;++rt){let Zt=rt*ee-Xe,Nt=x*re+k*Ce+C*rt,In=E*(F-1-ut)+A*($-1-Zt)+P*le;for(let Et=0;Et<oe;++Et){let Jt=le*oe+Et,Sn=v[Nt+Jt],Cn=T[In+Et];tt+=Sn*Cn}}}m[g*re+b*me+y*Pe+le]=tt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var LH={kernelName:hg,backendName:"cpu",kernelFunc:MH};function BH(e){let{inputs:t,backend:n}=e,{x:s}=t,r=w.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,i=De([r,r],s.dtype),o=i.values;for(let l=0;l<a.length;l++)o[l*r+l]=a[l];let u=[...s.shape,...s.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var VH={kernelName:fg,backendName:"cpu",kernelFunc:BH},WH={kernelName:Xd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:i,dilations:o}=n,u=t,l=u.data.get(s.dataId).values,c=s.shape.length,p=u.data.get(r.dataId).values,d=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:b,outWidth:y,padInfo:v,strideHeight:x,strideWidth:k,filterHeight:C,filterWidth:T,dilationHeight:E,dilationWidth:A,outShape:P}=N.computeDilation2DInfo(s.shape,r.shape,a,i,"NHWC",o),R=w.sizeFromShape(P),F=P.length,$=w.getArrayFromDType(s.dtype,R);for(let W=0;W<h;++W)for(let q=0;q<b;++q){let K=q*x-v.top;for(let Y=0;Y<y;++Y){let Z=Y*k-v.left;for(let te=0;te<g;++te){let ee=Number.MIN_SAFE_INTEGER;for(let ne=0;ne<C;++ne){let oe=K+ne*E;if(oe>=0&&oe<f)for(let re=0;re<T;++re){let le=Z+re*A;if(le>=0&&le<m){let me=w.locToIndex([W,oe,le,te],c,w.computeStrides(s.shape)),we=w.locToIndex([ne,re,te],d,w.computeStrides(r.shape)),Se=l[me]+p[we];Se>ee&&(ee=Se)}}}let se=w.locToIndex([W,q,Y,te],F,w.computeStrides(P));$[se]=ee}}}return{dataId:u.write(w.toTypedArray($,s.dtype),P,s.dtype),shape:P,dtype:s.dtype}}},UH={kernelName:Xf,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:C,dilationHeight:T,dilationWidth:E,outShape:A}=N.computeDilation2DInfo(s.shape,r.shape,i,o,"NHWC",u);w.assert(a.rank===A.length,()=>`Error in ${Xf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);let P=w.toNestedArray(A,l.data.get(a.dataId).values),R=w.makeZerosNestedTypedArray(r.shape,r.dtype);for(let $=0;$<d;++$)for(let z=0;z<g;++z){let W=z*v-y.top;for(let q=0;q<b;++q){let K=q*x-y.left;for(let Y=0;Y<m;++Y){let Z=Number.MIN_SAFE_INTEGER,te=0,ee=0;for(let se=0;se<k;++se){let ne=W+se*T;if(ne>=0&&ne<h)for(let oe=0;oe<C;++oe){let re=K+oe*E;if(re>=0&&re<f){let le=c[$][ne][re][Y]+p[se][oe][Y];le>Z&&(Z=le,te=se,ee=oe)}}}R[te][ee][Y]+=P[$][z][q][Y]}}}return{dataId:l.write(w.toTypedArray(R,s.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}},GH={kernelName:Kf,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r,dy:a}=e,{strides:i,pad:o,dilations:u}=n,l=t,c=w.toNestedArray(s.shape,l.data.get(s.dataId).values),p=w.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:d,inHeight:h,inWidth:f,inChannels:m,outHeight:g,outWidth:b,padInfo:y,strideHeight:v,strideWidth:x,filterHeight:k,filterWidth:C,dilationHeight:T,dilationWidth:E,outShape:A}=N.computeDilation2DInfo(s.shape,r.shape,i,o,"NHWC",u);w.assert(a.rank===A.length,()=>`Error in ${Kf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);let 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Ne=X();Ne.registerFlag("HAS_WEBGL",()=>Ne.getNumber("WEBGL_VERSION")>0);Ne.registerFlag("WEBGL_VERSION",()=>Bm(2)?2:Bm(1)?1:0);Ne.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Ne.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Ne.get("WEBGL_VERSION")===2);Ne.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Ne.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Ne.registerFlag("WEBGL_PACK",()=>Ne.getBool("HAS_WEBGL"));Ne.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_CLIP",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_PACK_REDUCE",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_LAZILY_UNPACK",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_CONV_IM2COL",()=>Ne.getBool("WEBGL_PACK"));Ne.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>ZC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>JC(Ne.getNumber("WEBGL_VERSION")));Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let 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       bool isnan_custom(float val) {
-        return (val > 0.0 || val < 0.0) ? false : val != 0.0;
+        uint floatToUint = floatBitsToUint(val);
+        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
       }
 
       bvec4 isnan_custom(vec4 val) {
@@ -105,11 +106,11 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function gi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function qp(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function $5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function _5(e,t,n="index"){let s=e.map((a,i)=>i),r=$5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function dv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function gi(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / ${r}`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${i}; ${o};`}).join("")}function Hp(e,t,n="index"){let s=w.computeStrides(t);return s.map((r,a)=>{let i=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,o=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${i}; ${o};`}).join("")}function $5(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function _5(e,t,n="index"){let s=e.map((a,i)=>i),r=$5(s,t);return r.map((a,i)=>{let o=`int ${e[i]} = ${n} / ${r[i]}`,u=i===r.length-1?`int ${e[i+1]} = ${n} - ${e[i]} * ${r[i]}`:`index -= ${e[i]} * ${r[i]}`;return`${o}; ${u};`}).join("")}function cv(e){let t=w.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function pv(){return`
+`}function dv(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
@@ -152,10 +153,10 @@ Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To spee
 
     return c / 255.0;
   }
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 `),i=t.texShape,o=hn(),u=F5(o),l,c,p=z5(o);return t.isPacked?(l=R5(t.logicalShape,i,n.enableShapeUniforms),c=P5(o)):(l=D5(t.logicalShape,i,n.enableShapeUniforms),c=O5(o)),n.packedInputs&&(p+=V5),[p,u,c,r,l,a,n.userCode].join(`
-`)}function su(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return J5(e,t);case 1:return tK(e,t);case 2:return sK(e,t);case 3:return aK(e,t);case 4:return oK(e,t);case 5:return uK(e);case 6:return lK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function i1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Z5(e);case 1:return eK(e,t);case 2:return nK(e,t);case 3:return rK(e,t);default:return iK(e,t)}}function E5(e,t,n=!1,s){let r="";n?r+=i1(e,s):r+=su(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=cK(e,t):r+=dK(e,t)),r}function R5(e,t,n){switch(e.length){case 0:return o1();case 1:return W5(e,t,n);case 2:return Y5(e,t,n);case 3:return G5(e,t,n);default:return q5(e,t,n)}}function D5(e,t,n){switch(e.length){case 0:return o1();case 1:return U5(e,t,n);case 2:return Q5(e,t,n);case 3:return H5(e,t,n);case 4:return j5(e,t,n);case 5:return K5(e,t);case 6:return X5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function F5(e){return`
+`)}function nu(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return J5(e,t);case 1:return tK(e,t);case 2:return sK(e,t);case 3:return aK(e,t);case 4:return oK(e,t);case 5:return uK(e);case 6:return lK(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function i1(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Z5(e);case 1:return eK(e,t);case 2:return nK(e,t);case 3:return rK(e,t);default:return iK(e,t)}}function E5(e,t,n=!1,s){let r="";n?r+=i1(e,s):r+=nu(e,s);let a=e.shapeInfo.logicalShape,i=t.logicalShape;return a.length<=i.length&&(n?r+=cK(e,t):r+=dK(e,t)),r}function R5(e,t,n){switch(e.length){case 0:return o1();case 1:return W5(e,t,n);case 2:return Y5(e,t,n);case 3:return G5(e,t,n);default:return q5(e,t,n)}}function D5(e,t,n){switch(e.length){case 0:return o1();case 1:return U5(e,t,n);case 2:return Q5(e,t,n);case 3:return H5(e,t,n);case 4:return j5(e,t,n);case 5:return K5(e,t);case 6:return X5(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function F5(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
@@ -362,7 +363,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
     int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-    ${qp(["r","c","d"],e)}
+    ${Hp(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
 `;let s=gi(["r","c","d"],e);return`
@@ -419,7 +420,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
       int index = resTexRC.x * outTexShape[1] + resTexRC.y;
-      ${qp(["r","c","d","d2"],e)}
+      ${Hp(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
   `;let s=gi(["r","c","d","d2"],e);return`
@@ -573,7 +574,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     }
   `}function tK(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
       float ${s}(int index) {
-        ${ru(e)}
+        ${su(e)}
       }
     `;let r=e.shapeInfo.texShape,a=r[0],i=r[1];if(i===1&&a===1)return`
       float ${s}(int index) {
@@ -643,15 +644,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
       return sampleTexture(${s}, uv);
     }
-  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=au(e,u),h=["row","col"];return`
-      ${su(d,t)}
+  `}let{newShape:i,keptDims:o}=w.squeezeShape(n),u=i;if(u.length<n.length){let d=ru(e,u),h=["row","col"];return`
+      ${nu(d,t)}
       float ${r}(int row, int col) {
-        return ${r}(${iu(h,o)});
+        return ${r}(${au(h,o)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;let l=a[0],c=a[1],p=bi(s);return c===1?t?`
       float ${r}(int row, int col) {
@@ -691,10 +692,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${l}, ${c}, index);
     return sampleTexture(${s}, uv);
   }
-`}function rK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=au(e,d),m=["b","row","col"];return`
+`}function rK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,i=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=ru(e,d),m=["b","row","col"];return`
         ${i1(f,t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${iu(m,h)});
+          return ${r}(${au(m,h)});
         }
       `}let o=hn();if(t)return`
     vec4 ${r}(int b, int row, int col) {
@@ -711,16 +712,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${u}, ${l}, ${p}, ${c}, b, row, col);
       return ${o.texture2D}(${s}, uv);
     }
-  `}function aK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=au(e,l),g=["row","col","depth"];return`
-        ${su(m,t)}
+  `}function aK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],i=n[2],{newShape:o,keptDims:u}=w.squeezeShape(n),l=o;if(l.length<n.length){let m=ru(e,l),g=["row","col","depth"];return`
+        ${nu(m,t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${iu(g,u)});
+          return ${r}(${au(g,u)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${i}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
       float ${r}(int row, int col, int depth) {
@@ -789,16 +790,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});
       return ${r.texture2D}(${n}, uv);
     }
-  `}function oK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=au(e,u),v=["row","col","depth","depth2"];return`
-      ${su(y,t)}
+  `}function oK(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],i=n[2]*a,o=n[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(n);if(u.length<n.length){let y=ru(e,u),v=["row","col","depth","depth2"];return`
+      ${nu(y,t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${iu(v,l)});
+        return ${r}(${au(v,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${o}, ${i}, ${a}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===o&&c==null)return t?`
       float ${r}(int row, int col, int depth, int depth2) {
@@ -859,10 +860,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index + ${b});
       return sampleTexture(${s}, uv);
     }
-  `}function uK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=au(e,u),g=["row","col","depth","depth2","depth3"];return`
-      ${su(m)}
+  `}function uK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,i=t[2]*a,o=t[1]*i,{newShape:u,keptDims:l}=w.squeezeShape(t);if(u.length<t.length){let m=ru(e,u),g=["row","col","depth","depth2","depth3"];return`
+      ${nu(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${iu(g,l)});
+        return ${s}(${au(g,l)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -870,7 +871,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${o}, ${i}, ${a}, ${r})) +
           depth3;
-        ${ru(e)}
+        ${su(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===o&&c==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -900,11 +901,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function lK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=au(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
-      ${su(g)}
+  `}function lK(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=w.squeezeShape(t);if(r.length<t.length){let g=ru(e,r),b=["row","col","depth","depth2","depth3","depth4"];return`
+      ${nu(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${iu(b,a)});
+        return ${s}(${au(b,a)});
       }
     `}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -915,7 +916,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${i}, 1)));
-        ${ru(e)}
+        ${su(e)}
       }
     `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
       float ${s}(int row, int col, int depth,
@@ -950,7 +951,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function ru(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function su(e){let t=e.name,n=w.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
@@ -981,9 +982,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${d}
       return get${s}(${f});
     }
-  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function hv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function au(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function iu(e,t){return t.map(n=>e[n]).join(", ")}function pK(e,t,n,s){let r=n.map((x,k)=>{let C={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(C.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:C}}),a=r.map(x=>x.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=A5(r,i,t),u=zC(e.gl,o),l=e.createProgram(u),c=null,p=e.getUniformLocation(l,"NAN",!1);X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let d=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,d),h[`offset${k}`]=e.getUniformLocation(l,`offset${k}`,d),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(l,`${k}Shape`,d),m[`${k}TexShape`]=e.getUniformLocation(l,`${k}TexShape`,d))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,"outShape",d),y=e.getUniformLocation(l,"outShapeStrides",d),b=e.getUniformLocation(l,"outTexShape",d));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(l,x.name,d)}),{program:t,fragmentShader:u,source:o,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function nw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function hK(e,t,n,s,r){t.program.enableShapeUniforms||(nw(t.inShapeInfos,n),nw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=hv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function fK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=hv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function kn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var mK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
+  `}function ot(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function pv(e,t,n){let{newShape:s,keptDims:r}=w.squeezeShape(t),a=t.length,i=e&&a===3&&t[0]===1,o=i?t.slice(1):s,u=!e&&a>1&&!w.arraysEqual(t,n)&&s.length<a||i;return{useSqueezeShape:u,uniformShape:u?o:t,keptDims:r}}function ru(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function au(e,t){return t.map(n=>e[n]).join(", ")}function pK(e,t,n,s){let r=n.map((x,k)=>{let C={logicalShape:x.shape,texShape:x.isUniform?null:x.texData.texShape,isUniform:x.isUniform,isPacked:x.isUniform?!1:x.texData.isPacked,flatOffset:null};return x.texData!=null&&x.texData.slice!=null&&x.texData.slice.flatOffset>0&&(C.flatOffset=x.texData.slice.flatOffset),{name:t.variableNames[k],shapeInfo:C}}),a=r.map(x=>x.shapeInfo),i={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},o=A5(r,i,t),u=zC(e.gl,o),l=e.createProgram(u),c=null,p=e.getUniformLocation(l,"NAN",!1);X().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(l,"INFINITY",!1));let d=!1,h={},f={},m={};for(let x=0;x<t.variableNames.length;x++){let k=t.variableNames[x];h[k]=e.getUniformLocation(l,k,d),h[`offset${k}`]=e.getUniformLocation(l,`offset${k}`,d),t.enableShapeUniforms&&(f[`${k}Shape`]=e.getUniformLocation(l,`${k}Shape`,d),m[`${k}TexShape`]=e.getUniformLocation(l,`${k}TexShape`,d))}let g,b,y;t.enableShapeUniforms&&(g=e.getUniformLocation(l,"outShape",d),y=e.getUniformLocation(l,"outShapeStrides",d),b=e.getUniformLocation(l,"outTexShape",d));let v=[];return t.customUniforms&&t.customUniforms.forEach((x,k)=>{v[k]=e.getUniformLocation(l,x.name,d)}),{program:t,fragmentShader:u,source:o,webGLProgram:l,uniformLocations:h,customUniformLocations:v,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:y,outTexShapeLocation:b}}function tw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],i=a.shape;if(!w.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&a.isUniform)return;let o=n.texShape,u=a.isUniform?null:a.texData.texShape;if(!w.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function hK(e,t,n,s,r){t.program.enableShapeUniforms||(tw(t.inShapeInfos,n),tw([t.outShapeInfo],[s]));let a=s.texData.texture,i=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,i[0],i[1]):e.setOutputMatrixTexture(a.texture,i[0],i[1]),e.setProgram(t.webGLProgram),X().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((u,l)=>{let c=t.program.variableNames[l],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=pv(t.program.packedInputs,u.shape,u.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,u.texData.texShape[0],u.texData.texShape[1]),p!=null){if(u.isUniform){if(w.sizeFromShape(u.shape)<2)e.gl.uniform1f(p,u.uniformValues[0]);else{let m=u.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}u.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,u.texData.slice.flatOffset),e.setInputMatrixTexture(u.texData.texture.texture,p,l)}});let o=t.outShapeLocation;if(o)switch(s.shape.length){case 1:e.gl.uniform1iv(o,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(o,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(o,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(o,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let u=w.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(u));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(u));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(u));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((u,l)=>{let c=t.customUniformLocations[l],p=r[l];if(u.type==="float")e.gl.uniform1fv(c,p);else if(u.type==="vec2")e.gl.uniform2fv(c,p);else if(u.type==="vec3")e.gl.uniform3fv(c,p);else if(u.type==="vec4")e.gl.uniform4fv(c,p);else if(u.type==="int")e.gl.uniform1iv(c,p);else if(u.type==="ivec2")e.gl.uniform2iv(c,p);else if(u.type==="ivec3")e.gl.uniform3iv(c,p);else if(u.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${u.type} is not supported yet.`)}),e.executeProgram()}function fK(e,t,n){let s="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!i.isUniform){let u=i.texData.texShape,{useSqueezeShape:l,uniformShape:c,keptDims:p}=pv(e.packedInputs,i.shape,u),d="",h="",f="";if(c.length===1&&e.packedInputs){let k=[Math.ceil(u[0]/2),Math.ceil(u[1]/2)];d=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let k=w.computeStrides(c);f=`${k[0]===u[1]}_${k[k.length-1]===u[1]}`}let m=i.shape.length,g=c.length===2&&w.arraysEqual(i.shape,u),b=w.sizeFromShape(i.shape)===1,y=N.getBroadcastDims(i.shape,n.shape),v=!e.packedInputs&&m===n.shape.length&&w.arraysEqual(u,n.texData.texShape),x=e.packedInputs||c.length>2?"":`${u[0]>1}_${u[1]>1}`;s+=`${m}_${v}_${l?p:""}_${c.length}_${b}_${y}_${g}_${d}_${h}_${f}_${x}_${o}`}else{let u=i.isUniform?"uniform":i.texData.texShape;s+=`${i.shape}_${u}_${o}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${X().getNumber("WEBGL_VERSION")}`,a}function kn(e){return X().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var mK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?qp(["r","c","d"],e):gi(["r","c","d"],e)}
+        ${this.enableShapeUniforms?Hp(["r","c","d"],e):gi(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1003,7 +1004,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
     `}},gK=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?qp(["r","c","d"],e):gi(["r","c","d"],e)}
+        ${this.enableShapeUniforms?Hp(["r","c","d"],e):gi(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1037,7 +1038,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${t.output} = encode_float(x);
       }
     `}},vK=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=hn();this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?pv():dv(e)}
+      ${this.enableShapeUniforms?dv():cv(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1095,7 +1096,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?pv():dv(e)}
+        ${this.enableShapeUniforms?dv():cv(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1110,7 +1111,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${n.output} = ${r};
         }
-    `}},wK={};Ae(wK,{bindVertexProgramAttributeStreams:()=>g1,createBufferFromOutputTexture:()=>v1,createFloat16MatrixTexture:()=>p1,createFloat16PackedMatrixTexture:()=>m1,createFloat32MatrixTexture:()=>d1,createIndexBuffer:()=>c1,createPackedMatrixTexture:()=>f1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>l1,createVertexShader:()=>u1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>w1,downloadFloat32MatrixFromBuffer:()=>x1,downloadMatrixFromPackedOutputTexture:()=>I1,downloadPackedMatrixFromBuffer:()=>k1,getInternalFormatForFloat16MatrixTexture:()=>mv,getInternalFormatForFloat16PackedMatrixTexture:()=>yv,getInternalFormatForFloat32MatrixTexture:()=>fv,getInternalFormatForPackedMatrixTexture:()=>bv,getInternalFormatForUnsignedBytesMatrixTexture:()=>gv,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>y1});function u1(e){let t=hn(),n=`${t.version}
+    `}},wK={};Ae(wK,{bindVertexProgramAttributeStreams:()=>g1,createBufferFromOutputTexture:()=>v1,createFloat16MatrixTexture:()=>p1,createFloat16PackedMatrixTexture:()=>m1,createFloat32MatrixTexture:()=>d1,createIndexBuffer:()=>c1,createPackedMatrixTexture:()=>f1,createUnsignedBytesMatrixTexture:()=>h1,createVertexBuffer:()=>l1,createVertexShader:()=>u1,downloadByteEncodedFloatMatrixFromOutputTexture:()=>w1,downloadFloat32MatrixFromBuffer:()=>x1,downloadMatrixFromPackedOutputTexture:()=>I1,downloadPackedMatrixFromBuffer:()=>k1,getInternalFormatForFloat16MatrixTexture:()=>fv,getInternalFormatForFloat16PackedMatrixTexture:()=>bv,getInternalFormatForFloat32MatrixTexture:()=>hv,getInternalFormatForPackedMatrixTexture:()=>gv,getInternalFormatForUnsignedBytesMatrixTexture:()=>mv,uploadDenseMatrixToTexture:()=>b1,uploadPixelDataToTexture:()=>y1});function u1(e){let t=hn(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1119,7 +1120,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return PC(e,n)}function l1(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return BC(e,t)}function c1(e){let t=new Uint16Array([0,1,2,2,1,3]);return VC(e,t)}function Ql(e,t,n,s,r,a){UC(t,n);let i=WC(e),o=e.TEXTURE_2D;return fe(e,()=>e.bindTexture(o,i)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),fe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),X().getNumber("WEBGL_VERSION")===1?fe(e,()=>e.texImage2D(o,0,s,t,n,0,r,a,null)):fe(e,()=>e.texStorage2D(o,1,s,t,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:i,texShape:[n,t]}}function fv(e){return e.internalFormatFloat}function d1(e,t,n,s){let[r,a]=Yl(t,n);return Ql(e,r,a,fv(s),s.textureFormatFloat,e.FLOAT)}function mv(e){return e.internalFormatHalfFloat}function p1(e,t,n,s){let[r,a]=Yl(t,n);return Ql(e,r,a,mv(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function gv(e){return e.downloadTextureFormat}function h1(e,t,n,s){let[r,a]=Yl(t,n);return Ql(e,r,a,gv(s),e.RGBA,e.UNSIGNED_BYTE)}function bv(e){return e.internalFormatPackedFloat}function f1(e,t,n,s){let[r,a]=tu(t,n);return Ql(e,r,a,bv(s),e.RGBA,e.FLOAT)}function yv(e){return e.internalFormatPackedHalfFloat}function m1(e,t,n,s){let[r,a]=tu(t,n);return Ql(e,r,a,yv(s),e.RGBA,s.textureTypeHalfFloat)}function g1(e,t,n){return fe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Lm(e,t,"clipSpacePos",n,3,20,0)&&Lm(e,t,"uv",n,2,20,12)}function b1(e,t,n,s,r,a){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*s*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new Float32Array(n*s*4),o=e.FLOAT,u=a.internalFormatPackedFloat),i.set(r),X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,o,i)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,s,0,e.RGBA,o,i)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function y1(e,t,n){fe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):X().getNumber("WEBGL_VERSION")===2?fe(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):fe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),fe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function v1(e,t,n,s){let r=e.createBuffer();fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let o=4*4*t*n;return fe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,o,e.STREAM_READ)),fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),fe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function x1(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function w1(e,t,n,s){let[r,a]=Yl(t,n),i=4,o=new Uint8Array(m5(t*n,i));return fe(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function k1(e,t,n,s,r,a,i,o){let u=e,l=new Float32Array(g5(a,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function I1(e,t,n){let s=new Float32Array(t*n*4);return fe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var jf=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=X().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,p5(t,e)):this.gl=vs(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(X().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Fu(this.gl,r),Mn(this.gl,a))this.textureHalfFloatExtension=Fu(this.gl,a);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Mn(this.gl,s))this.colorBufferHalfFloatExtension=Fu(this.gl,s);else if(X().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Mn(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Mn(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=l1(this.gl),this.indexBuffer=c1(this.gl),this.framebuffer=GC(this.gl),this.textureConfig=cv(this.gl,this.textureHalfFloatExtension)}get debug(){return X().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. 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this.throwIfDisposed(),m1(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),f1(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Lm(this.gl,this.framebuffer),this.outputTexture=null),fe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>w1(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return k1(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return x1(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=v1(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(X().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>I1(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=u1(t));let n=MC(t);return fe(t,()=>t.attachShader(n,this.vertexShader)),fe(t,()=>t.attachShader(n,e)),LC(t,n),this.debug&&sd(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=g1(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&fe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&sd(this.gl,this.program),fe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?qC(this.gl,e,t):jC(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),fe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),KC(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=eu(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&sd(this.gl,this.program),Fu(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),fe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),fe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Du(this.gl,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await w.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=kK(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&w.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),rd(this.gl,e,this.framebuffer),this.debug&&Fu(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(rd(this.gl,this.outputTexture,this.framebuffer),this.debug&&Fu(this.gl)):Lm(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;rd(s,e,this.framebuffer),this.debug&&Fu(s),this.outputTexture=e,fe(s,()=>s.viewport(0,0,t,n)),fe(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),fe(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function kK(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:IK,bincountImpl:S1,bincountReduceImpl:SK,ceilImpl:CK,concatImpl:NK,equalImpl:TK,expImpl:$K,expm1Impl:_K,floorImpl:AK,gatherNdImpl:EK,gatherV2Impl:RK,greaterImpl:DK,greaterEqualImpl:FK,lessImpl:OK,lessEqualImpl:PK,linSpaceImpl:zK,logImpl:MK,maxImpl:LK,maximumImpl:BK,minimumImpl:VK,multiplyImpl:WK,negImpl:UK,notEqualImpl:GK,prodImpl:HK,rangeImpl:qK,rsqrtImpl:jK,sigmoidImpl:KK,simpleAbsImpl:C1,sliceImpl:XK,sparseFillEmptyRowsImpl:YK,sparseReshapeImpl:QK,sparseSegmentReductionImpl:N1,sqrtImpl:ZK,stridedSliceImpl:JK,stringNGramsImpl:eX,stringSplitImpl:tX,stringToHashBucketFastImpl:nX,subImpl:sX,tileImpl:rX,topKImpl:aX,transposeImpl:yv,uniqueImpl:iX}=Yy;function T1(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function un(e,t){return t===1?[e]:T1(e,t)}function oX(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var uX=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=kn(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
@@ -1159,7 +1160,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${s>0?"}":""}
       `}this.userCode=`
       ${lX(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?pv():dv(e)}
+      ${this.enableShapeUniforms?dv():cv(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1179,7 +1180,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${t?_5(["r","c","d"],"inputShape"):gi(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var cX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=rw(t,n),r=aw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=sw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=rw(n,s),a=aw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=sw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function dX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function sw(e,t,n,s,r){let a=pX(t,s),i;if(r){let[u,l]=tu(e[0],e[1]);i=u*l}else{let[u,l]=Yl(e[0],e[1]);i=u*l}let o=dX(n,a);return i*o}function pX(e,t){switch(e){case 3:return bv(t);case 4:return yv(t);case 1:return fv(t);case 0:return mv(t);case 2:return gv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function hX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function rw(e,t){if(e===1)return 3;if(e===0||e==null)return hX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function aw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
+  `}var cX=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=sw(t,n),r=rw(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=nw(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return s===3?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===4?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===1?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===0?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===2&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=sw(n,s),a=rw(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let i=nw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),o=X().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function dX(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function nw(e,t,n,s,r){let a=pX(t,s),i;if(r){let[u,l]=eu(e[0],e[1]);i=u*l}else{let[u,l]=Xl(e[0],e[1]);i=u*l}let o=dX(n,a);return i*o}function pX(e,t){switch(e){case 3:return gv(t);case 4:return bv(t);case 1:return hv(t);case 0:return fv(t);case 2:return mv(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function hX(e){return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?3:1:e?4:0}function sw(e,t){if(e===1)return 3;if(e===0||e==null)return hX(t);if(e===3||e===2)return 2;throw new Error(`Unknown logical texture type ${e}`)}function rw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Hs=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1190,7 +1191,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},ts="if (isnan(x)) return x;",fX="return x;",iw="return abs(x);",mX="return (x >= 0.0) ? x : (exp(x) - 1.0);",gX=ts+`
+    `}},ts="if (isnan(x)) return x;",fX="return x;",aw="return abs(x);",mX="return (x >= 0.0) ? x : (exp(x) - 1.0);",gX=ts+`
   return (x < 0.0) ? 0.0 : x;
 `,bX=ts+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
@@ -1241,10 +1242,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(getChannel(packedInput, ${i}));
       }
-    `}},CX=xs.whereImpl,NX=1e-7,TX=1e-4,Zc={};function $X(e){return e in Zc||(Zc[e]={}),Zc[e]}var _X=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),AX=600;function EX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*AX/1024/1024}var _1=class extends nl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof jf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new jf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new jf(n),this.binaryCache=$X(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new cX(this.gpgpu),this.numMBBeforeWarning=EX(),this.texData=new Ud(this,Ss())}nextDataId(){return _1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Yr(i,Pi):p=new Hs(i,Pi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=N.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Yr(s,Pi):h=new Hs(s,Pi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Yc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=N.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Yr(r,Pi):d=new Hs(r,Pi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!FC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Yc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?id(t):t,o=a?new yK(i):new bK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=_X){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return CX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Yr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=C1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,iw,e.dtype);let t=new Hs(e.shape,iw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new SX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new uX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ma(e.shape),...ga(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ma(t),...ga(t)],a=new $1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=id(r),o;s?o=new gK(i):o=new mK(i);let u=!0,l=[t!=null?t:Yc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Yc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!el(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=fK(e,l,c),d=this.getAndSaveBinary(p,()=>pK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),hK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?NX:TX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=QC(n,o),t.texShape=c),r!=null){let p=id(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=tu(c[0],c[1])),o?d=new xK(p,m):d=new vK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),C=this.texData.get(k.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=RX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},A1=_1;A1.nextDataId=0;function RX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var xpe="0.0.0";function DX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}dp.isBrowser()&&pp("webgl",()=>new A1,2);var wpe={forceHalfFloat:DX},E1=`
+    `}},CX=xs.whereImpl,NX=1e-7,TX=1e-4,Qc={};function $X(e){return e in Qc||(Qc[e]={}),Qc[e]}var _X=X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),AX=600;function EX(){return X().global.screen==null?1024:X().global.screen.height*X().global.screen.width*window.devicePixelRatio*AX/1024/1024}var _1=class extends tl{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!X().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof qf)t=e;else{let n=vs(X().getNumber("WEBGL_VERSION"),e);t=new qf(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=vs(X().getNumber("WEBGL_VERSION"));t=new qf(n),this.binaryCache=$X(X().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new cX(this.gpgpu),this.numMBBeforeWarning=EX(),this.texData=new Wd(this,Ss())}nextDataId(){return _1.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||X().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:1,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(X().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:1,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:i,isPacked:o}=t;if(a!=null){let p;o?p=new Yr(i,Pi):p=new Hs(i,Pi);let d=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let u=this.activeTimers!=null,l;u&&(l=w.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=N.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=w.now()-l),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new Yr(s,Pi):h=new Hs(s,Pi);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(X().getBool("DEBUG")&&!X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&X().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,l;if(a!=="complex64"&&X().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture.texture,...Xc(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),f=h[0],m=h[1];c=N.mergeRealAndImagArrays(f,m)}else if(u==null)c=this.getValuesFromTexture(e);else{let h=w.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}if(l!=null&&this.disposeIntermediateTensorInfo(l),u!=null){let h=this.gpgpu.gl;fe(h,()=>h.deleteBuffer(u))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Ss().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:i,isPacked:o,texture:u}=n;if(i==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;o?d=new Yr(r,Pi):d=new Hs(r,Pi);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:i}],i),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(u==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let l=this.decode(e,t.customTexShape),c=Ss().makeTensorFromDataId(l.dataId,l.shape,l.dtype),p=this.texData.get(l.dataId);return{tensorRef:c,...p.texture}}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!FC(n))throw X().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=w.sizeFromShape(t);if(X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...Xc(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=X().getBool("WEBGL_PACK")&&s===!0,i=a?ad(t):t,o=a?new yK(i):new bK(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),a=w.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:w.now(),endMs:null}}endTimer(e){return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=w.now(),e)}async getQueryTime(e){if(X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=_X){return X().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&w.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return CX(e.shape,t)}packedUnaryOp(e,t,n){let s=new Yr(e.shape,t),r=this.compileAndRun(s,[e],n);return Ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=C1(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,aw,e.dtype);let t=new Hs(e.shape,aw),n=this.compileAndRun(t,[e]);return Ss().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return Ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new SX(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new uX(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ma(e.shape),...ga(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ma(t),...ga(t)],a=new $1(r,n),i=!0,o=[n],u=this.runWebGLProgram(a,[s],e.dtype,o,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=w.sizeFromShape(r),d=t[0]*t[1]*4;w.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let i=ad(r),o;s?o=new gK(i):o=new mK(i);let u=!0,l=[t!=null?t:Xc(i)],c=this.runWebGLProgram(o,[{shape:i,dtype:a,dataId:e}],a,l,u,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let i=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(i.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===0){let g=a!=null?a:Xc(e.outputShape);o.texShape=g.map(b=>b*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),w.sizeFromShape(i.shape)===0)return o.values=w.getTypedArrayFromDType(i.dtype,0),i;let u=[],l=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let b=this.texData.get(g.dataId);if(b.texture==null){if(!e.packedInputs&&w.sizeFromShape(g.shape)<=X().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!b.isPacked!=!!e.packedInputs)g=b.isPacked?this.unpackTensor(g):this.packTensor(g),u.push(g),b=this.texData.get(g.dataId);else if(b.isPacked&&!Ju(b.shape,g.shape)){let y=g,v=g.shape;g.shape=b.shape,g=this.packedReshape(g,v),u.push(g),b=this.texData.get(g.dataId),y.shape=v}return{shape:g.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let c={shape:i.shape,texData:o,isUniform:!1},p=fK(e,l,c),d=this.getAndSaveBinary(p,()=>pK(this.gpgpu,e,l,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),hK(this.gpgpu,d,l,c,s),u.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=X().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=w.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!X().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let g=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),g}return i}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(X().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=j(()=>{if(!X().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=X().getBool("DEBUG");X().set("DEBUG",!1);let t=this.abs(Ie(1e-8)).dataSync()[0];if(X().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?NX:TX}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:i,isPacked:o}=t;if(a!=null)return;let u=this.activeTimers!=null,l;u&&(l=w.now());let c=t.texShape;if(c==null&&(c=QC(n,o),t.texShape=c),r!=null){let p=ad(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(o||!m)&&([h,f]=eu(c[0],c[1])),o?d=new xK(p,m):d=new vK(p,m);let g=m?[f,h]:c,b=this.makeTensorInfo(g,s),y=this.texData.get(b.dataId);m?y.usage=2:y.usage=1,y.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),h,f,r);let v=[[f,h]],x=!0,k=this.runWebGLProgram(d,[b],s,v,x),C=this.texData.get(k.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(b),this.texData.delete(k.dataId),t.values=null,u&&(this.uploadWaitMs+=w.now()-l)}else{let p=this.acquireTexture(c,i,s,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=RX(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*w.bytesPerElement(t)}},A1=_1;A1.nextDataId=0;function RX(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var xpe="0.0.0";function DX(){X().set("WEBGL_FORCE_F16_TEXTURES",!0)}cp.isBrowser()&&dp("webgl",()=>new A1,2);var wpe={forceHalfFloat:DX},E1=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,so=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
+`,no=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=kn(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1254,12 +1255,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float b = getBAtOutCoords();
         setOutput(binaryOperation(a, b));
       }
-    `}},jp=`
+    `}},qp=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,Zl=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=kn(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
+`,Ql=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=kn(r);let a="";if(s)if(r===0||w.sizeFromShape(this.outputShape)===1)a=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
@@ -1303,13 +1304,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function En(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var FX={kernelName:Ma,backendName:"webgl",kernelFunc:En};function Er(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=En({inputs:{x:s},backend:n}),u=En({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var OX={kernelName:jd,backendName:"webgl",kernelFunc:Er},R1="return (a < 0.) ? b * a : a;",D1=`
+    `}};function En(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var FX={kernelName:Ma,backendName:"webgl",kernelFunc:En};function Er(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.texData.get(a.dataId),o=En({inputs:{x:s},backend:n}),u=En({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var OX={kernelName:qd,backendName:"webgl",kernelFunc:Er},R1="return (a < 0.) ? b * a : a;",D1=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function PX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(D1,r.shape,i.shape):new so(R1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var zX={kernelName:La,backendName:"webgl",kernelFunc:PX},F1="return (a < 0.) ? b * a : a;",O1=`
+`;function PX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=n.makeTensorInfo([],"float32",w.createScalarValue(a,"float32")),o=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(D1,r.shape,i.shape):new no(R1,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],"float32");return n.disposeIntermediateTensorInfo(i),u}var zX={kernelName:La,backendName:"webgl",kernelFunc:PX},F1="return (a < 0.) ? b * a : a;",O1=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function MX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(O1,s.shape,r.shape):new so(F1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var LX={kernelName:Qa,backendName:"webgl",kernelFunc:MX},ou="if (isnan(x)) return x;",BX=`
+`;function MX(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(O1,s.shape,r.shape):new no(F1,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var LX={kernelName:Qa,backendName:"webgl",kernelFunc:MX},iu="if (isnan(x)) return x;",BX=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
 `,VX=`
@@ -1317,7 +1318,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Yr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,C={dataId:x.dataId,dtype:x.dtype,shape:u.shape},T={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new so(e,u.shape,l.shape);return c.runWebGLProgram(E,[C,T],yn(x.dtype,k.dtype))}),y=Er({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||yn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?N.fromUint8ToStringArray(f):f,b=u.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Zl(t,u.shape,l.shape,n):h=new so(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function Kp(e,t=!1){if(e==="linear")return t?vX:fX;if(e==="relu")return t?wX:gX;if(e==="elu")return t?xX:mX;if(e==="relu6")return t?kX:bX;if(e==="prelu")return t?O1:F1;if(e==="leakyrelu")return t?D1:R1;if(e==="sigmoid")return t?IX:yX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=kn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
+`;function Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:i}=r,o=a,u=s||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),d=n(p.values,u);return o.makeTensorInfo(i.shape,u,d)}let l=X().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return l?c=new Yr(i.shape,t):c=new Hs(i.shape,e),o.runWebGLProgram(c,[i],u)}}function qt({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,c=o;if(s&&u.dtype==="complex64"){let f=c.texData.get(u.dataId),m=c.texData.get(l.dataId),[g,b]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(v=>{let[x,k]=v,C={dataId:x.dataId,dtype:x.dtype,shape:u.shape},T={dataId:k.dataId,dtype:k.dtype,shape:l.shape},E=new no(e,u.shape,l.shape);return c.runWebGLProgram(E,[C,T],yn(x.dtype,k.dtype))}),y=Er({inputs:{real:g,imag:b},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(b),y}let p=a||yn(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||c.shouldExecuteOnCPU([u,l]))&&r!=null){let f=c.texData.get(u.dataId).values,m=c.texData.get(l.dataId).values,g=u.dtype==="string"?N.fromUint8ToStringArray(f):f,b=u.dtype==="string"?N.fromUint8ToStringArray(m):m,[y,v]=r(u.shape,l.shape,g,b,p),x=c.makeTensorInfo(v,p),k=c.texData.get(x.dataId);return k.values=y,x}let d=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Ql(t,u.shape,l.shape,n):h=new no(e,u.shape,l.shape),c.runWebGLProgram(h,[u,l],p)}}function jp(e,t=!1){if(e==="linear")return t?vX:fX;if(e==="relu")return t?wX:gX;if(e==="elu")return t?xX:mX;if(e==="relu6")return t?kX:bX;if(e==="prelu")return t?O1:F1;if(e==="leakyrelu")return t?D1:R1;if(e==="sigmoid")return t?IX:yX;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P1=class{constructor(e,t,n,s=!1,r=!1,a=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=kn(this.outputShape.length);let l=s?e[1]:e[2],c=Math.ceil(l/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";i&&(o?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${i}
         }`:u?m=`vec4 activation(vec4 a) {
@@ -1356,7 +1357,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},ow={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},uw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},iw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},ow=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1369,7 +1370,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},lw="return a * b;";function xv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new uw(ow.REAL,s.shape,r.shape),c=new uw(ow.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Er({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=WK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Zl(lw,s.shape,r.shape):i=new so(lw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var WX={kernelName:Ka,backendName:"webgl",kernelFunc:xv};function UX(e,t,n){let s=[ma(e.shape),...ga(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ma(t),...ga(t)],i=new $1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!el(r.shape,u)&&!(c.texture!==null&&el(c.shape,u))?UX(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var GX={kernelName:Eo,backendName:"webgl",kernelFunc:he},cw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
+    `}},uw="return a * b;";function vv(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),l=new ow(iw.REAL,s.shape,r.shape),c=new ow(iw.IMAG,s.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:s.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Er({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let o=n.texData.get(s.dataId),u=n.texData.get(r.dataId),[l,c]=WK(s.shape,r.shape,o.values,u.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=l,p}let i;return X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new Ql(uw,s.shape,r.shape):i=new no(uw,s.shape,r.shape),n.runWebGLProgram(i,[s,r],a)}var WX={kernelName:Ka,backendName:"webgl",kernelFunc:vv};function UX(e,t,n){let s=[ma(e.shape),...ga(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ma(t),...ga(t)],i=new $1(a,s),o=!0,u=[s],l=n.runWebGLProgram(i,[r],e.dtype,u,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}function he(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,i=n,o=w.sizeFromShape(r.shape),u=w.inferFromImplicitShape(a,o),l=w.sizeFromShape(u);w.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let c=i.texData.get(r.dataId);return c.isPacked&&!Ju(r.shape,u)&&!(c.texture!==null&&Ju(c.shape,u))?UX(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var GX={kernelName:Ao,backendName:"webgl",kernelFunc:he},lw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let c=1/t;u=`sumValue += dot(values * ${w.isInt(c)?c.toPrecision(2):c}, ones);`}let l="";r%n>0&&(l=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1514,7 +1515,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${u});
       }
-    `}};function qX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yi(e,t,n,s){let r=qX(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n==="mean"?c=i===0?new cw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new cw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new HX({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var jX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=KX(t);this.userCode=`
+    `}};function qX(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function yi(e,t,n,s){let r=qX(e.shape),a=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],c,p;n==="mean"?c=i===0?new lw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new lw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):c=new HX({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var jX=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=KX(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${r}));
@@ -1536,14 +1537,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function Xp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XX(e.shape,t):new jX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function YX(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=N.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Xp(e,u,s),o=N.getInnerMostAxes(o.length,a)),N.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=N.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=cp(e.dtype),v=yi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Yp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return YX(r,a,i,n)}var QX={kernelName:ai,backendName:"webgl",kernelFunc:Yp};function cn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=vv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Xp(r,a,i);return l}var ZX={kernelName:ci,backendName:"webgl",kernelFunc:cn},z1=1e3;function zd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=n?T.shape[1]:T.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?Kp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>z1&&q===!1){let Z=T,te=E;n&&(Z=cn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=cn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=xv({inputs:{a:ne,b:re},backend:r});K=Yp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=yn(e.dtype,t.dtype),te=new P1(k,C,[P,h,f],n,s,F,W,$,z),ee=[T,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function JX(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return zd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var e8={kernelName:na,backendName:"webgl",kernelFunc:JX},dw="return abs(x);";function t8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=C1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yr(s.shape,dw):r=new Hs(s.shape,dw),n.runWebGLProgram(r,[s],s.dtype)}var n8={kernelName:io,backendName:"webgl",kernelFunc:t8},s8=ts+`
+    `}};function Kp(e,t,n){let s=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XX(e.shape,t):new jX(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function YX(e,t,n,s){let r=t,a=e.shape.length,i=w.parseAxisParam(r,e.shape),o=i,u=N.getAxesPermutation(o,a),l=u!=null,c=e;l&&(c=Kp(e,u,s),o=N.getInnerMostAxes(o.length,a)),N.assertAxesAreInnerMostDims("sum",o,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,o),h=p;n&&(h=N.expandShapeToKeepDim(p,i));let f=w.sizeFromShape(d),g=w.sizeFromShape(e.shape)/f,b=he({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),y=lp(e.dtype),v=yi(b,y,"sum",s),x=he({inputs:{x:v},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(b),s.disposeIntermediateTensorInfo(v),l&&s.disposeIntermediateTensorInfo(c),x}function Xp(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return YX(r,a,i,n)}var QX={kernelName:ai,backendName:"webgl",kernelFunc:Xp};function cn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];let l;if(i.shouldExecuteOnCPU([r])){let p=i.texData.get(r.dataId).values,d=yv(p,r.shape,r.dtype,a,u);l=i.makeTensorInfo(u,r.dtype);let h=i.texData.get(l.dataId);h.values=d}else l=Kp(r,a,i);return l}var ZX={kernelName:ci,backendName:"webgl",kernelFunc:cn},z1=1e3;function Pd({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=he({inputs:{x:e},backend:r,attrs:{shape:k}}),E=he({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=n?T.shape[1]:T.shape[2],F=a!=null,$=i!=null,z=u==="leakyrelu",W=u!=null?jp(u,!0):null,q=F||$||z||W!=null,K;if((h===1||f===1)&&R>z1&&q===!1){let Z=T,te=E;n&&(Z=cn({inputs:{x:T},backend:r,attrs:{perm:[0,2,1]}}),A.push(Z)),s&&(te=cn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),A.push(te));let ee=f!==1,se=f===1,ne=Z;ee&&(ne=he({inputs:{x:Z},backend:r,attrs:{shape:[P,R,1]}}),A.push(ne));let oe=f===1?2:1,re=te;se&&(re=he({inputs:{x:te},backend:r,attrs:{shape:[P,1,R]}}),A.push(re));let le=vv({inputs:{a:ne,b:re},backend:r});K=Xp({inputs:{x:le},backend:r,attrs:{axis:oe,keepDims:!0}}),A.push(le)}else{let Z=yn(e.dtype,t.dtype),te=new P1(k,C,[P,h,f],n,s,F,W,$,z),ee=[T,E];if(a!=null&&ee.push(a),$&&ee.push(i),z){let se=r.makeTensorInfo([],"float32",w.createScalarValue(o,"float32"));ee.push(se),A.push(se)}K=r.runWebGLProgram(te,ee,Z)}let Y=he({inputs:{x:K},backend:r,attrs:{shape:x}});A.push(K);for(let Z of A)r.disposeIntermediateTensorInfo(Z);return Y}function JX(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Pd({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var e8={kernelName:na,backendName:"webgl",kernelFunc:JX},cw="return abs(x);";function t8(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),i=C1(a.values);return n.makeTensorInfo(s.shape,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yr(s.shape,cw):r=new Hs(s.shape,cw),n.runWebGLProgram(r,[s],s.dtype)}var n8={kernelName:ao,backendName:"webgl",kernelFunc:t8},s8=ts+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,r8=Ke({opSnippet:s8}),a8={kernelName:sl,backendName:"webgl",kernelFunc:r8},i8=ts+`
+`,r8=Ke({opSnippet:s8}),a8={kernelName:nl,backendName:"webgl",kernelFunc:r8},i8=ts+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:rl,backendName:"webgl",kernelFunc:o8},pw="return a + b;",l8=qt({opSnippet:pw,packedOpSnippet:pw,supportsComplex:!0,cpuKernelImpl:IK}),c8={kernelName:kr,backendName:"webgl",kernelFunc:l8},d8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:sl,backendName:"webgl",kernelFunc:o8},dw="return a + b;",l8=qt({opSnippet:dw,packedOpSnippet:dw,supportsComplex:!0,cpuKernelImpl:IK}),c8={kernelName:kr,backendName:"webgl",kernelFunc:l8},d8=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1559,7 +1560,7 @@ return log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:rl,back
         vec4 result = ${s};
         setOutput(result);
       }
-    `}};function ld(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return En({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ld({inputs:s.slice(0,u),backend:n}),c=ld({inputs:s.slice(u),backend:n});return ld({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>yn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new p8(s[0].shape,a):new d8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var h8={kernelName:xa,backendName:"webgl",kernelFunc:ld};function f8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims("all",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var m8={kernelName:al,backendName:"webgl",kernelFunc:f8};function g8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims("any",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var b8={kernelName:il,backendName:"webgl",kernelFunc:g8},y8=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function ud(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return En({inputs:{x:s[0]},backend:n});if(s.length>X().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(s.length/2),l=ud({inputs:s.slice(0,u),backend:n}),c=ud({inputs:s.slice(u),backend:n});return ud({inputs:[l,c],backend:n})}let r=s.map(u=>u.dtype).reduce((u,l)=>yn(u,l)),a=s.map(u=>u.shape),o=X().getBool("WEBGL_PACK")?new p8(s[0].shape,a):new d8(s[0].shape,a);return n.runWebGLProgram(o,s,r)}var h8={kernelName:xa,backendName:"webgl",kernelFunc:ud};function f8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims("all",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"all",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var m8={kernelName:rl,backendName:"webgl",kernelFunc:f8};function g8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,o)),N.assertAxesAreInnerMostDims("any",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"any",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var b8={kernelName:al,backendName:"webgl",kernelFunc:g8},y8=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1641,23 +1642,23 @@ return log(x + sqrt(x * x - 1.0));`,o8=Ke({opSnippet:i8}),u8={kernelName:rl,back
         }
         setOutput(bestIndex);
       }
-    `}};function M1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=N.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new y8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=M1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function L1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=N.computeOptimalWindowSize(a),o=new v8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=L1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function B1(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=N.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=M1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return L1(e,t,s)}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=B1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var w8={kernelName:wa,backendName:"webgl",kernelFunc:x8};function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=B1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var I8={kernelName:ol,backendName:"webgl",kernelFunc:k8},S8=ts+`
+    `}};function M1(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let i=N.computeOptimalWindowSize(a),o={windowSize:i,inSize:a,batchSize:r,outSize:Math.ceil(a/i)},u=new y8(o,n,s==null),l=[t];s!=null&&l.push(s);let c=e.runWebGLProgram(u,l,"int32");if(c.shape[1]===1)return c;let p=M1(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function L1(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],i=N.computeOptimalWindowSize(a),o=new v8(r,i,n,s==null),u=s==null?[t]:[t,s],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let c=L1(e,t,n,l);return e.disposeIntermediateTensorInfo(l),c}return l}function B1(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!X().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],i=e.texData.get(t.dataId),o=i!==null&&i.isPacked,u=t;o&&(u=e.unpackTensor(t),a.push(u));let[l,c]=N.computeOutAndReduceShapes(u.shape,r),p=w.sizeFromShape(c),d=he({inputs:{x:u},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=M1(e,d,s);a.push(h);let f=he({inputs:{x:h},backend:e,attrs:{shape:l}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return L1(e,t,s)}function x8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=B1(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var w8={kernelName:wa,backendName:"webgl",kernelFunc:x8};function k8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=cn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=B1(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var I8={kernelName:il,backendName:"webgl",kernelFunc:k8},S8=ts+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,C8=Ke({opSnippet:S8}),N8={kernelName:ul,backendName:"webgl",kernelFunc:C8},T8=ts+"return log(x + sqrt(x * x + 1.0));",$8=Ke({opSnippet:T8}),_8={kernelName:ll,backendName:"webgl",kernelFunc:$8},A8=ts+`
+`,C8=Ke({opSnippet:S8}),N8={kernelName:ol,backendName:"webgl",kernelFunc:C8},T8=ts+"return log(x + sqrt(x * x + 1.0));",$8=Ke({opSnippet:T8}),_8={kernelName:ul,backendName:"webgl",kernelFunc:$8},A8=ts+`
   return atan(x);
-`,E8=Ke({opSnippet:A8}),R8={kernelName:cl,backendName:"webgl",kernelFunc:E8},D8=BX+`
+`,E8=Ke({opSnippet:A8}),R8={kernelName:ll,backendName:"webgl",kernelFunc:E8},D8=BX+`
   return atan(a, b);
 `,F8=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+VX+`
   return result;
-`,O8=qt({opSnippet:D8,packedOpSnippet:F8}),P8={kernelName:pl,backendName:"webgl",kernelFunc:O8},z8=ts+`
+`,O8=qt({opSnippet:D8,packedOpSnippet:F8}),P8={kernelName:dl,backendName:"webgl",kernelFunc:O8},z8=ts+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelName:dl,backendName:"webgl",kernelFunc:M8},tl=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let T=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelName:cl,backendName:"webgl",kernelFunc:M8},el=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(f||(b="-1.0 / 1e-20"),n){let T=">=";this.userCode=`
         const ivec2 strides = ivec2(${i}, ${o});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1798,7 +1799,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(${v});
       }
-    `}},wv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let A=">=";this.userCode=`
+    `}},xv=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let y=t==="avg",v="0.0";if(y||(v="-1.0 / 1e-20"),n){let A=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${i}, ${o}, ${u});
         const ivec3 pads = ivec3(${m}, ${g}, ${b});
@@ -1961,7 +1962,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
           setOutput(${k});
         }
       }
-    `}};function B8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new tl(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var V8={kernelName:ka,backendName:"webgl",kernelFunc:B8};function W8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new wv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var U8={kernelName:qd,backendName:"webgl",kernelFunc:W8},G8=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
+    `}};function B8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,"avgPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new el(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var V8={kernelName:ka,backendName:"webgl",kernelFunc:B8};function W8(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,u,l),d=new xv(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var U8={kernelName:Hd,backendName:"webgl",kernelFunc:W8},G8=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${l}, ${c});
       const float avgMultiplier = float(${p});
 
@@ -2059,7 +2060,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function q8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new H8(d);return n.runWebGLProgram(h,[r],i.dtype)}var j8={kernelName:ig,backendName:"webgl",kernelFunc:q8};function K8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;nu([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=new G8(c);return n.runWebGLProgram(p,[r],i.dtype)}var X8={kernelName:ag,backendName:"webgl",kernelFunc:K8};function Y8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return zd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Q8={kernelName:Ia,backendName:"webgl",kernelFunc:Y8},Z8=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function q8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new H8(d);return n.runWebGLProgram(h,[r],i.dtype)}var j8={kernelName:ag,backendName:"webgl",kernelFunc:q8};function K8(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a;tu([r,a],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=s,c=N.computePool2DInfo(i.shape,o,u,1,l),p=new G8(c);return n.runWebGLProgram(p,[r],i.dtype)}var X8={kernelName:rg,backendName:"webgl",kernelFunc:K8};function Y8(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Pd({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var Q8={kernelName:Ia,backendName:"webgl",kernelFunc:Y8},Z8=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let i="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2082,7 +2083,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},eY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=X().getBool("WEBGL_PACK_NORMALIZATION")?new J8(s.shape,r.shape,a.shape,c,p,u):new Z8(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},tY={kernelName:Pa,backendName:"webgl",kernelFunc:eY},nY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=sY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Um[i]} = start[${i}] + coords.${Um[i]};`);s=`
+    `}},eY=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:i,scale:o}=e;w.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),w.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),w.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[s,r,a],c=null;i!=null&&(c=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let d=X().getBool("WEBGL_PACK_NORMALIZATION")?new J8(s.shape,r.shape,a.shape,c,p,u):new Z8(s.shape,r.shape,a.shape,c,p,u);return t.runWebGLProgram(d,l,l[0].dtype)},tY={kernelName:Pa,backendName:"webgl",kernelFunc:eY},nY=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ot(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=sY(this.rank),s,r=e.map((a,i)=>`sourceLoc.${Wm[i]} = start[${i}] + coords.${Wm[i]};`);s=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${r.join(`
@@ -2092,7 +2093,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         ${s}
         setOutput(getSource(${n}));
       }
-    `}},Um=["x","y","z","w","u","v"];function sY(e){if(e===1)return"sourceLoc";if(e<=6)return Um.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ot(this.rank),n=un("coords",this.rank),s=un("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
+    `}},Wm=["x","y","z","w","u","v"];function sY(e){if(e===1)return"sourceLoc";if(e<=6)return Wm.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var rY=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=ot(this.rank),n=un("coords",this.rank),s=un("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,i=`
       result.x = ${a};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${s[this.rank-1]};
@@ -2121,7 +2122,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         ${o}
         setOutput(result);
       }
-    `}};function aY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=wt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function uu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=XK(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rY(u):new nY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),aY(r,o,u,n)}var iY={kernelName:Po,backendName:"webgl",kernelFunc:uu},oY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=uu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},uY={kernelName:oo,backendName:"webgl",kernelFunc:oY};function lY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=S1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var cY={kernelName:og,backendName:"webgl",kernelFunc:lY};function dY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var pY={kernelName:ug,backendName:"webgl",kernelFunc:dY},hY="return float(a != b);",V1=qt({opSnippet:hY,cpuKernelImpl:GK,dtype:"bool"}),fY={kernelName:So,backendName:"webgl",kernelFunc:V1};function Jl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return En({inputs:{x:r.complexTensorInfos.real},backend:n})}var mY={kernelName:np,backendName:"webgl",kernelFunc:Jl},gY="return float(int(x));";function bY(e,t){let n=new Hs(e.shape,gY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Gm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return En({inputs:{x:r},backend:n});let i=$t(r.shape),o=Gm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Er({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Jl({inputs:{input:r},backend:n}),o=Gm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=En({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return bY(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=V1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var yY={kernelName:Sa,backendName:"webgl",kernelFunc:Gm},hw="return ceil(x);",vY=Ke({opSnippet:hw,packedOpSnippet:hw,cpuKernelImpl:CK}),xY={kernelName:Ca,backendName:"webgl",kernelFunc:vY},wY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function aY(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),i=s.texData.get(a.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=wt.computeFlatOffset(t,w.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=s.dataRefCount.get(i.slice.origDataId)||1;return s.dataRefCount.set(i.slice.origDataId,u+1),a}function ou(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=XK(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}let{isPacked:l}=n.texData.get(r.dataId),c=wt.isSliceContinous(r.shape,o,u);if(l||!c){let p=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new rY(u):new nY(u),d=[o];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),aY(r,o,u,n)}var iY={kernelName:Oo,backendName:"webgl",kernelFunc:ou},oY=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=he({inputs:{x:r},backend:n,attrs:{shape:u}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:l}}),g=he({inputs:{x:m},backend:n,attrs:{shape:c}}),b=ou({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeIntermediateTensorInfo(y)),b},uY={kernelName:io,backendName:"webgl",kernelFunc:oY};function lY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i}=s,o=n.readSync(r.dataId),u=n.readSync(a.dataId),l=S1(o,u,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,l)}var cY={kernelName:ig,backendName:"webgl",kernelFunc:lY};function dY(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),i=n.readSync(r.dataId),o=N.assertAndGetBroadcastShape(Array.from(a),Array.from(i));return n.makeTensorInfo([o.length],"int32",Int32Array.from(o))}var pY={kernelName:og,backendName:"webgl",kernelFunc:dY},hY="return float(a != b);",V1=qt({opSnippet:hY,cpuKernelImpl:GK,dtype:"bool"}),fY={kernelName:Io,backendName:"webgl",kernelFunc:V1};function Zl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return En({inputs:{x:r.complexTensorInfos.real},backend:n})}var mY={kernelName:tp,backendName:"webgl",kernelFunc:Zl},gY="return float(int(x));";function bY(e,t){let n=new Hs(e.shape,gY),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Um(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return En({inputs:{x:r},backend:n});let i=$t(r.shape),o=Um({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Er({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Zl({inputs:{input:r},backend:n}),o=Um({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(i),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=En({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return bY(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=V1({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var yY={kernelName:Sa,backendName:"webgl",kernelFunc:Um},pw="return ceil(x);",vY=Ke({opSnippet:pw,packedOpSnippet:pw,cpuKernelImpl:CK}),xY={kernelName:Ca,backendName:"webgl",kernelFunc:vY},wY=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
         float value = getAAtOutCoords();
@@ -2156,7 +2157,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function fw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function NY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new CY(s.shape),i=[fw(s,r.complexTensorInfos.real),fw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var TY={kernelName:Kd,backendName:"webgl",kernelFunc:NY},$Y=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
+    `}};function hw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function NY(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new CY(s.shape),i=[hw(s,r.complexTensorInfos.real),hw(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,i,i[0].dtype)}var TY={kernelName:jd,backendName:"webgl",kernelFunc:NY},$Y=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2171,12 +2172,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }`;for(let f=1;f<o.length;f++){let m=o[f-1];p+=`
         if (${u} < ${o[f]}  && ${u} >= ${o[f-1]}) {
           return getChannel(
-            getT${f}(${Jc(i,u,m)}),
-            vec2(${Jc(l,u,m)}));
+            getT${f}(${Zc(i,u,m)}),
+            vec2(${Zc(l,u,m)}));
         }`}let d=o.length,h=o[o.length-1];p+=`
         return getChannel(
-          getT${d}(${Jc(i,u,h)}),
-          vec2(${Jc(l,u,h)}));`,this.userCode=`
+          getT${d}(${Zc(i,u,h)}),
+          vec2(${Zc(l,u,h)}));`,this.userCode=`
       float getValue(${i.map(f=>"int "+f)}) {
         ${p}
       }
@@ -2202,7 +2203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(result);
       }
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@@ -2471,7 +2472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
 
         ${s.output} = result;
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+    `}};function G1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=s.texData.get(e.dataId),c=n.inChannels,p=u[0]*u[1]*u[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,b=[];if(!((p===1||d===1)&&c>z1)&&l.isPacked&&h&&l.texture!=null&&u[2]%2!==0&&w.arraysEqual(l.shape.slice(-3),u.slice(-3))){let x=u[0]*u[1]*(u[2]+1),k={dataId:e.dataId,shape:[1,x,n.inChannels],dtype:e.dtype},C=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,w.assert(Ju(l.shape,k.shape),()=>`packed reshape ${l.shape} to ${k.shape} isn't free`);let T=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(T);let E=Pd({a:k,b:T,backend:s,transposeA:f,transposeB:m,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),A=s.texData.get(E.dataId);w.assert(A.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=C,A.shape=n.outShape,g=En({inputs:{x:E},backend:s}),g.shape=n.outShape,b.push(E)}else{let x=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],k=he({inputs:{x:e},backend:s,attrs:{shape:[1,x,n.inChannels]}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),T=Pd({a:k,b:C,transposeA:f,transposeB:m,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});g=he({inputs:{x:T},backend:s,attrs:{shape:n.outShape}}),b.push(k),b.push(C),b.push(T)}for(let x of b)s.disposeIntermediateTensorInfo(x);return g}function H1({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=u*l*c,g=d*p,b=[m,g],y=!0,v=!1,x=[],k=he({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),C=he({inputs:{x:t},backend:s,attrs:{shape:[1,m,w.sizeFromShape(t.shape)/m]}});x.push(k),x.push(C);let T=new FY(b,n),E=[k.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],A=s.runWebGLProgram(T,[k],"float32",E),P=he({inputs:{x:A},backend:s,attrs:{shape:[1,b[0],b[1]]}});x.push(A),x.push(P);let R=r!=null,F=a!=null,$=o==="leakyrelu",z=o?jp(o,!0):null,W=new P1(P.shape,C.shape,[1,g,n.outChannels],y,v,R,z,F,$),q=[P,C];if(r&&q.push(r),F&&q.push(a),$){let te=s.makeTensorInfo([],"float32",w.createScalarValue(i,"float32"));q.push(te),x.push(te)}let K=s.runWebGLProgram(W,q,"float32"),Y=f?[1,d,p,n.outChannels]:[1,n.outChannels,d,p],Z=he({inputs:{x:K},backend:s,attrs:{shape:Y}});x.push(K);for(let te of x)s.disposeIntermediateTensorInfo(te);return Z}function OY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=G1({x:r,filter:a,convInfo:d,backend:n});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=H1({x:r,filter:a,convInfo:d,backend:n});else{let m=new U1(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=he({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var PY={kernelName:Na,backendName:"webgl",kernelFunc:OY},zY=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2665,12 +2666,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new zY(d);return n.runWebGLProgram(h,[r,a],"float32")}var WY={kernelName:lg,backendName:"webgl",kernelFunc:VY};function UY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new MY(d);return n.runWebGLProgram(h,[r,a],"float32")}var GY={kernelName:Ta,backendName:"webgl",kernelFunc:UY};function HY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new DY(l);return n.runWebGLProgram(c,[r,a],"float32")}var qY={kernelName:Xd,backendName:"webgl",kernelFunc:HY};function jY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=N.computeConv3DInfo(r.shape,u,i,1,o),c=new LY(l);return n.runWebGLProgram(c,[r,a],"float32")}var KY={kernelName:cg,backendName:"webgl",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=N.computeConv3DInfo(u,a.shape,o,1,i),c=new BY(l);return n.runWebGLProgram(c,[r,a],"float32")}var YY={kernelName:dg,backendName:"webgl",kernelFunc:XY},QY=ou+`
+    `}};function VY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:c}=s,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,c,i,1,o,l,!1,p),h=new zY(d);return n.runWebGLProgram(h,[r,a],"float32")}var WY={kernelName:ug,backendName:"webgl",kernelFunc:VY};function UY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:c}=s,p=N.convertConv2DDataFormat(l),d=N.computeConv2DInfo(i,a.shape,o,1,u,c,!1,p),h=new MY(d);return n.runWebGLProgram(h,[r,a],"float32")}var GY={kernelName:Ta,backendName:"webgl",kernelFunc:UY};function HY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeConv3DInfo(r.shape,a.shape,i,u,o),c=new DY(l);return n.runWebGLProgram(c,[r,a],"float32")}var qY={kernelName:Kd,backendName:"webgl",kernelFunc:HY};function jY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,pad:o,filterShape:u}=s,l=N.computeConv3DInfo(r.shape,u,i,1,o),c=new LY(l);return n.runWebGLProgram(c,[r,a],"float32")}var KY={kernelName:lg,backendName:"webgl",kernelFunc:jY};function XY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:i,strides:o,inputShape:u}=s,l=N.computeConv3DInfo(u,a.shape,o,1,i),c=new BY(l);return n.runWebGLProgram(c,[r,a],"float32")}var YY={kernelName:cg,backendName:"webgl",kernelFunc:XY},QY=iu+`
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-`,ZY=Ke({opSnippet:QY}),JY={kernelName:$a,backendName:"webgl",kernelFunc:ZY},e7=`
+`,ZY=Ke({opSnippet:QY}),JY={kernelName:$a,backendName:"webgl",kernelFunc:ZY},e9=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,t7=Ke({opSnippet:e7}),n7={kernelName:_a,backendName:"webgl",kernelFunc:t7},s7=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,t9=Ke({opSnippet:e9}),n9={kernelName:_a,backendName:"webgl",kernelFunc:t9},s9=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,o,u]=e,[l]=t,[c,p]=n;this.outputShape=[l,c,p,u];let d=s==="bilinear"?1:0,[h,f]=[`${i-1}.0`,`${o-1}.0`],[m,g,b]=c>1?[`${(i-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[y,v,x]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${y});
       void main() {
@@ -2731,20 +2732,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
           setOutput(newValue);
         }
       }
-    `}},r7=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new s7(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},a7={kernelName:co,backendName:"webgl",kernelFunc:r7},mw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${gw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
+    `}},r9=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new s9(r.shape,a.shape,o,u,l);return n.runWebGLProgram(c,[r,a,i],"float32")},a9={kernelName:lo,backendName:"webgl",kernelFunc:r9},fw=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${mw(s,"coords")})`,a=e[e.length-1],i="",o="";t?(i=n?`end != ${a-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${a}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
         ${ot(s)} coords = getOutputCoords();
-        int end = ${bw(s,"coords")};
+        int end = ${gw(s,"coords")};
         float val = ${r};
         int pow2 = int(pow(2.0, index));
         if (${i}) {
           int idx = ${o};
-          ${bw(s,"coords")} = idx;
-          val += getX(${gw(s,"coords")});
+          ${gw(s,"coords")} = idx;
+          val += getX(${mw(s,"coords")});
         }
         setOutput(val);
       }
-    `}};function gw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function bw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function i7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=N.getAxesPermutation([a],u),c=r;l!=null&&(c=cn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=En({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new mw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new mw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=cn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var o7={kernelName:lo,backendName:"webgl",kernelFunc:i7};function u7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=S1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=SK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var l7={kernelName:pg,backendName:"webgl",kernelFunc:u7},c7=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}};function mw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function gw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function i9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length,l=N.getAxesPermutation([a],u),c=r;l!=null&&(c=cn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=N.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let d=c.shape[p],h=En({inputs:{x:c},backend:n});for(let f=0;f<=Math.ceil(Math.log2(d))-1;f++){let m=new fw(c.shape,!1,o),g=[[f]],b=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(b)}if(i){let f=new fw(c.shape,i,o),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(l!=null){let f=N.getUndoAxesPermutation(l),m=cn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(c),m}return h}var o9={kernelName:uo,backendName:"webgl",kernelFunc:i9};function u9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:i,binaryOutput:o}=s;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(a.dataId),c=S1(u,l,a.dtype,a.shape,i);return n.makeTensorInfo([i],a.dtype,c)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(a),c=SK(u,l,i,o);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var l9={kernelName:dg,backendName:"webgl",kernelFunc:u9},c9=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2763,7 +2764,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function d7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new c7(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var p7={kernelName:po,backendName:"webgl",kernelFunc:d7},q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function d9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=new c9(f,a,i);return n.runWebGLProgram(m,[r],r.dtype)}var p9={kernelName:co,backendName:"webgl",kernelFunc:d9},q1=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=kn(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,o=e.outChannels/e.inChannels,u="",l="";n&&(s?u=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?u=`float activation(float a) {
@@ -3003,7 +3004,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         ${f}
         setOutput(result);
       }
-    `}};function h7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new j1(p):d=new q1(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var f7={kernelName:Aa,backendName:"webgl",kernelFunc:h7},m7=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+    `}};function h9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(i,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d;X().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new j1(p):d=new q1(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var f9={kernelName:Aa,backendName:"webgl",kernelFunc:h9},m9=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3038,7 +3039,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},g7=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
+    `}},g9=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${a}, ${i});
 
       void main() {
@@ -3083,13 +3084,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function b7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new m7(p);return n.runWebGLProgram(d,[r,a],"float32")}var y7={kernelName:hg,backendName:"webgl",kernelFunc:b7};function v7(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new g7(p);return n.runWebGLProgram(d,[r,a],"float32")}var x7={kernelName:fg,backendName:"webgl",kernelFunc:v7},w7=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function b9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:c}=s,p=N.computeConv2DInfo(r.shape,c,i,o,u,l,!0),d=new m9(p);return n.runWebGLProgram(d,[r,a],"float32")}var y9={kernelName:pg,backendName:"webgl",kernelFunc:b9};function v9(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:c}=s,p=N.computeConv2DInfo(c,a.shape,i,o,u,l,!0),d=new g9(p);return n.runWebGLProgram(d,[r,a],"float32")}var x9={kernelName:hg,backendName:"webgl",kernelFunc:v9},w9=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function k7(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new w7(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var I7={kernelName:mg,backendName:"webgl",kernelFunc:k7},S7=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
+    `}};function k9(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=w.sizeFromShape(s.shape),i=he({inputs:{x:s},backend:n,attrs:{shape:[a]}}),o=new w9(a),u=n.runWebGLProgram(o,[i],i.dtype),l=he({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var I9={kernelName:fg,backendName:"webgl",kernelFunc:k9},S9=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:c,left:p}=s;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${a});
       const ivec2 pads = ivec2(${c}, ${p});
       const float neg_infinity = -3.4e38;
@@ -3127,7 +3128,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         float result = curVal;
         setOutput(result);
       }
-    `}};function C7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),c,p=new S7(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var N7={kernelName:Yd,backendName:"webgl",kernelFunc:C7};function T7(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=cn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=xv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Yp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var $7={kernelName:Qd,backendName:"webgl",kernelFunc:T7},_7="return (x >= 0.0) ? x : (exp(x) - 1.0);",A7=`
+    `}};function C9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u}=s,l=N.computeDilation2DInfo(r.shape,a.shape,i,o,"NHWC",u),c,p=new S9(l);c=n.runWebGLProgram(p,[r,a],"float32");let d=he({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),d}var N9={kernelName:Xd,backendName:"webgl",kernelFunc:C9};function T9(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=cn({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=he({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=vv({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Xp({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var $9={kernelName:Yd,backendName:"webgl",kernelFunc:T9},_9="return (x >= 0.0) ? x : (exp(x) - 1.0);",A9=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3136,12 +3137,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,E7=Ke({opSnippet:_7,packedOpSnippet:A7}),R7={kernelName:Ra,backendName:"webgl",kernelFunc:E7},D7="return (b >= 1.0) ? a : a * (b + 1.0);",F7=`
+`,E9=Ke({opSnippet:_9,packedOpSnippet:A9}),R9={kernelName:Ra,backendName:"webgl",kernelFunc:E9},D9="return (b >= 1.0) ? a : a * (b + 1.0);",F9=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,O7=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Zl(F7,s.shape,r.shape):new so(D7,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},P7={kernelName:gg,backendName:"webgl",kernelFunc:O7},z7=`
+`,O9=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=X().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ql(F9,s.shape,r.shape):new no(D9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},P9={kernelName:mg,backendName:"webgl",kernelFunc:O9},z9=`
   return vec4(equal(a, b));
-`,M7="return float(a == b);",L7=qt({opSnippet:M7,packedOpSnippet:z7,dtype:"bool",cpuKernelImpl:TK}),B7={kernelName:ho,backendName:"webgl",kernelFunc:L7},V7=`
+`,M9="return float(a == b);",L9=qt({opSnippet:M9,packedOpSnippet:z9,dtype:"bool",cpuKernelImpl:TK}),B9={kernelName:po,backendName:"webgl",kernelFunc:L9},V9=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3156,9 +3157,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,W7=Ke({opSnippet:V7}),U7={kernelName:hl,backendName:"webgl",kernelFunc:W7},G7=ou+`
+`,W9=Ke({opSnippet:V9}),U9={kernelName:pl,backendName:"webgl",kernelFunc:W9},G9=iu+`
   return exp(x);
-`,H7=`
+`,H9=`
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -3167,7 +3168,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,K1=Ke({opSnippet:G7,packedOpSnippet:H7,cpuKernelImpl:$K,dtype:"float32"}),q7={kernelName:Da,backendName:"webgl",kernelFunc:K1};function Hm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var j7={kernelName:fo,backendName:"webgl",kernelFunc:Hm},yw="return exp(x) - 1.0;",K7=Ke({opSnippet:yw,packedOpSnippet:yw,cpuKernelImpl:_K}),X7={kernelName:mo,backendName:"webgl",kernelFunc:K7},vw=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,K1=Ke({opSnippet:G9,packedOpSnippet:H9,cpuKernelImpl:$K,dtype:"float32"}),q9={kernelName:Da,backendName:"webgl",kernelFunc:K1};function Gm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),he({inputs:{x:a},backend:s,attrs:{shape:o}})}var j9={kernelName:ho,backendName:"webgl",kernelFunc:Gm},bw="return exp(x) - 1.0;",K9=Ke({opSnippet:bw,packedOpSnippet:bw,cpuKernelImpl:_K}),X9={kernelName:fo,backendName:"webgl",kernelFunc:K9},yw=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3200,12 +3201,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function X1(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new vw("real",u,t),c=new vw("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Er({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Y7(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!1,n)}var Q7={kernelName:bg,backendName:"webgl",kernelFunc:Y7},Z7=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function X1(e,t,n){let s=n.texData.get(e.dataId),r=w.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],i=r/a,o=he({inputs:{x:e},backend:n,attrs:{shape:[i,a]}}),u=o.shape,l=new yw("real",u,t),c=new yw("imag",u,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:u},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:u}],d=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=Er({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=he({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}function Y9(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!1,n)}var Q9={kernelName:gg,backendName:"webgl",kernelFunc:Y9},Z9=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function ec(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Z7(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var J7={kernelName:fl,backendName:"webgl",kernelFunc:ec},e9=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function Jl(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new Z9(s,r),o=[[r]];return t.runWebGLProgram(i,[],a,o)}}var J9={kernelName:hl,backendName:"webgl",kernelFunc:Jl},eQ=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3219,7 +3220,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
           }
           setOutput(outputValue);
         }
-    `}},t9={kernelName:go,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new e9(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},xw="return floor(x);",n9=Ke({opSnippet:xw,packedOpSnippet:xw,cpuKernelImpl:AK}),s9={kernelName:Fa,backendName:"webgl",kernelFunc:n9},r9=`
+    `}},tQ={kernelName:mo,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new eQ(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},vw="return floor(x);",nQ=Ke({opSnippet:vw,packedOpSnippet:vw,cpuKernelImpl:AK}),sQ={kernelName:Fa,backendName:"webgl",kernelFunc:nQ},rQ=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3229,7 +3230,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
   } else {
     return NAN;
   }
-`,a9=`
+`,aQ=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -3250,7 +3251,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,i9=qt({opSnippet:r9,packedOpSnippet:a9,dtype:"int32"}),o9={kernelName:Oa,backendName:"webgl",kernelFunc:i9},u9=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,s]=e;this.outputShape=e,this.userCode=`
+`,iQ=qt({opSnippet:rQ,packedOpSnippet:aQ,dtype:"int32"}),oQ={kernelName:Oa,backendName:"webgl",kernelFunc:iQ},uQ=class{constructor(e){this.variableNames=["A"];let t=hn(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3272,7 +3273,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},l9=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,s]=e;this.outputShape=e,this.userCode=`
+    `}},lQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hn(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3306,7 +3307,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
 
         ${t.output} = result;
       }
-    `}},c9={kernelName:fd,backendName:"webgl",kernelFunc:d9},zi;function d9(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(o||i)&&(zi==null&&(zi=document.createElement("canvas").getContext("2d")),zi.canvas.width=u,zi.canvas.height=l,zi.drawImage(r,0,0,u,l),r=zi.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=X().getBool("WEBGL_PACK")?new l9(p):new u9(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function p9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=G1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)b=H1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,C=h==="leakyrelu",T=h?Kp(h,!1):null,E=new U1(g,x,T,k,C),A=[r,a];if(i&&A.push(i),o&&A.push(o),C){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));A.push(P),y.push(P)}b=n.runWebGLProgram(E,A,"float32")}let v=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var h9={kernelName:sa,backendName:"webgl",kernelFunc:p9};function f9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?Kp(d,b):null,v=[r,a],x=i!=null,k=o!=null,C=d==="leakyrelu";if(x&&v.push(i),k&&v.push(o),C){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let T;b?T=new j1(g,x,y,k,C):T=new q1(g,x,y,k,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],A=n.runWebGLProgram(T,v,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),A}var m9={kernelName:ra,backendName:"webgl",kernelFunc:f9},g9=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ot(t.length),r=ot(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
+    `}},cQ={kernelName:hd,backendName:"webgl",kernelFunc:dQ},zi;function dQ(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[l,u],p=[l,u,a];(o||i)&&(zi==null&&(zi=document.createElement("canvas").getContext("2d")),zi.canvas.width=u,zi.canvas.height=l,zi.drawImage(r,0,0,u,l),r=zi.canvas);let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=2,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=X().getBool("WEBGL_PACK")?new lQ(p):new uQ(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function pQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(r.shape,a.shape,u,p,l,d,!1,m),b,y=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))b=G1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else if(X().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)b=H1({x:r,filter:a,convInfo:g,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:f});else{let x=i!=null,k=o!=null,C=h==="leakyrelu",T=h?jp(h,!1):null,E=new U1(g,x,T,k,C),A=[r,a];if(i&&A.push(i),o&&A.push(o),C){let P=n.makeTensorInfo([],"float32",w.createScalarValue(f,"float32"));A.push(P),y.push(P)}b=n.runWebGLProgram(E,A,"float32")}let v=he({inputs:{x:b},backend:n,attrs:{shape:g.outShape}});return y.push(b),y.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var hQ={kernelName:sa,backendName:"webgl",kernelFunc:pQ};function fQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),w.assert(N.eitherStridesOrDilationsAreOne(u,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,u,m,l,p,!0),b=X().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,y=d?jp(d,b):null,v=[r,a],x=i!=null,k=o!=null,C=d==="leakyrelu";if(x&&v.push(i),k&&v.push(o),C){let P=n.makeTensorInfo([],"float32",w.createScalarValue(h,"float32"));v.push(P),f.push(P)}let T;b?T=new j1(g,x,y,k,C):T=new q1(g,x,y,k,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],A=n.runWebGLProgram(T,v,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),A}var mQ={kernelName:ra,backendName:"webgl",kernelFunc:fQ},gQ=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=ot(t.length),r=ot(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
         ${s} strides = ${s}(${this.strides});
          void main() {
           ${r} coords = getOutputCoords();
@@ -3317,24 +3318,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
           }
           setOutput(getX(flattenIndex, coords[1]));
         }
-      `}};function b9(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=EK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new g9(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var y9={kernelName:yo,backendName:"webgl",kernelFunc:b9},v9=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),s=x9(e,2);this.userCode=`
+      `}};function bQ(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=he({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=he({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let b=n.readSync(r.dataId),y=n.bufferSync(s),v=EK(b,y,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,v.values)}let f=new gQ(i,p,[l,c]),m=n.runWebGLProgram(f,[h,d],h.dtype),g=he({inputs:{x:m},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var yQ={kernelName:bo,backendName:"webgl",kernelFunc:bQ},vQ=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ot(this.rank),s=xQ(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${s}));
       }
-    `}};function x9(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=RK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new v9(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var w9={kernelName:bo,backendName:"webgl",kernelFunc:Y1},k9="return float(a > b);",I9=`
+    `}};function xQ(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function Y1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0];if(X().get("DEBUG")){let y=n.readSync(a.dataId),v=r.shape[u];for(let x=0;x<y.length;++x){let k=y[x];w.assert(k<=v-1&&k>=0,()=>`GatherV2: the index value ${k} is not in [0, ${v-1}]`)}}let l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=he({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=he({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let y=n.bufferSync(h),v=n.bufferSync(d),x=RK(v,y,f);return p.forEach(k=>n.disposeIntermediateTensorInfo(k)),n.makeTensorInfo(l.outputShape,x.dtype,x.values)}let m=new vQ(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let b=he({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeIntermediateTensorInfo(y)),b}var wQ={kernelName:go,backendName:"webgl",kernelFunc:Y1},kQ="return float(a > b);",IQ=`
   return vec4(greaterThan(a, b));
-`,S9=qt({opSnippet:k9,packedOpSnippet:I9,cpuKernelImpl:DK,dtype:"bool"}),C9={kernelName:vo,backendName:"webgl",kernelFunc:S9},N9="return float(a >= b);",T9=`
+`,SQ=qt({opSnippet:kQ,packedOpSnippet:IQ,cpuKernelImpl:DK,dtype:"bool"}),CQ={kernelName:yo,backendName:"webgl",kernelFunc:SQ},NQ="return float(a >= b);",TQ=`
   return vec4(greaterThanEqual(a, b));
-`,$9=qt({opSnippet:N9,packedOpSnippet:T9,dtype:"bool",cpuKernelImpl:FK}),_9={kernelName:za,backendName:"webgl",kernelFunc:$9};function A9(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!0,n)}var E9={kernelName:yg,backendName:"webgl",kernelFunc:A9},R9="return float(!isnan(x) && !isinf(x));",D9=Ke({opSnippet:R9,dtype:"bool"}),F9={kernelName:ml,backendName:"webgl",kernelFunc:D9},O9="return float(isinf(x));",P9=Ke({opSnippet:O9,dtype:"bool"}),z9={kernelName:gl,backendName:"webgl",kernelFunc:P9},M9="return float(isnan(x));",L9=Ke({opSnippet:M9,dtype:"bool"}),B9={kernelName:bl,backendName:"webgl",kernelFunc:L9},V9="return float(a < b);",W9=`
+`,$Q=qt({opSnippet:NQ,packedOpSnippet:TQ,dtype:"bool",cpuKernelImpl:FK}),_Q={kernelName:za,backendName:"webgl",kernelFunc:$Q};function AQ(e){let{inputs:t,backend:n}=e,{input:s}=t;return X1(s,!0,n)}var EQ={kernelName:bg,backendName:"webgl",kernelFunc:AQ},RQ="return float(!isnan(x) && !isinf(x));",DQ=Ke({opSnippet:RQ,dtype:"bool"}),FQ={kernelName:fl,backendName:"webgl",kernelFunc:DQ},OQ="return float(isinf(x));",PQ=Ke({opSnippet:OQ,dtype:"bool"}),zQ={kernelName:ml,backendName:"webgl",kernelFunc:PQ},MQ="return float(isnan(x));",LQ=Ke({opSnippet:MQ,dtype:"bool"}),BQ={kernelName:gl,backendName:"webgl",kernelFunc:LQ},VQ="return float(a < b);",WQ=`
   return vec4(lessThan(a, b));
-`,U9=qt({opSnippet:V9,packedOpSnippet:W9,cpuKernelImpl:OK,dtype:"bool"}),G9={kernelName:xo,backendName:"webgl",kernelFunc:U9},H9="return float(a <= b);",q9=`
+`,UQ=qt({opSnippet:VQ,packedOpSnippet:WQ,cpuKernelImpl:OK,dtype:"bool"}),GQ={kernelName:vo,backendName:"webgl",kernelFunc:UQ},HQ="return float(a <= b);",qQ=`
   return vec4(lessThanEqual(a, b));
-`,j9=qt({opSnippet:H9,packedOpSnippet:q9,cpuKernelImpl:PK,dtype:"bool"}),K9={kernelName:wo,backendName:"webgl",kernelFunc:j9};function X9(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=zK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var Y9={kernelName:vg,backendName:"webgl",kernelFunc:X9},Q9=ou+`
+`,jQ=qt({opSnippet:HQ,packedOpSnippet:qQ,cpuKernelImpl:PK,dtype:"bool"}),KQ={kernelName:xo,backendName:"webgl",kernelFunc:jQ};function XQ(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,i=zK(s,r,a);return t.makeTensorInfo([i.length],"float32",i)}var YQ={kernelName:yg,backendName:"webgl",kernelFunc:XQ},QQ=iu+`
   return x < 0.0 ? 0./0. : log(x);
-`,Z9=`
+`,ZQ=`
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -3342,18 +3343,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,J9=Ke({opSnippet:Q9,packedOpSnippet:Z9,cpuKernelImpl:MK}),eQ={kernelName:Ba,backendName:"webgl",kernelFunc:J9},tQ=ou+`
+`,JQ=Ke({opSnippet:QQ,packedOpSnippet:ZQ,cpuKernelImpl:MK}),eZ={kernelName:Ba,backendName:"webgl",kernelFunc:JQ},tZ=iu+`
   return log(1.0 + x);
-`,nQ=Ke({opSnippet:tQ}),sQ={kernelName:yl,backendName:"webgl",kernelFunc:nQ},rQ="return float(a >= 1.0 && b >= 1.0);",aQ=`
+`,nZ=Ke({opSnippet:tZ}),sZ={kernelName:bl,backendName:"webgl",kernelFunc:nZ},rZ="return float(a >= 1.0 && b >= 1.0);",aZ=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,iQ=qt({opSnippet:rQ,packedOpSnippet:aQ,dtype:"bool"}),oQ={kernelName:ko,backendName:"webgl",kernelFunc:iQ},uQ="return float(!(x >= 1.0));",lQ=Ke({opSnippet:uQ}),cQ={kernelName:vl,backendName:"webgl",kernelFunc:lQ},dQ="return float(a >= 1.0 || b >= 1.0);",pQ=`
+`,iZ=qt({opSnippet:rZ,packedOpSnippet:aZ,dtype:"bool"}),oZ={kernelName:wo,backendName:"webgl",kernelFunc:iZ},uZ="return float(!(x >= 1.0));",lZ=Ke({opSnippet:uZ}),cZ={kernelName:yl,backendName:"webgl",kernelFunc:lZ},dZ="return float(a >= 1.0 || b >= 1.0);",pZ=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,hQ=qt({opSnippet:dQ,packedOpSnippet:pQ,dtype:"bool"}),fQ={kernelName:Jd,backendName:"webgl",kernelFunc:hQ},mQ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
+`,hZ=qt({opSnippet:dZ,packedOpSnippet:pZ,dtype:"bool"}),fZ={kernelName:Zd,backendName:"webgl",kernelFunc:hZ},mZ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3372,7 +3373,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         float val = x * ${o};
         setOutput(val);
       }
-    `}},gQ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
+    `}},gZ=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${s}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3434,7 +3435,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         vec4 result = xAtOutputCoords * ${o};
         setOutput(result);
       }
-    `}},bQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool("WEBGL_PACK_NORMALIZATION")?new gQ(r.shape,a,i,o,u):new mQ(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},yQ={kernelName:ep,backendName:"webgl",kernelFunc:bQ},vQ=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
+    `}},bZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:i,alpha:o,beta:u}=s,l=X().getBool("WEBGL_PACK_NORMALIZATION")?new gZ(r.shape,a,i,o,u):new mZ(r.shape,a,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},yZ={kernelName:Jd,backendName:"webgl",kernelFunc:bZ},vZ=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3489,14 +3490,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
       }
       setOutput(result);
       }
-    `}},xQ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new vQ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},wQ={kernelName:xg,backendName:"webgl",kernelFunc:xQ};function kQ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=yi(o,e.dtype,"max",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function Q1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[c[T]];let k=vv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let C=n.texData.get(h.dataId);C.values=k}else h=Xp(r,c,n);l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims("max",l,o);let[f,m]=N.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=N.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=LK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=kQ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var IQ={kernelName:Va,backendName:"webgl",kernelFunc:Q1},SQ=E1+`
+    `}},xZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:c}=s,p=new vZ(r.shape,o,u,l,c);return n.runWebGLProgram(p,[r,a,i],r.dtype)},wZ={kernelName:vg,backendName:"webgl",kernelFunc:xZ};function kZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=yi(o,e.dtype,"max",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}function Q1(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let v=n.texData.get(h.dataId).values,x=new Array(o);for(let T=0;T<x.length;T++)x[T]=r.shape[c[T]];let k=yv(v,r.shape,r.dtype,c,x);h=n.makeTensorInfo(x,r.dtype);let C=n.texData.get(h.dataId);C.values=k}else h=Kp(r,c,n);l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims("max",l,o);let[f,m]=N.computeOutAndReduceShapes(h.shape,l),g=f;i&&(g=N.expandShapeToKeepDim(f,u));let b;if(d){let v=n.texData.get(h.dataId).values,x=LK(v,w.sizeFromShape(m),g,r.dtype);b=n.makeTensorInfo(g,r.dtype);let k=n.texData.get(b.dataId);k.values=x}else b=kZ(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),b}var IZ={kernelName:Va,backendName:"webgl",kernelFunc:Q1},SZ=E1+`
   return max(a, b);
-`,CQ=`
+`,CZ=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+jp+`
+  `+qp+`
   return result;
-`,NQ=qt({opSnippet:SQ,packedOpSnippet:CQ,cpuKernelImpl:BK}),TQ={kernelName:Wa,backendName:"webgl",kernelFunc:NQ};function $Q(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;nu(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new tl(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var _Q={kernelName:Ua,backendName:"webgl",kernelFunc:$Q};function AQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new wv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var EQ={kernelName:tp,backendName:"webgl",kernelFunc:AQ},RQ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
+`,NZ=qt({opSnippet:SZ,packedOpSnippet:CZ,cpuKernelImpl:BK}),TZ={kernelName:Wa,backendName:"webgl",kernelFunc:NZ};function $Z(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;tu(r,"maxPool");let{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1;w.assert(N.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return En({inputs:{x:r},backend:n});let p=new el(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var _Z={kernelName:Ua,backendName:"webgl",kernelFunc:$Z};function AZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=s,c=[1,1,1],p=N.computePool3DInfo(r.shape,a,i,c,o,l,u),d=new xv(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var EZ={kernelName:ep,backendName:"webgl",kernelFunc:AZ},RZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=a-1-e.padInfo.left,u=r*a-1;this.userCode=`
       const ivec2 pads = ivec2(${i}, ${o});
 
       void main() {
@@ -3542,7 +3543,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}},DQ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
+    `}},DZ=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=o-1-e.padInfo.front,p=u-1-e.padInfo.top,d=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
       const ivec3 pads = ivec3(${c}, ${p}, ${d});
 
       void main() {
@@ -3606,14 +3607,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         }
         setOutput(dotProd);
       }
-    `}};function FQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new wv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new DQ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var OQ={kernelName:kg,backendName:"webgl",kernelFunc:FQ};function PQ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;nu([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=N.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new tl(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new RQ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var zQ={kernelName:wg,backendName:"webgl",kernelFunc:PQ};function MQ(e,t,n,s){let r=new tl(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new tl(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var LQ={kernelName:Ig,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=N.computePool2DInfo(s.shape,r,a,l,i),[p,d]=MQ(s,o,c,u);return[p,d]}};function BQ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=yi(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var VQ={kernelName:Ga,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let C=vv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let T=i.texData.get(f.dataId);T.values=C}else f=Xp(s,c,i);h.push(f),l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=N.expandShapeToKeepDim(m,u));let y=BQ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function WQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"min",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var UQ={kernelName:Ha,backendName:"webgl",kernelFunc:WQ},GQ=E1+`
+    `}};function FZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,i=a,{filterSize:o,strides:u,pad:l,dimRoundingMode:c}=s,p=[1,1,1],d=N.computePool3DInfo(i.shape,o,u,p,l,c),h=new xv(d,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new DZ(d),g=n.runWebGLProgram(m,[r,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}var OZ={kernelName:wg,backendName:"webgl",kernelFunc:FZ};function PZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:i}=t,o=a;tu([a,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:c,dimRoundingMode:p}=s,d=N.computePool2DInfo(o.shape,u,l,1,c,p),h=!0,f=new el(d,"max",h),m=n.runWebGLProgram(f,[o],o.dtype),g=new RZ(d),b=n.runWebGLProgram(g,[r,m],o.dtype);return n.disposeIntermediateTensorInfo(m),b}var zZ={kernelName:xg,backendName:"webgl",kernelFunc:PZ};function MZ(e,t,n,s){let r=new el(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new el(n,"max",!0,!0,t);let i=s.runWebGLProgram(r,[e],"float32");return[a,i]}var LZ={kernelName:kg,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:i,includeBatchInIndex:o}=t,u=n;w.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let l=[1,1];w.assert(N.eitherStridesOrDilationsAreOne(a,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);let c=N.computePool2DInfo(s.shape,r,a,l,i),[p,d]=MZ(s,o,c,u);return[p,d]}};function BZ(e,t,n,s){let r=w.sizeFromShape(t),i=w.sizeFromShape(e.shape)/r,o=he({inputs:{x:e},attrs:{shape:[i,r]},backend:s}),u=yi(o,"float32","mean",s),l=he({inputs:{x:u},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(o),s.disposeIntermediateTensorInfo(u),l}var VZ={kernelName:Ga,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,i=n,o=s.shape.length,u=w.parseAxisParam(a,s.shape),l=u,c=N.getAxesPermutation(l,o),p=c!=null,d=i.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let x=i.texData.get(f.dataId).values,k=new Array(o);for(let E=0;E<k.length;E++)k[E]=s.shape[c[E]];let C=yv(x,s.shape,s.dtype,c,k);f=i.makeTensorInfo(k,s.dtype);let T=i.texData.get(f.dataId);T.values=C}else f=Kp(s,c,i);h.push(f),l=N.getInnerMostAxes(l.length,o)}N.assertAxesAreInnerMostDims("sum",l,o);let[m,g]=N.computeOutAndReduceShapes(f.shape,l),b=m;r&&(b=N.expandShapeToKeepDim(m,u));let y=BZ(f,g,b,i);for(let v of h)i.disposeIntermediateTensorInfo(v);return y}};function WZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=w.parseAxisParam(a,r.shape),l=u,c=N.getAxesPermutation(l,o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",l,o);let[d,h]=N.computeOutAndReduceShapes(p.shape,l),f=w.sizeFromShape(h),m=he({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=yi(m,m.dtype,"min",n),b;if(i){let y=N.expandShapeToKeepDim(d,u);b=he({inputs:{x:g},backend:n,attrs:{shape:y}})}else b=he({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),b}var UZ={kernelName:Ha,backendName:"webgl",kernelFunc:WZ},GZ=E1+`
   return min(a, b);
-`,HQ=`
+`,HZ=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+jp+`
+  `+qp+`
   return result;
-`,qQ=qt({opSnippet:GQ,packedOpSnippet:HQ,cpuKernelImpl:VK}),jQ={kernelName:qa,backendName:"webgl",kernelFunc:qQ},KQ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=ot(s),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
+`,qZ=qt({opSnippet:GZ,packedOpSnippet:HZ,cpuKernelImpl:VK}),jZ={kernelName:qa,backendName:"webgl",kernelFunc:qZ},KZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=ot(s),a=t.map(l=>l[0]).join(","),i=t.map((l,c)=>l[0]+e[c]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),u=n==="reflect"?0:1;if(s===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3642,7 +3643,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         ${r} coords = outC - start;
         setOutput(getX(${o}));
       }
-    `}},XQ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ot(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
+    `}},XZ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=ot(s),a=t.map(h=>h[0]).join(","),i=t.map((h,f)=>h[0]+e[f]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
         ${r} source = rc;
         if (source < start) {
           source = start * 2 - source - ${p};
@@ -3698,13 +3699,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         ${d}
         setOutput(result);
       }
-    `}},YQ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XQ(s.shape,r,a):new KQ(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},QQ={kernelName:ja,backendName:"webgl",kernelFunc:YQ},ZQ=`if (b == 0.0) return NAN;
-  return mod(a, b);`,JQ=`
+    `}},YZ=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,i=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XZ(s.shape,r,a):new KZ(s.shape,r,a);return t.runWebGLProgram(i,[s],s.dtype)},QZ={kernelName:ja,backendName:"webgl",kernelFunc:YZ},ZZ=`if (b == 0.0) return NAN;
+  return mod(a, b);`,JZ=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
-  `+jp+`
+  `+qp+`
   return result;
-`,eZ=qt({opSnippet:ZQ,packedOpSnippet:JQ}),tZ={kernelName:xl,backendName:"webgl",kernelFunc:eZ},nZ=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
+`,e7=qt({opSnippet:ZZ,packedOpSnippet:JZ}),t7={kernelName:vl,backendName:"webgl",kernelFunc:e7},n7=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3724,11 +3725,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,M8=Ke({opSnippet:z8}),L8={kernelNam
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}},sZ=`
+    `}},s7=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,rZ=`
+return a / b;`,r7=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3746,9 +3747,9 @@ return a / b;`,rZ=`
   }
 
   return result;
-`,Z1=qt({opSnippet:sZ,packedOpSnippet:rZ,checkOutOfBounds:!0}),aZ={kernelName:Ea,backendName:"webgl",kernelFunc:Z1},ww="return a - b;",J1=qt({opSnippet:ww,packedOpSnippet:ww,supportsComplex:!0,cpuKernelImpl:sX}),iZ={kernelName:ui,backendName:"webgl",kernelFunc:J1};function e2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=Q1({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=he({inputs:{x:o},backend:n,attrs:{shape:u}}),c=J1({inputs:{a:r,b:l},backend:n}),p=K1({inputs:{x:c},backend:n}),d=Yp({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:d},backend:n,attrs:{shape:u}}),f=Z1({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var oZ={kernelName:ii,backendName:"webgl",kernelFunc:e2};function uZ(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:e2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new nZ(l,c,a),d=[[i]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}var lZ={kernelName:Sg,backendName:"webgl",kernelFunc:uZ},cZ=ts+`
+`,Z1=qt({opSnippet:s7,packedOpSnippet:r7,checkOutOfBounds:!0}),a7={kernelName:Ea,backendName:"webgl",kernelFunc:Z1},xw="return a - b;",J1=qt({opSnippet:xw,packedOpSnippet:xw,supportsComplex:!0,cpuKernelImpl:sX}),i7={kernelName:ui,backendName:"webgl",kernelFunc:J1};function e2(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=Q1({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=he({inputs:{x:o},backend:n,attrs:{shape:u}}),c=J1({inputs:{a:r,b:l},backend:n}),p=K1({inputs:{x:c},backend:n}),d=Xp({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=he({inputs:{x:d},backend:n,attrs:{shape:u}}),f=Z1({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var o7={kernelName:ii,backendName:"webgl",kernelFunc:e2};function u7(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:i,normalized:o}=s,u=o?r:e2({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],c=u.shape[1],p=new n7(l,c,a),d=[[i]],h=n.runWebGLProgram(p,[u],"int32",d);return o||n.disposeIntermediateTensorInfo(u),h}var l7={kernelName:Ig,backendName:"webgl",kernelFunc:u7},c7=ts+`
   return -x;
-`,dZ=`
+`,d7=`
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -3758,14 +3759,14 @@ return a / b;`,rZ=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function pZ(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=UK(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yr(s.shape,dZ):r=new Hs(s.shape,cZ),n.runWebGLProgram(r,[s],s.dtype)}var hZ={kernelName:Io,backendName:"webgl",kernelFunc:pZ},fZ=xs.nonMaxSuppressionV3Impl;function mZ(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=fZ(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var gZ={kernelName:Co,backendName:"webgl",kernelFunc:mZ},bZ=xs.nonMaxSuppressionV4Impl;function yZ(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=bZ(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var vZ={kernelName:wl,backendName:"webgl",kernelFunc:yZ},xZ=xs.nonMaxSuppressionV5Impl;function wZ(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xZ(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var kZ={kernelName:No,backendName:"webgl",kernelFunc:wZ},IZ=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function p7(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[i,o]=UK(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r;return X().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Yr(s.shape,d7):r=new Hs(s.shape,c7),n.runWebGLProgram(r,[s],s.dtype)}var h7={kernelName:ko,backendName:"webgl",kernelFunc:p7},f7=xs.nonMaxSuppressionV3Impl;function m7(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=f7(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var g7={kernelName:So,backendName:"webgl",kernelFunc:m7},b7=xs.nonMaxSuppressionV4Impl;function y7(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=b7(c,p,i,o,u,l);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var v7={kernelName:xl,backendName:"webgl",kernelFunc:y7},x7=xs.nonMaxSuppressionV5Impl;function w7(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=x7(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var k7={kernelName:Co,backendName:"webgl",kernelFunc:w7},I7=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${s}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},SZ=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new IZ(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},CZ={kernelName:$o,backendName:"webgl",kernelFunc:SZ};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Er({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var NZ={kernelName:Ho,backendName:"webgl",kernelFunc:Md};function t2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Jl({inputs:{input:s},backend:n}),a=t2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=Er({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return ec({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var TZ={kernelName:To,backendName:"webgl",kernelFunc:t2};function $Z(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Hm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Hm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=W1({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var _Z={kernelName:_o,backendName:"webgl",kernelFunc:$Z},AZ=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=ot(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
+    `}},S7=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=w.sizeFromShape(r.shape),l=new I7(u,a,i,o),c=he({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[c],r.dtype);n.disposeIntermediateTensorInfo(c);let d=[...r.shape,a],h=he({inputs:{x:p},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(p),h},C7={kernelName:To,backendName:"webgl",kernelFunc:S7};function zd(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Zl({inputs:{input:s},backend:n}),a=zd({inputs:{x:r},backend:n}),i=Yp({inputs:{input:s},backend:n}),o=zd({inputs:{x:i},backend:n}),u=Er({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Jl({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var N7={kernelName:Go,backendName:"webgl",kernelFunc:zd};function t2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Zl({inputs:{input:s},backend:n}),a=t2({inputs:{x:r},backend:n}),i=Yp({inputs:{input:s},backend:n}),o=zd({inputs:{x:i},backend:n}),u=Er({inputs:{real:a,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return Jl({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var T7={kernelName:No,backendName:"webgl",kernelFunc:t2};function $7(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Gm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Gm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=W1({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeIntermediateTensorInfo(c)),l}var _7={kernelName:$o,backendName:"webgl",kernelFunc:$7},A7=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let s=e.length,r=ot(s),a=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
         int start = ${a};
         int end = ${i};
 
@@ -3790,7 +3791,7 @@ return a / b;`,rZ=`
           setOutput(getX(${o}));
         }
       }
-    `}},EZ=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ot(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
+    `}},E7=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=ot(s),a=t.map(f=>f[0]).join(","),i=t.map((f,m)=>f[0]+e[m]).join(","),o=un("rc",s),u=un("source",s),l=`${o[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[s-1]} += 1;
        if(${l}) {
       `,s===1?"":`}
        rc = outputLoc;
@@ -3814,7 +3815,7 @@ return a / b;`,rZ=`
         ${h}
         setOutput(result);
       }
-    `}},n2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return ec({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new EZ(r.shape,a,i):new AZ(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},RZ={kernelName:Xa,backendName:"webgl",kernelFunc:n2},DZ=`
+    `}},n2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return Jl({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new E7(r.shape,a,i):new A7(r.shape,a,i),u=[[i]];return n.runWebGLProgram(o,[r],r.dtype,u)},R7={kernelName:Xa,backendName:"webgl",kernelFunc:n2},D7=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -3823,7 +3824,7 @@ return a / b;`,rZ=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,FZ=`
+`,F7=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -3837,11 +3838,11 @@ return a / b;`,rZ=`
   result.a = isExpZero.a ? 1.0 : result.a;
 
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
-  `+jp+`
+  `+qp+`
   return result;
-`,OZ=qt({opSnippet:DZ,packedOpSnippet:FZ}),PZ={kernelName:Ya,backendName:"webgl",kernelFunc:OZ};function zZ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=N.getAxesPermutation(c,o),d=r;p!=null&&(d=cn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,o),u.push(d)),N.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=HK(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=N.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=cp(r.dtype),v=yi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=N.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var MZ={kernelName:Ao,backendName:"webgl",kernelFunc:zZ},s2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=qK(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},LZ={kernelName:kl,backendName:"webgl",kernelFunc:s2},BZ="return 1.0 / x;",VZ=Ke({opSnippet:BZ}),WZ={kernelName:Il,backendName:"webgl",kernelFunc:VZ},UZ=ts+`
+`,O7=qt({opSnippet:D7,packedOpSnippet:F7}),P7={kernelName:Ya,backendName:"webgl",kernelFunc:O7};function z7(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s,o=r.shape.length,u=[],l=w.parseAxisParam(a,r.shape),c=l,p=N.getAxesPermutation(c,o),d=r;p!=null&&(d=cn({inputs:{x:r},backend:n,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,o),u.push(d)),N.assertAxesAreInnerMostDims("prod",c,o);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:b}=HK(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,b,m)}else{let[f,m]=N.computeOutAndReduceShapes(d.shape,c),g=w.sizeFromShape(m),b=he({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),y=lp(r.dtype),v=yi(b,y,"prod",n);h=he({inputs:{x:v},backend:n,attrs:{shape:f}}),u.push(b),u.push(v)}if(i){u.push(h);let f=N.expandShapeToKeepDim(h.shape,l);h=he({inputs:{x:h},backend:n,attrs:{shape:f}})}return u.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var M7={kernelName:_o,backendName:"webgl",kernelFunc:z7},s2=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=qK(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},L7={kernelName:wl,backendName:"webgl",kernelFunc:s2},B7="return 1.0 / x;",V7=Ke({opSnippet:B7}),W7={kernelName:kl,backendName:"webgl",kernelFunc:V7},U7=ts+`
   return (x < 0.0) ? 0.0 : x;
-`,GZ=`
+`,G7=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3851,9 +3852,9 @@ return a / b;`,rZ=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,HZ=Ke({opSnippet:UZ,packedOpSnippet:GZ}),qZ={kernelName:Za,backendName:"webgl",kernelFunc:HZ},jZ=ts+`
+`,H7=Ke({opSnippet:U7,packedOpSnippet:G7}),q7={kernelName:Za,backendName:"webgl",kernelFunc:H7},j7=ts+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,KZ=`
+`,K7=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -3863,7 +3864,7 @@ return a / b;`,rZ=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,XZ=Ke({opSnippet:jZ,packedOpSnippet:KZ}),YZ={kernelName:ei,backendName:"webgl",kernelFunc:XZ},QZ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,X7=Ke({opSnippet:j7,packedOpSnippet:K7}),Y7={kernelName:ei,backendName:"webgl",kernelFunc:X7},Q7=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0]/c[0]},
           ${l[1]/c[1]});
@@ -3896,7 +3897,7 @@ return a / b;`,rZ=`
 
         setOutput(newValue);
       }
-    `}},ZZ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},Z7=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${l[0]/c[0]},
           ${l[1]/c[1]},
@@ -3973,7 +3974,7 @@ return a / b;`,rZ=`
 
         setOutput(newValue);
       }
-    `}};function JZ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new ZZ(r.shape,u,l,a,i):new QZ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],"float32")}var eJ={kernelName:Ja,backendName:"webgl",kernelFunc:JZ},tJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function J7(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Z7(r.shape,u,l,a,i):new Q7(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],"float32")}var eJ={kernelName:Ja,backendName:"webgl",kernelFunc:J7},tJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4054,7 +4055,7 @@ return a / b;`,rZ=`
 
         setOutput(accumulator);
       }
-    `}};function nJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new tJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var sJ={kernelName:Ng,backendName:"webgl",kernelFunc:nJ},rJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function nJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new tJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var sJ={kernelName:Cg,backendName:"webgl",kernelFunc:nJ},rJ=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,i,o,u]=e;this.outputShape=[a,t,n,u];let l=[s&&t>1?i-1:i,s&&n>1?o-1:o],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${l[0]/c[0]},
           ${l[1]/c[1]});
@@ -4117,7 +4118,7 @@ return a / b;`,rZ=`
 
         setOutput(newValue);
       }
-    `}};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aJ(r.shape,u,l,a,i):new rJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var oJ={kernelName:Sl,backendName:"webgl",kernelFunc:iJ},uJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function iJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=X().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new aJ(r.shape,u,l,a,i):new rJ(r.shape,u,l,a,i);return n.runWebGLProgram(c,[r],r.dtype)}var oJ={kernelName:Il,backendName:"webgl",kernelFunc:iJ},uJ=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,i]=e,o=[n&&a>1?s-1:s,n&&i>1?r-1:r],u=[n&&a>1?a-1:a,n&&i>1?i-1:i],l=o[0]/u[0],c=o[1]/u[1],p=1/l,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4187,7 +4188,7 @@ return a / b;`,rZ=`
 
         setOutput(accumulator);
       }
-    `}};function lJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new uJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var cJ={kernelName:Cg,backendName:"webgl",kernelFunc:lJ},dJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function lJ(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:i}=s,o=new uJ(a.shape,r.shape,i);return n.runWebGLProgram(o,[a],a.dtype)}var cJ={kernelName:Sg,backendName:"webgl",kernelFunc:lJ},dJ=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
@@ -4225,7 +4226,7 @@ return a / b;`,rZ=`
           }
           setOutput(result);
         }
-    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function hJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return En({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pJ(r.shape,o):new dJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var fJ={kernelName:Ro,backendName:"webgl",kernelFunc:hJ},mJ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
+    `;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((b,y)=>d(y,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function hJ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=r.shape.length,o=w.parseAxisParam(a,r.shape);if(i===0)return En({inputs:{x:r},backend:n});let u=X().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new pJ(r.shape,o):new dJ(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var fJ={kernelName:Eo,backendName:"webgl",kernelFunc:hJ},mJ=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4244,7 +4245,7 @@ return a / b;`,rZ=`
           }
           setOutput(outputValue);
         }
-    `}},gJ={kernelName:qo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new mJ(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},bJ=`
+    `}},gJ={kernelName:Ho,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new mJ(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[[l,c,Math.sin(r),Math.cos(r)]];return o.runWebGLProgram(u,[s],s.dtype,p)}},bJ=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4259,7 +4260,7 @@ return a / b;`,rZ=`
       return base + 1.0;
     }
   }
-`,yJ=Ke({opSnippet:bJ}),vJ={kernelName:Do,backendName:"webgl",kernelFunc:yJ},xJ="return inversesqrt(x);",wJ=Ke({opSnippet:xJ,cpuKernelImpl:jK}),kJ={kernelName:ti,backendName:"webgl",kernelFunc:wJ},r2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,yJ=Ke({opSnippet:bJ}),vJ={kernelName:Ro,backendName:"webgl",kernelFunc:yJ},xJ="return inversesqrt(x);",wJ=Ke({opSnippet:xJ,cpuKernelImpl:jK}),kJ={kernelName:ti,backendName:"webgl",kernelFunc:wJ},r2=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let o=ot(r.length),u=ot(a.length),l="";n===1?l="i":n===2&&(l="i, j");let c=`getIndices(${l})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${o} strides = ${o}(${r});
 
         void main() {
@@ -4279,7 +4280,7 @@ return a / b;`,rZ=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function IJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new r2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var SJ={kernelName:Fo,backendName:"webgl",kernelFunc:IJ},CJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`
+      `}};function IJ(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=he({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=he({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new r2(u,o,h.shape.length,f.shape.length,c,d),b=n.runWebGLProgram(g,[f,h,m],f.dtype),y=he({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(b),n.disposeIntermediateTensorInfo(m),y}var SJ={kernelName:Do,backendName:"webgl",kernelFunc:IJ},CJ=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);s=o.join(),r=u.join()}let a=ot(n);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
         float cVal = getC(${s});
@@ -4289,13 +4290,13 @@ return a / b;`,rZ=`
           setOutput(getB(${r}));
         }
       }
-    `}};function NJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new CJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var TJ={kernelName:Oo,backendName:"webgl",kernelFunc:NJ},$J=`
+    `}};function NJ(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new CJ(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var TJ={kernelName:Fo,backendName:"webgl",kernelFunc:NJ},$J=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${N.SELU_SCALEALPHA};
   float scale = ${N.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,_J=Ke({opSnippet:$J}),AJ={kernelName:Cl,backendName:"webgl",kernelFunc:_J},EJ=ou+`
+`,_J=Ke({opSnippet:$J}),AJ={kernelName:Sl,backendName:"webgl",kernelFunc:_J},EJ=iu+`
   return 1.0 / (1.0 + exp(-1.0 * x));
 `,RJ=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
@@ -4310,12 +4311,12 @@ return a / b;`,rZ=`
 `,DJ=Ke({opSnippet:EJ,packedOpSnippet:RJ,cpuKernelImpl:KK}),FJ={kernelName:si,backendName:"webgl",kernelFunc:DJ},OJ=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,PJ=Ke({opSnippet:OJ}),zJ={kernelName:Nl,backendName:"webgl",kernelFunc:PJ},MJ=ou+`
+`,PJ=Ke({opSnippet:OJ}),zJ={kernelName:Cl,backendName:"webgl",kernelFunc:PJ},MJ=iu+`
   return sin(x);
 `,LJ=Ke({opSnippet:MJ}),BJ={kernelName:ni,backendName:"webgl",kernelFunc:LJ},VJ=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:zo,backendName:"webgl",kernelFunc:WJ},GJ=`
+`,WJ=Ke({opSnippet:VJ}),UJ={kernelName:Po,backendName:"webgl",kernelFunc:WJ},GJ=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4335,15 +4336,15 @@ return a / b;`,rZ=`
     result = log(exp_x + 1.0);
   }
   return result;
-`,HJ=Ke({opSnippet:GJ}),qJ={kernelName:Tl,backendName:"webgl",kernelFunc:HJ},jJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=n2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},KJ={kernelName:Mo,backendName:"webgl",kernelFunc:jJ};function XJ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
+`,HJ=Ke({opSnippet:GJ}),qJ={kernelName:Nl,backendName:"webgl",kernelFunc:HJ},jJ=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=n2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=he({inputs:{x:c},backend:n,attrs:{shape:p}}),m=cn({inputs:{x:f},backend:n,attrs:{perm:d}}),g=he({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeIntermediateTensorInfo(b)),g},KJ={kernelName:zo,backendName:"webgl",kernelFunc:jJ};function XJ(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
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+              ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u,!0);return n.makeTensorInfo(c,s.dtype,l)}var eee={kernelName:sp,backendName:"webgl",kernelFunc:JJ};function tee(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var nee={kernelName:ap,backendName:"webgl",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=new r2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var ree={kernelName:ip,backendName:"webgl",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=uu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var iee={kernelName:Lo,backendName:"webgl",kernelFunc:aee},kw="return sqrt(x);",oee=Ke({opSnippet:kw,packedOpSnippet:kw,cpuKernelImpl:ZK}),uee={kernelName:ri,backendName:"webgl",kernelFunc:oee},lee="return x * x;",cee=Ke({opSnippet:lee}),dee={kernelName:_l,backendName:"webgl",kernelFunc:cee},Iw="return (a - b) * (a - b);",pee=qt({opSnippet:Iw,packedOpSnippet:Iw}),hee={kernelName:oi,backendName:"webgl",kernelFunc:pee};function fee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ts+`
+             ${a.shape}`);let i=n.readSync(s.dataId),o=n.readSync(r.dataId),u=n.readSync(a.dataId),[l,c]=N1(i,s.shape,s.dtype,o,u);return n.makeTensorInfo(c,s.dtype,l)}var nee={kernelName:rp,backendName:"webgl",kernelFunc:tee};function see(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=new r2(l,u,r.shape.length,a.shape.length,c,[p,1],d),f=n.runWebGLProgram(h,[a,r,i],a.dtype),m=he({inputs:{x:f},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(f),m}var ree={kernelName:ap,backendName:"webgl",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=ou({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var iee={kernelName:Mo,backendName:"webgl",kernelFunc:aee},ww="return sqrt(x);",oee=Ke({opSnippet:ww,packedOpSnippet:ww,cpuKernelImpl:ZK}),uee={kernelName:ri,backendName:"webgl",kernelFunc:oee},lee="return x * x;",cee=Ke({opSnippet:lee}),dee={kernelName:$l,backendName:"webgl",kernelFunc:cee},kw="return (a - b) * (a - b);",pee=qt({opSnippet:kw,packedOpSnippet:kw}),hee={kernelName:oi,backendName:"webgl",kernelFunc:pee};function fee({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=ts+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
   `,a=new Hs(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var mee={kernelName:di,backendName:"webgl",kernelFunc:fee},gee=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=ot(n.length),a=ot(n.length),i="";if(s===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
       ${r} begin = ${r}(${e});
@@ -4353,7 +4354,7 @@ return a / b;`,rZ=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${i}));
       }
-    `}};function bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),E=uu({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=JK(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new gee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let C=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),C}var yee={kernelName:Bo,backendName:"webgl",kernelFunc:bee};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=eX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var xee={kernelName:op,backendName:"webgl",kernelFunc:vee};function wee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=tX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var kee={kernelName:Tg,backendName:"webgl",kernelFunc:wee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=nX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var See={kernelName:$g,backendName:"webgl",kernelFunc:Iee},Cee="return tan(x);",Nee=Ke({opSnippet:Cee}),Tee={kernelName:Vo,backendName:"webgl",kernelFunc:Nee},$ee=`
+    `}};function bee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=he({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let T=wt.computeOutShape(y,v,x),E=ou({inputs:{x:r},backend:n,attrs:{begin:y,size:T}});k=he({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),A=De(r.shape,r.dtype,E),P=JK(h,A,x,y);k=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new gee(y,x,h);k=n.runWebGLProgram(E,[r],r.dtype)}let C=he({inputs:{x:k},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(k),C}var yee={kernelName:Lo,backendName:"webgl",kernelFunc:bee};function vee(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=eX(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var xee={kernelName:ip,backendName:"webgl",kernelFunc:vee};function wee(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:i}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(i.dataId)[0],[l,c,p]=tX(o,u,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",l),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var kee={kernelName:Ng,backendName:"webgl",kernelFunc:wee};function Iee(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(a.dataId),o=nX(i,r);return n.makeTensorInfo(a.shape,"int32",o)}var See={kernelName:Tg,backendName:"webgl",kernelFunc:Iee},Cee="return tan(x);",Nee=Ke({opSnippet:Cee}),Tee={kernelName:Bo,backendName:"webgl",kernelFunc:Nee},$ee=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
 `,_ee=Ke({opSnippet:$ee}),Aee={kernelName:li,backendName:"webgl",kernelFunc:_ee},Eee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=ot(this.rank),r=Ree(e);this.userCode=`
@@ -4435,7 +4436,7 @@ return a / b;`,rZ=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function Wr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Sw(e){let t=1;for(;t<e;)t*=2;return t}function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=aX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,ec({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Wr(n,h);let b=Sw(a),y=Sw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new Fee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Wr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Oee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Wr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let C=v;v=uu({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Wr(n,C);let T=Y1({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Wr(n,g);let E=l.slice(0,-1);E.push(a),C=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Wr(n,C);let A=T;return T=he({inputs:{x:T},attrs:{shape:E},backend:n}),Wr(n,A),[T,v]}var zee={kernelName:Wo,backendName:"webgl",kernelFunc:Pee},Mee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
+     `}};function Wr(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Iw(e){let t=1;for(;t<e;)t*=2;return t}function Pee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=r.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([r])||c<o||a>u){let P=n.readSync(r.dataId),[R,F]=aX(P,l,r.dtype,a,i);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(F.shape,F.dtype,F.values)]}if(a===0)return l[l.length-1]=0,[n.makeTensorInfo(l,r.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(c===1)return[r,Jl({attrs:{shape:l,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=w.sizeFromShape(l)/c,g=he({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Wr(n,h);let b=Iw(a),y=Iw(c),v=null,x=()=>v===null?[g,g]:[g,v],k=(P,R,F)=>{let $=x(),z=new Fee(F),q=[[c],[v===null?1:0],[Number.NEGATIVE_INFINITY],[P],[R]],K=v;v=n.runWebGLProgram(z,$,"int32",q),Wr(n,K)};for(let P=1;P<b;P*=2){let R=P*2;for(let F=P;F>=1;F/=2)k(R,F,[m,y])}for(let P=y;P>b;P/=2){let R=x(),F=new Oee([m,P/2]),z=[[c],[v===null?1:0],[b]],W=v;v=n.runWebGLProgram(F,R,"int32",z),Wr(n,W);let q=b/2,K=q*2;for(let Y=q;Y>=1;Y/=2)k(K,Y,v.shape)}let C=v;v=ou({inputs:{x:v},backend:n,attrs:{begin:0,size:[m,a]}}),Wr(n,C);let T=Y1({inputs:{x:g,indices:v},backend:n,attrs:{axis:1,batchDims:1}});Wr(n,g);let E=l.slice(0,-1);E.push(a),C=v,v=he({inputs:{x:v},attrs:{shape:E},backend:n}),Wr(n,C);let A=T;return T=he({inputs:{x:T},attrs:{shape:E},backend:n}),Wr(n,A),[T,v]}var zee={kernelName:Vo,backendName:"webgl",kernelFunc:Pee},Mee=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let i=n==="nearest"?1:2,o;switch(s){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${o} == 2) {
@@ -4547,7 +4548,7 @@ return a / b;`,rZ=`
               }
               setOutput(outputValue);
             }
-        `}};function Lee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Mee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Bee={kernelName:Uo,backendName:"webgl",kernelFunc:Lee};function Vee(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;nu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=iX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var Wee={kernelName:_g,backendName:"webgl",kernelFunc:Vee};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=uu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Gee={kernelName:Go,backendName:"webgl",kernelFunc:Uee},Hee=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
+        `}};function Lee(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Mee(p,d,i,o,u,g);return n.runWebGLProgram(b,[r,a],"float32")}var Bee={kernelName:Wo,backendName:"webgl",kernelFunc:Lee};function Vee(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;tu(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=s.readSync(a.dataId),{outputValues:o,outputShape:u,indices:l}=iX(i,r,a.shape,a.dtype);return[s.makeTensorInfo(u,a.dtype,o),s.makeTensorInfo([l.length],"int32",l)]}var Wee={kernelName:$g,backendName:"webgl",kernelFunc:Vee};function Uee(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=ou({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=he({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Gee={kernelName:Uo,backendName:"webgl",kernelFunc:Uee},Hee=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,i=a*Math.ceil(r/n);this.outputShape=[s,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,c=n%4,p=`
         sumValue += dot(values, segFilter);
     `,d="";r%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${r}) {
@@ -4653,10 +4654,10 @@ return a / b;`,rZ=`
         }
         setOutput(${u});
       }
-    `}};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=N.getAxesPermutation([l],o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=N.getInnerMostAxes(1,o)[0]);let d=N.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=cp(r.dtype),g=(x,k,C,T,E)=>{let A=x.shape[0],P=x.shape[1],R=N.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new Hee(F,k),z=n.compileAndRun($,[x,C],T);if(u.push(z),z.shape[1]===E)return z;let W=s2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=a2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,T,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=N.getUndoAxesPermutation(c);v=cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jee={kernelName:up,backendName:"webgl",kernelFunc:qee},Kee=[e8,n8,a8,u8,c8,h8,m8,b8,w8,I8,N8,_8,R8,P8,L8,V8,U8,j8,X8,Q8,tY,uY,cY,pY,yY,xY,SY,OX,TY,RY,PY,WY,GY,qY,KY,YY,JY,n7,a7,o7,l7,p7,f7,y7,x7,I7,N7,$7,R7,P7,B7,U7,q7,j7,X7,Q7,J7,t9,s9,o9,c9,h9,m9,y9,w9,C9,_9,FX,E9,AY,F9,z9,B9,zX,G9,K9,Y9,eQ,sQ,oQ,cQ,fQ,yQ,wQ,IQ,TQ,_Q,EQ,OQ,zQ,LQ,VQ,UQ,jQ,QQ,tZ,lZ,WX,hZ,gZ,vZ,kZ,fY,CZ,TZ,_Z,RZ,PZ,LX,MZ,LZ,mY,aZ,WZ,qZ,YZ,GX,eJ,sJ,oJ,cJ,fJ,gJ,vJ,kJ,SJ,TJ,AJ,FJ,zJ,BJ,UJ,iY,oZ,qJ,KJ,YJ,ZJ,eee,nee,ree,iee,uee,dee,hee,mee,yee,xee,kee,See,iZ,QX,Tee,Aee,Dee,zee,Bee,ZX,Wee,Gee,jee,NZ];for(let e of Kee)Al(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Xee(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function cd(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function kv(){return`
+    `}};function qee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:i}=s,o=r.shape.length,u=[],l=0,c=N.getAxesPermutation([l],o),p=r;c!=null&&(p=cn({inputs:{x:r},backend:n,attrs:{perm:c}}),u.push(p),l=N.getInnerMostAxes(1,o)[0]);let d=N.segment_util.computeOutShape(p.shape,l,i),h=w.sizeFromShape([p.shape[l]]),f=he({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(f);let m=lp(r.dtype),g=(x,k,C,T,E)=>{let A=x.shape[0],P=x.shape[1],R=N.segment_util.segOpComputeOptimalWindowSize(P,E),F={windowSize:R,inSize:P,batchSize:A,numSegments:E},$=new Hee(F,k),z=n.compileAndRun($,[x,C],T);if(u.push(z),z.shape[1]===E)return z;let W=s2({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),q=a2({inputs:{x:W},backend:n,attrs:{reps:[P/R]}});return u.push(W),u.push(q),g(z,k,q,T,E)},b=g(f,"unsortedSegmentSum",a,m,i),y=he({inputs:{x:b},backend:n,attrs:{shape:d}}),v=y;if(c!=null){u.push(y);let x=N.getUndoAxesPermutation(c);v=cn({inputs:{x:v},backend:n,attrs:{perm:x}})}return u.forEach(x=>n.disposeIntermediateTensorInfo(x)),v}var jee={kernelName:op,backendName:"webgl",kernelFunc:qee},Kee=[e8,n8,a8,u8,c8,h8,m8,b8,w8,I8,N8,_8,R8,P8,L8,V8,U8,j8,X8,Q8,tY,uY,cY,pY,yY,xY,SY,OX,TY,RY,PY,WY,GY,qY,KY,YY,JY,n9,a9,o9,l9,p9,f9,y9,x9,I9,N9,$9,R9,P9,B9,U9,q9,j9,X9,Q9,J9,tQ,sQ,oQ,cQ,hQ,mQ,yQ,wQ,CQ,_Q,FX,EQ,AY,FQ,zQ,BQ,zX,GQ,KQ,YQ,eZ,sZ,oZ,cZ,fZ,yZ,wZ,IZ,TZ,_Z,EZ,OZ,zZ,LZ,VZ,UZ,jZ,QZ,t7,l7,WX,h7,g7,v7,k7,fY,C7,T7,_7,R7,P7,LX,M7,L7,mY,a7,W7,q7,Y7,GX,eJ,sJ,oJ,cJ,fJ,gJ,vJ,kJ,SJ,TJ,AJ,FJ,zJ,BJ,UJ,iY,o7,qJ,KJ,YJ,ZJ,eee,nee,ree,iee,uee,dee,hee,mee,yee,xee,kee,See,i7,QX,Tee,Aee,Dee,zee,Bee,ZX,Wee,Gee,jee,N7];for(let e of Kee)_l(e);var zs=X();zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);zs.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);zs.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);zs.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Xee(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Wt(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function ld(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function wv(){return`
   @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
 `}function Rr(){return`
-  ${kv()}
+  ${wv()}
   fn main(@builtin(local_invocation_id) LocalId : vec3<u32>,
           @builtin(global_invocation_id) GlobalId : vec3<u32>,
           @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {
@@ -4693,7 +4694,7 @@ return a / b;`,rZ=`
     }
   `),s===!0)return r.push(`
       struct Matrix0 {
-        numbers: array<${cd(t.dtype,n.isVec4)}>;
+        numbers: array<${ld(t.dtype,n.isVec4)}>;
       };
       struct Uniform {
         size            : i32;
@@ -4704,8 +4705,8 @@ return a / b;`,rZ=`
 
       @group(0) @binding(0) var<storage, write> result : Matrix0;
       @group(0) @binding(2) var<uniform> uniforms: Uniform;
-    `),[Cw,r.join(`
-`),Nw(t.shape),n.getUserCode()].join(`
+    `),[Sw,r.join(`
+`),Cw(t.shape),n.getUserCode()].join(`
 `);let a="struct Uniforms { NAN : f32; ";n.variableNames.forEach((p,d)=>{a+=`${p.charAt(0).toLowerCase()+p.slice(1)}Shape : ${Wt(e[d].shape.length)}; `}),a+=`outShape : ${Wt(t.shape.length)} ; `;let i=t.shape.length-1;a+=`
        outShapeStrides: ${Wt(i)}; `,n.size&&(a+="size : i32; "),n.uniforms&&(a+=n.uniforms),a+="};",r.push(a),n.atomic?r.push(`
     struct Matrix0 {
@@ -4715,21 +4716,21 @@ return a / b;`,rZ=`
     @group(0) @binding(0) var<storage, read_write> result : Matrix0;
   `):r.push(`
     struct Matrix0 {
-        numbers: array<${cd(t.dtype,n.isVec4)}>;
+        numbers: array<${ld(t.dtype,n.isVec4)}>;
     };
 
     @group(0) @binding(0) var<storage, write> result : Matrix0;
   `),n.variableNames.forEach((p,d)=>{r.push(`
     struct Matrix${1+d} {
-      numbers: array<${cd(e[d].dtype,n.isVec4)}>;
+      numbers: array<${ld(e[d].dtype,n.isVec4)}>;
     };
     @group(0) @binding(${1+d}) var<storage, read> ${p} : Matrix${1+d};
     `)}),a!==""&&r.push(`
     @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms : Uniforms;
-    `);let[o,u]=nte(t.shape,n.dispatchLayout),l=[Cw,r.join(`
-`),Nw(t.shape),o,Qee(t.shape.length)];if(n.atomic||l.push(Zee(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Jee(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
+    `);let[o,u]=nte(t.shape,n.dispatchLayout),l=[Sw,r.join(`
+`),Cw(t.shape),o,Qee(t.shape.length)];if(n.atomic||l.push(Zee(t.shape,t.dtype,n.isVec4)),u===t.shape.length){let p=e.map(d=>Jee(d,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
 `);l.push(p)}return l.push(n.getUserCode()),l.join(`
-`)}var Cw=`
+`)}var Sw=`
   // Checks whether coordinates lie within the bounds of the shape.
   fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
     return all(coord >= vec2<i32>(0)) && all(coord < shape);
@@ -4764,20 +4765,17 @@ return a / b;`,rZ=`
     return res;
   }
 
-  fn isNanCustom(val : f32) -> bool {
-    if (val > 0.0) {
-      return false;
-    }
-    if (val < 0.0) {
-      return false;
-    }
-    if (val == 0.0) {
-      return false;
-    }
-    return true;
+  // NaN defination in IEEE 754-1985 is :
+  //   - sign = either 0 or 1.
+  //   - biased exponent = all 1 bits.
+  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).
+  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
+  fn isnan(val: f32) -> bool {
+    let floatToUint: u32 = bitcast<u32>(val);
+    return (floatToUint & 0x7fffffffu) > 0x7f800000u;
   }
-  fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
-    return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
+  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
+    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
 `;function Qee(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
@@ -4796,7 +4794,7 @@ return a / b;`,rZ=`
           return dot(coords, vec4<i32>(
             uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
         }
-        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Zee(e,t,n){let s=e.length,r=cd(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:w.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Zee(e,t,n){let s=e.length,r=ld(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result.numbers[flatIndex] = ${r}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -4905,7 +4903,7 @@ return a / b;`,rZ=`
     }
     `,a];let i="",o=[n,s,r],u=0;for(let d=0;d<o.length;d++){let h=o[d];if(h.length!==0)if(u+=h.length,h.length===1)i+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=Xee(h,"uniforms.outShape");i+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)i+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?i+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:i+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let l=[];for(let d=0;d<u;d++)l.push(`d${d}`);let c=Wt(u),p=`fn getOutputCoords() -> ${c} {
     ${i}
-  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Nw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+  `;return l.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${l.join(",")}); }`,[p,u]}function Cw(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=w.computeStrides(e),s=Wt(t),r=[];for(let i=0;i<t;i++)r.push(`d${i}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
       return vec2<i32>(d0, d1);
     }`;let a="var index2 = index;"+n.map((i,o)=>{let u=`let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`,l=o===n.length-1?`let ${r[o+1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`:`index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;return`${u}; ${l};`}).join("");return`
@@ -4913,10 +4911,10 @@ return a / b;`,rZ=`
       ${a}
       return ${s}(${r.join(",")});
     }
-  `}var i2={};Ae(i2,{ArrayBufferToTypedArray:()=>u2,GPUBytesPerElement:()=>qm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>Iv,computeWorkGroupSizeForMatMul:()=>o2,computeWorkPerThreadForConv2d:()=>Sv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Cv,tilesFitEvenlyIntoShape:()=>Ks});var Mi=65535,ea=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ea(e.x.map(u=>t[u]))/(n[0]*s[0])),e.y?Math.ceil(ea(e.y.map(u=>t[u]))/(n[1]*s[1])):1,e.z?Math.ceil(ea(e.z.map(u=>t[u]))/(n[2]*s[2])):1];if(r<=Mi&&a<=Mi&&i<=Mi)return[r,a,i];w.assert(r>Mi&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let o=Math.ceil(Math.sqrt(r));return o>Mi?(o=Math.ceil(Math.cbrt(r)),w.assert(o<=Mi,()=>"Total dispatch size exceeds WebGPU maximum."),[o,o,o]):[o,o,1]}function Iv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function o2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Sv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function qm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function u2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Cv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var ste="return a + b;",rte="return areal * breal - aimag * bimag;",ate="return areal * bimag + aimag * breal;",ite="return a / b;",ote="return a * b;",ute="return (a - b) * (a - b);",lte="return a - b;",cte="return f32(a == b);",dte="return vec4<f32>(a == b);",pte="return f32(a > b);",hte="return vec4<f32>(a > b);",fte="return f32(a >= b);",mte="return vec4<f32>(a >= b);",gte="return f32(a < b);",bte="return vec4<f32>(a < b);",yte="return f32(a <= b);",vte="return vec4<f32>(a <= b);",xte="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",wte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  `}var i2={};Ae(i2,{ArrayBufferToTypedArray:()=>u2,GPUBytesPerElement:()=>Hm,computeDispatch:()=>_e,computeWorkGroupSizeForConv2d:()=>kv,computeWorkGroupSizeForMatMul:()=>o2,computeWorkPerThreadForConv2d:()=>Iv,flatDispatchLayout:()=>Be,isWebGPUSupported:()=>Sv,tilesFitEvenlyIntoShape:()=>Ks});var ea=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function Ks(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function _e(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,i]=[Math.ceil(ea(e.x.map(o=>t[o]))/(n[0]*s[0])),e.y?Math.ceil(ea(e.y.map(o=>t[o]))/(n[1]*s[1])):1,e.z?Math.ceil(ea(e.z.map(o=>t[o]))/(n[2]*s[2])):1];return[r,a,i]}function kv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function o2(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Iv(e,t){let n=ea(e.x.map(r=>t[r])),s=ea(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function Be(e){return{x:e.map((t,n)=>n)}}function Hm(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function u2(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function Sv(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var ste="return a + b;",rte="return areal * breal - aimag * bimag;",ate="return areal * bimag + aimag * breal;",ite="return a / b;",ote="return a * b;",ute="return (a - b) * (a - b);",lte="return a - b;",cte="return f32(a == b);",dte="return vec4<f32>(a == b);",pte="return f32(a > b);",hte="return vec4<f32>(a > b);",fte="return f32(a >= b);",mte="return vec4<f32>(a >= b);",gte="return f32(a < b);",bte="return vec4<f32>(a < b);",yte="return f32(a <= b);",vte="return vec4<f32>(a <= b);",xte="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",wte=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
   vec4<f32>(b >= vec4<f32>(1.0)));`,kte=`
-  if (isNanCustom(a)) { return a; }
-  if (isNanCustom(b)) { return b; }
+  if (isnan(a)) { return a; }
+  if (isnan(b)) { return b; }
   `,l2=`
   if (isNaN.r) {
     resultTemp.r = uniforms.NAN;
@@ -4993,14 +4991,14 @@ return a / b;`,rZ=`
   `,_te="if (a < 0.0) { return b * a; }  return a;",Ate=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function Tw(e,t){let n=t?l2:kte;return t?`
+  `;function Nw(e,t){let n=t?l2:kte;return t?`
     var resultTemp = vec4<f32>(${e}(a, b));
-    let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
+    let isNaN = isnanVec4(a) | isnanVec4(b);
     `+n+`
     return resultTemp;
   `:n+`
     return ${e}(a, b);
-  `}function tc(e,t){switch(e){case 0:return ote;case 1:return ste;case 2:return lte;case 3:return ite;case 4:return t?dte:cte;case 5:return t?hte:pte;case 6:return t?mte:fte;case 7:return t?bte:gte;case 8:return t?vte:yte;case 9:return t?wte:xte;case 10:return t?Nte:Cte;case 11:return ute;case 12:return t?Ste:Ite;case 14:return t?Ate:_te;case 15:return Tw("max",t);case 16:return Tw("min",t);case 13:return t?$te:Tte;case 17:return rte;case 18:return ate;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Ete="return abs(a);",Rte="return ceil(a);",Dte="return cos(a);",Fte=`
+  `}function ec(e,t){switch(e){case 0:return ote;case 1:return ste;case 2:return lte;case 3:return ite;case 4:return t?dte:cte;case 5:return t?hte:pte;case 6:return t?mte:fte;case 7:return t?bte:gte;case 8:return t?vte:yte;case 9:return t?wte:xte;case 10:return t?Nte:Cte;case 11:return ute;case 12:return t?Ste:Ite;case 14:return t?Ate:_te;case 15:return Nw("max",t);case 16:return Nw("min",t);case 13:return t?$te:Tte;case 17:return rte;case 18:return ate;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Ete="return abs(a);",Rte="return ceil(a);",Dte="return cos(a);",Fte=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
 `,Ote="return exp(a) - 1.0;",Pte="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",zte=`
@@ -5021,7 +5019,7 @@ return a / b;`,rZ=`
 `,Mte="return exp(a);",Lte="return floor(a);",Bte="return a;",Vte=`if (a < 0.0) { return 1.0/0.0; }
   return log(a);`,Wte="return f32(!(a >= 1.0));",Ute="return -a;",Gte="return (a < 0.0) ? b * a : a;",Hte="if (a < 0.0) { return uniforms.alpha * a; } return a;",qte="if(a < 0.0) { return 0.0; } return a;",jte="return clamp(a, 0.0, 6.0);",Kte="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Xte=`
   var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
-  let isNaN = isNanCustomVec4(a);
+  let isNaN = isnanVec4(a);
 
   if (isNaN.r) {
     resFloat.r = a.r;
@@ -5042,7 +5040,7 @@ return a / b;`,rZ=`
 `,ene="return sqrt(a);",tne="return a * a;",nne=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,sne="return f32(i32((a)));";function Gi(e,t){switch(e){case 0:return Ete;case 2:return Dte;case 3:return Fte;case 1:return Rte;case 4:return t?zte:Pte;case 5:return Mte;case 6:return Ote;case 7:return Lte;case 8:return Bte;case 9:return Vte;case 10:return Wte;case 11:return Ute;case 12:return Gte;case 15:return Hte;case 13:return t?Xte:qte;case 14:return t?Kte:jte;case 16:return Yte;case 19:return Qte;case 17:return Zte;case 18:return Jte;case 20:return ene;case 21:return tne;case 22:return nne;case 23:return sne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e==="linear")return Gi(8);if(e==="relu")return Gi(13,t);if(e==="elu")return Gi(4,t);if(e==="relu6")return Gi(14,t);if(e==="prelu")return tc(14,t);if(e==="sigmoid")return Gi(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function c2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
+`,sne="return f32(i32((a)));";function Ui(e,t){switch(e){case 0:return Ete;case 2:return Dte;case 3:return Fte;case 1:return Rte;case 4:return t?zte:Pte;case 5:return Mte;case 6:return Ote;case 7:return Lte;case 8:return Bte;case 9:return Vte;case 10:return Wte;case 11:return Ute;case 12:return Gte;case 15:return Hte;case 13:return t?Xte:qte;case 14:return t?Kte:jte;case 16:return Yte;case 19:return Qte;case 17:return Zte;case 18:return Jte;case 20:return ene;case 21:return tne;case 22:return nne;case 23:return sne;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function Zs(e,t=!1){if(e===null)return null;if(e==="linear")return Ui(8);if(e==="relu")return Ui(13,t);if(e==="elu")return Ui(4,t);if(e==="relu6")return Ui(14,t);if(e==="prelu")return ec(14,t);if(e==="sigmoid")return Ui(19);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function c2(e,t,n,s){return w.assert(s%4===0&&e[0]===4,()=>"tileInner must be divisible by 4. And ColPerThread must be 4"),`
   var<workgroup> mm_Asub : array<array<vec4<f32>, ${s/e[0]}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n/e[0]}>, ${s}>;
 
@@ -5147,7 +5145,7 @@ return a / b;`,rZ=`
         }
       }
       ${c2(this.elementsPerThread,this.tileAOuter,this.tileBOuter,this.tileInner)}
-    `}};function Nv(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
+    `}};function Cv(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
     var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
     var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
     ${Rr()}
@@ -5316,7 +5314,7 @@ return a / b;`,rZ=`
         ${s}
         setOutputAtCoords(batch, row, col, value);
       }
-      ${this.outputShape[1]>1?Nv([this.workPerThread,this.workPerThread,1],this.workGroupSize):ane(this.workGroupSize)}
+      ${this.outputShape[1]>1?Cv([this.workPerThread,this.workPerThread,1],this.workGroupSize):ane(this.workGroupSize)}
     `}};function ine(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
     ${Rr()}
@@ -5498,10 +5496,10 @@ return a / b;`,rZ=`
         }
       }
       ${une(this.workGroupSize)}
-    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var cne={kernelName:Eo,backendName:"webgpu",kernelFunc:Me};function Tv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=jo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new one([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new lne(k,C,[P,h,f],a,u,i):R?F=new rne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new d2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[T,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}],W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function dne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Tv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var pne={kernelName:na,backendName:"webgpu",kernelFunc:dne},$w=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+    `}};function Me(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a),o=w.sizeFromShape(i);return w.assert(a===o,()=>`The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var cne={kernelName:Ao,backendName:"webgpu",kernelFunc:Me};function Nv({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,c=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[l-1]:e.shape[l-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),b=w.sizeFromShape(m),y=w.sizeFromShape(g),x=qo.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);w.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let k=n?[b,p,h]:[b,h,p],C=s?[y,f,d]:[y,d,f],T=Me({inputs:{x:e},backend:r,attrs:{shape:k}}),E=Me({inputs:{x:t},backend:r,attrs:{shape:C}}),A=[T,E],P=Math.max(b,y),R=p%4===0&&f%4===0&&!n&&!s&&f>=32,F;h*f<=32?F=new one([P,h,f],n,s,a,u,i):!n&&!s&&(h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h))?F=new lne(k,C,[P,h,f],a,u,i):R?F=new rne(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,u,i):F=new d2(k,[P,h,f],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,u,i);let $=[T,E];a&&$.push(a),i&&$.push(i);let z=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}],W=r.runWebGPUProgram(F,$,e.dtype,z),q=Me({inputs:{x:W},backend:r,attrs:{shape:x}});A.push(W);for(let K of A)r.disposeData(K.dataId);return q}function dne(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s;return Nv({a:r,b:a,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:c})}var pne={kernelName:na,backendName:"webgpu",kernelFunc:dne},Tw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
-        ${tc(this.op,!1)}
+        ${ec(this.op,!1)}
       }
 
       ${Ue()}
@@ -5517,7 +5515,7 @@ return a / b;`,rZ=`
          let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
          let b = getBByOutputCoords(coords);`;return`
         fn binaryOperation(a : f32, b : f32) -> f32 {
-          ${tc(this.op,!1)}
+          ${ec(this.op,!1)}
         }
         var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
         ${Ue()}
@@ -5542,7 +5540,7 @@ return a / b;`,rZ=`
         }
         `}},fne=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
       fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
-        ${tc(this.op,this.isVec4)}
+        ${ec(this.op,this.isVec4)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -5553,7 +5551,7 @@ return a / b;`,rZ=`
       }
     `}},p2=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
       fn binaryOperation(a : f32, b : f32) -> f32 {
-        ${tc(this.op,!1)}
+        ${ec(this.op,!1)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -5562,9 +5560,9 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, binaryOperation(a, b));
         }
       }
-      `}};function _w(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new fne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new hne(e,t,n,a):new p2(e,t,n)}function Zn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var mne={kernelName:Ma,backendName:"webgpu",kernelFunc:Zn};function lu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=Zn({inputs:{x:s},backend:n}),u=Zn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var gne={kernelName:jd,backendName:"webgpu",kernelFunc:lu},nc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+      `}};function $w(e,t,n){if(w.arraysEqual(t,n)&&w.sizeFromShape(t)%4===0)return new fne(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new hne(e,t,n,a):new p2(e,t,n)}function Zn(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var mne={kernelName:Ma,backendName:"webgpu",kernelFunc:Zn};function uu(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),i=n.tensorMap.get(a.dataId),o=Zn({inputs:{x:s},backend:n}),u=Zn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},a}var gne={kernelName:qd,backendName:"webgpu",kernelFunc:uu},tc=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${Gi(this.op,!1)}
+        ${Ui(this.op,!1)}
       }
       ${Ue()}
         if (index < uniforms.size) {
@@ -5572,7 +5570,7 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new nc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function fn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=_w(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],yn(b.dtype,y.dtype))});else{let g=new $w(17,i.shape,o.shape),b=new $w(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=lu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||yn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?N.fromUint8ToStringArray(p):p,f=i.dtype==="string"?N.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=_w(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:bne,ceilImpl:yne,concatImpl:vne,equalImpl:xne,expImpl:wne,expm1Impl:kne,floorImpl:Ine,gatherNdImpl:Sne,gatherV2Impl:Cne,greaterEqualImpl:Nne,greaterImpl:Tne,lessEqualImpl:$ne,lessImpl:_ne,logImpl:Ane,maxImpl:Ene,maximumImpl:Rne,minimumImpl:Dne,multiplyImpl:Fne,negImpl:One,notEqualImpl:Pne,prodImpl:zne,rangeImpl:Mne,rsqrtImpl:Lne,simpleAbsImpl:Bne,sliceImpl:Vne,stridedSliceImpl:Wne,stringNGramsImpl:Une,subImpl:Gne,tileImpl:Hne,topKImpl:qne,transposeImpl:jne,uniqueImpl:kpe}=Qy,Kne=jt({opType:0,cpuKernelImpl:Bne}),Xne={kernelName:io,backendName:"webgpu",kernelFunc:Kne},Yne=fn({opSnippet:1,cpuKernelImpl:bne,supportsComplex:!0}),Qne={kernelName:kr,backendName:"webgpu",kernelFunc:Yne},Zne=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
+      `}};function jt({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,i=r,o=n||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.tensorMap.get(a.dataId),c=t(l.values,o);return i.makeTensorInfo(a.shape,o,c)}let u=new tc(a.shape,e);return i.runWebGPUProgram(u,[a],o)}}function fn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:i,b:o}=r,u=a;if(n&&i.dtype==="complex64"){let p=u.tensorMap.get(i.dataId),d=u.tensorMap.get(o.dataId),h,f;if(e!==0)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[b,y]=g,v={dataId:b.dataId,dtype:b.dtype,shape:i.shape},x={dataId:y.dataId,dtype:y.dtype,shape:o.shape},k=$w(e,i.shape,o.shape);return u.runWebGPUProgram(k,[v,x],yn(b.dtype,y.dtype))});else{let g=new Tw(17,i.shape,o.shape),b=new Tw(18,i.shape,o.shape),y=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape}];h=u.runWebGPUProgram(g,y,"float32"),f=u.runWebGPUProgram(b,y,"float32")}let m=uu({inputs:{real:h,imag:f},backend:u});return u.disposeData(h.dataId),u.disposeData(f.dataId),m}let l=s||yn(i.dtype,o.dtype);if((i.dtype==="string"||o.dtype==="string"||u.shouldExecuteOnCPU([i,o]))&&t!=null){let p=u.tensorMap.get(i.dataId).values,d=u.tensorMap.get(o.dataId).values,h=i.dtype==="string"?N.fromUint8ToStringArray(p):p,f=i.dtype==="string"?N.fromUint8ToStringArray(d):d,[m,g]=t(i.shape,o.shape,h,f,l);return u.makeTensorInfo(g,l,m)}let c=$w(e,i.shape,o.shape);return u.runWebGPUProgram(c,[i,o],l)}}var{addImpl:bne,ceilImpl:yne,concatImpl:vne,equalImpl:xne,expImpl:wne,expm1Impl:kne,floorImpl:Ine,gatherNdImpl:Sne,gatherV2Impl:Cne,greaterEqualImpl:Nne,greaterImpl:Tne,lessEqualImpl:$ne,lessImpl:_ne,logImpl:Ane,maxImpl:Ene,maximumImpl:Rne,minimumImpl:Dne,multiplyImpl:Fne,negImpl:One,notEqualImpl:Pne,prodImpl:zne,rangeImpl:Mne,rsqrtImpl:Lne,simpleAbsImpl:Bne,sliceImpl:Vne,stridedSliceImpl:Wne,stringNGramsImpl:Une,subImpl:Gne,tileImpl:Hne,topKImpl:qne,transposeImpl:jne,uniqueImpl:kpe}=Yy,Kne=jt({opType:0,cpuKernelImpl:Bne}),Xne={kernelName:ao,backendName:"webgpu",kernelFunc:Kne},Yne=fn({opSnippet:1,cpuKernelImpl:bne,supportsComplex:!0}),Qne={kernelName:kr,backendName:"webgpu",kernelFunc:Yne},Zne=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
       ${Ue()}
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
@@ -5635,7 +5633,7 @@ return a / b;`,rZ=`
         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
             k = k + i32(workGroupSizeX)) {
           let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
-          if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {
+          if (!isnan(candidate) && candidate ${this.op} bestValue) {
             bestValue = candidate;
             bestIndex = k;
           }
@@ -5667,7 +5665,7 @@ return a / b;`,rZ=`
     `}},tse=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       let TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${kv()}
+      ${wv()}
       fn main(@builtin(local_invocation_id) localId : vec3<u32>,
               @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5699,7 +5697,7 @@ return a / b;`,rZ=`
           }
         }
       }
-    `}};function sse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=jne(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new tse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new nse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var rse={kernelName:ci,backendName:"webgpu",kernelFunc:vi};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var ise={kernelName:wa,backendName:"webgpu",kernelFunc:ase};function ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var use={kernelName:ol,backendName:"webgpu",kernelFunc:ose},f2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+    `}};function sse(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function vi(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,i=n,o=r.shape.length,u=new Array(o);for(let c=0;c<u.length;c++)u[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=i.tensorMap.get(r.dataId).values,d=jne(p,r.shape,r.dtype,a,u);return n.makeTensorInfo(u,r.dtype,d)}if(r.shape.length===2&&w.arraysEqual(a,[1,0])){let c=new tse(r.shape,a);return i.runWebGPUProgram(c,[r],r.dtype)}let l=new nse(r.shape,a);return i.runWebGPUProgram(l,[r],r.dtype)}var rse={kernelName:ci,backendName:"webgpu",kernelFunc:vi};function ase(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],"max"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var ise={kernelName:wa,backendName:"webgpu",kernelFunc:ase};function ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,i=w.parseAxisParam(a,r.shape),o=N.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=vi({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=N.getInnerMostAxes(i.length,u.shape.length)),N.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let c=new h2(u.shape,i[0],"min"),p=[{type:"int32",data:[i[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[u],"int32",p);return l.forEach(h=>n.disposeData(h.dataId)),d}var use={kernelName:il,backendName:"webgpu",kernelFunc:ose},f2=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
       ${Ue()}
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -5747,7 +5745,7 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, value);
         }
       }
-    `}};function lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new m2(c):(p=new f2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var cse={kernelName:ka,backendName:"webgpu",kernelFunc:lse};function dse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Tv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var pse={kernelName:Ia,backendName:"webgpu",kernelFunc:dse},hse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=fse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${jm[a]} = uniforms.start[${a}] + coords.${jm[a]};`),`
+    `}};function lse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u);if(c.filterWidth===1&&c.filterHeight===1&&w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});let p,d=[{type:"int32",data:[c.strideHeight,c.strideWidth]}];return c.filterHeight===1&&c.filterWidth===1?p=new m2(c):(p=new f2(c,"avg"),d.push({type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]})),n.runWebGPUProgram(p,[r],r.dtype,d)}var cse={kernelName:ka,backendName:"webgpu",kernelFunc:lse};function dse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;return Nv({a:r,b:a,transposeA:i,transposeB:o,backend:n})}var pse={kernelName:Ia,backendName:"webgpu",kernelFunc:dse},hse=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Wt(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=Wt(this.rank),t=fse(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${qm[a]} = uniforms.start[${a}] + coords.${qm[a]};`),`
       ${Ue()}
         if (index < uniforms.size) {
           var sourceLoc : ${e};
@@ -5757,13 +5755,13 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},jm=["x","y","z","w","u","v"];function fse(e){if(e===1)return"sourceLoc";if(e<=6)return jm.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function cu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=Vne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new hse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var mse={kernelName:Po,backendName:"webgpu",kernelFunc:cu},gse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=cu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},bse={kernelName:oo,backendName:"webgpu",kernelFunc:gse},g2=fn({opSnippet:10,dtype:"bool",cpuKernelImpl:Pne}),yse={kernelName:So,backendName:"webgpu",kernelFunc:g2};function sc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.real},backend:n})}var vse={kernelName:np,backendName:"webgpu",kernelFunc:sc};function xse(e,t){let n=new nc(e.shape,23),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Km(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Zn({inputs:{x:r},backend:n});let i=$t(r.shape),o=Km({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=lu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=sc({inputs:{input:r},backend:n}),o=Km({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Zn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return xse(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=g2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var wse={kernelName:Sa,backendName:"webgpu",kernelFunc:Km},kse=jt({opType:1,cpuKernelImpl:yne}),Ise={kernelName:Ca,backendName:"webgpu",kernelFunc:kse},Sse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+    `}},qm=["x","y","z","w","u","v"];function fse(e){if(e===1)return"sourceLoc";if(e<=6)return qm.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function lu(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:i}=s,[o,u]=wt.parseSliceParams(r,a,i);if(wt.assertParamsValid(r,o,u),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=Vne(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}if(w.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);let l=new hse(o,u),c=[{type:"int32",data:o}];return n.runWebGPUProgram(l,[r],r.dtype,c)}var mse={kernelName:Oo,backendName:"webgpu",kernelFunc:lu},gse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s;w.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((y,v)=>y*v),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=[],f=Me({inputs:{x:r},backend:n,attrs:{shape:u}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:l}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:c}}),b=lu({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(y=>n.disposeData(y.dataId)),b},bse={kernelName:io,backendName:"webgpu",kernelFunc:gse},g2=fn({opSnippet:10,dtype:"bool",cpuKernelImpl:Pne}),yse={kernelName:Io,backendName:"webgpu",kernelFunc:g2};function nc(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.real},backend:n})}var vse={kernelName:tp,backendName:"webgpu",kernelFunc:nc};function xse(e,t){let n=new tc(e.shape,23),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Zn({inputs:{x:r},backend:n});let i=$t(r.shape),o=jm({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=uu({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeData(o.dataId),u}if(r.dtype==="complex64"){let i=nc({inputs:{input:r},backend:n}),o=jm({inputs:{x:i},backend:n,attrs:{dtype:a}});return n.disposeData(i.dataId),o}if(!w.hasEncodingLoss(r.dtype,a)){let i=Zn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:a}}if(a==="int32")return xse(r,n);if(a==="bool"){let i=n.makeTensorInfo([],"bool",w.getTypedArrayFromDType("bool",1)),u=g2({inputs:{a:r,b:i},backend:n});return n.disposeData(i.dataId),u}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var wse={kernelName:Sa,backendName:"webgpu",kernelFunc:jm},kse=jt({opType:1,cpuKernelImpl:yne}),Ise={kernelName:Ca,backendName:"webgpu",kernelFunc:kse},Sse=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
           var clampedValue : vec4<f32>;
           for (var i = 0; i < 4; i = i + 1) {
-            if (isNanCustom(value[i])) {
+            if (isnan(value[i])) {
               clampedValue[i] = value[i];
             } else {
               clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
@@ -5777,7 +5775,7 @@ return a / b;`,rZ=`
       ${Ue()}
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
-          if (isNanCustom(value)) {
+          if (isnan(value)) {
             setOutputAtIndex(index, value);
             return;
           }
@@ -5798,7 +5796,7 @@ return a / b;`,rZ=`
           }
         }
       }
-    `}};function Zp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var _se={kernelName:Zd,backendName:"webgpu",kernelFunc:Zp};function Xm(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>sc({inputs:{input:y},backend:n})),f=e.map(y=>Zp({inputs:{input:y},backend:n})),m=Xm(h,t,n),g=Xm(f,t,n),b=lu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=vne(f,m,s,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Ase(e,t,n),o=a.map(h=>h.shape),u=new $se(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Ase(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function b2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Zn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return N.assertParamsConsistent(u,a),Xm(o,a,n)}var Ese={kernelName:uo,backendName:"webgpu",kernelFunc:b2},Rse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
+    `}};function Qp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Zn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var _se={kernelName:Qd,backendName:"webgpu",kernelFunc:Qp};function Km(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(y=>nc({inputs:{input:y},backend:n})),f=e.map(y=>Qp({inputs:{input:y},backend:n})),m=Km(h,t,n),g=Km(f,t,n),b=uu({inputs:{real:m,imag:g},backend:n});return h.forEach(y=>n.disposeData(y.dataId)),f.forEach(y=>n.disposeData(y.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),b}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(x=>{let k=w.sizeFromShape(x.shape.slice(t));return Me({inputs:{x},backend:n,attrs:{shape:[-1,k]}})}),f=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),m=N.computeOutShape(h.map(x=>x.shape),1),g=h[0].shape[0]===1,b=vne(f,m,s,g),y=N.computeOutShape(e.map(x=>x.shape),t),v=n.makeTensorInfo(y,s,b);return h.forEach(x=>n.disposeData(x.dataId)),v}let{tensors2D:a,outShape:i}=Ase(e,t,n),o=a.map(h=>h.shape),u=new $se(o),l=[],c=new Array(o.length-1);if(c.length>0){c[0]=o[0][1],l.push({type:"int32",data:[c[0]]});for(let h=1;h<c.length;h++)c[h]=c[h-1]+o[h][1],l.push({type:"int32",data:[c[h]]})}let p=n.runWebGPUProgram(u,a,a[0].dtype,l);a.forEach(h=>n.disposeData(h.dataId));let d=Me({inputs:{x:p},backend:n,attrs:{shape:i}});return n.disposeData(p.dataId),d}function Ase(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Me({inputs:{x:a},backend:n,attrs:{shape:[w.sizeFromShape(a.shape.slice(0,t)),w.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function b2(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=w.parseAxisParam(r,t[0].shape)[0],i=N.computeOutShape(t.map(l=>l.shape),a);if(w.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>w.sizeFromShape(l.shape)>0);if(o.length===1)return Zn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return N.assertParamsConsistent(u,a),Km(o,a,n)}var Ese={kernelName:oo,backendName:"webgpu",kernelFunc:b2},Rse=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
       inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
     ${Ue()}
 
@@ -5927,7 +5925,7 @@ return a / b;`,rZ=`
           }
         }
         ${e}
-      `}},Fse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Iv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Sv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Nv(this.elementsPerThread,this.workGroupSize),t=`
+      `}},Fse=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Iv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;w.assert(n%this.workGroupSize[0]===0&&n%this.workGroupSize[1]===0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],o=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[Ks(s,[a,o]),Ks(r,[o,i])]}getUserCode(){let e=Cv(this.elementsPerThread,this.workGroupSize),t=`
     let outRow = row / uniforms.outShape[2];
     let outCol = row % uniforms.outShape[2];
 
@@ -6037,7 +6035,7 @@ return a / b;`,rZ=`
 
         writeResult(batch, coords[1], coords[2], outChannel, acc);
       }
-    `}};function Pse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Tv({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function zse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,C=[k,x],T=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Rse(C,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,C[0],C[1]]}});A.push(z),A.push(W);let q=[1,C[0],C[1]],K=new d2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a);let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function y2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Pse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return zse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new Ose(n,u,o,l);else{h?c=new Dse(n,u,o,l):c=new Fse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),s.runWebGPUProgram(c,g,e.dtype,m)}function Mse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return y2({x:r,filter:a,convInfo:d,backend:s})}var Lse={kernelName:Na,backendName:"webgpu",kernelFunc:Mse},Bse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Iv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Sv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
+    `}};function Pse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=n.dataFormat==="channelsLast",c=!1,p=!1,d=n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",h,f;if(d){let b=n.inHeight*n.inWidth*n.inChannels;h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,b]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,b,n.outChannels]}})}else{let b=l?u[0]*u[1]*u[2]:u[0]*u[2]*u[3];h=Me({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),f=Me({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}})}let m=Nv({a:h,b:f,transposeA:c,transposeB:p,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i}),g=Me({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function zse({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:c,strideWidth:p,strideHeight:d,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:b,dataFormat:y}=n,v=y==="channelsLast",x=u*l*c,k=m*f,C=[k,x],T=!1,E=!1,A=[],P=Me({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),R=Me({inputs:{x:t},backend:s,attrs:{shape:[1,x,-1]}});A.push(P),A.push(R);let F=new Rse(C,v),$=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[p,d]},{type:"int32",data:[g,b]},{type:"int32",data:[f]},{type:"int32",data:[c*u]},{type:"int32",data:[c]}],z=s.runWebGPUProgram(F,[P],P.dtype,$),W=Me({inputs:{x:z},backend:s,attrs:{shape:[1,C[0],C[1]]}});A.push(z),A.push(W);let q=[1,C[0],C[1]],K=new d2(q,[1,k,n.outChannels],X().get("WEBGPU_MATMUL_WORK_PER_THREAD"),T,E,r,o,a),Y=q[1],Z=q[2],te=n.outChannels,ee=[{type:"int32",data:[Y]},{type:"int32",data:[te]},{type:"int32",data:[Z]}],se=[W,R];r&&se.push(r),a&&se.push(a);let ne=s.runWebGPUProgram(K,se,W.dtype,ee),oe=v?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],re=Me({inputs:{x:ne},backend:s,attrs:{shape:oe}});A.push(ne);for(let le of A)s.disposeData(le.dataId);return re}function y2({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:o=null}){let u=r!=null,l=a!=null,c;if(n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Pse({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:o,preluActivationWeights:a,leakyreluAlpha:i});if(X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&e.shape[0]===1)return zse({x:e,filter:t,convInfo:n,backend:s,bias:r,preluActivationWeights:a,leakyreluAlpha:i,activation:o});let d=X().getBool("WEBGPU_USE_NAIVE_CONV2D"),h=(n.inChannels%4===0||n.inChannels===3&&n.padInfo.type==="VALID")&&n.outChannels%4===0&&n.outChannels>=32,f=[n.padInfo.top,n.padInfo.left],m=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]}];if(d)c=new Ose(n,u,o,l);else{h?c=new Dse(n,u,o,l):c=new Fse(n,u,o,l);let b=n.outShape[1]*n.outShape[2],y=n.outShape[3],v=n.filterHeight*n.filterWidth*n.inShape[3];m.push({type:"int32",data:[b]},{type:"int32",data:[y]},{type:"int32",data:[v]})}let g=[e,t];return u&&g.push(r),l&&g.push(a),s.runWebGPUProgram(c,g,e.dtype,m)}function Mse(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:c}=n,p=N.convertConv2DDataFormat(u),d=N.computeConv2DInfo(r.shape,a.shape,i,l,o,c,!1,p);return y2({x:r,filter:a,convInfo:d,backend:s})}var Lse={kernelName:Na,backendName:"webgpu",kernelFunc:Mse},Bse=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=kv(this.dispatchLayout,this.outputShape),this.elementsPerThread=Iv(this.dispatchLayout,this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
     fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
       var batch = i32(globalId.z);
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
@@ -6090,7 +6088,7 @@ return a / b;`,rZ=`
       result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
     }
 
-    ${Nv(this.elementsPerThread,this.workGroupSize)}
+    ${Cv(this.elementsPerThread,this.workGroupSize)}
   `}},Vse=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
     ${Ue()} {
       if(index < uniforms.size) {
@@ -6196,7 +6194,7 @@ return a / b;`,rZ=`
         }
       }
     }
-    `}},Xse=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Kse(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},Yse={kernelName:co,backendName:"webgpu",kernelFunc:Xse},Qse=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+    `}},Xse=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=s,c=new Kse(r.shape[3],a.shape,o,u),p=[{type:"float32",data:[l]}];return n.runWebGPUProgram(c,[r,a,i],"float32",p)},Yse={kernelName:lo,backendName:"webgpu",kernelFunc:Xse},Qse=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6216,7 +6214,7 @@ return a / b;`,rZ=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new Qse(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Jse={kernelName:po,backendName:"webgpu",kernelFunc:Zse},v2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=[{type:"int32",data:[a]}],g=new Qse(f,i);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Jse={kernelName:co,backendName:"webgpu",kernelFunc:Zse},v2=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),w.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=Zs(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
           let b = getPreluActivationWeightsByOutputCoords(outCoord);
           ${r}
         }`:e=`
@@ -6226,7 +6224,7 @@ return a / b;`,rZ=`
         `,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasByOutputCoords(coords);":"";return`
       ${e}
 
-      ${kv()}
+      ${wv()}
       fn main(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = 0;
         let r = i32(globalId.x);
@@ -6371,9 +6369,9 @@ return a / b;`,rZ=`
         writeResult(batch, coords[1], coords[2], d2, dotProd);
       }
     `}};function ere(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=s,c=u;c==null&&(c=[1,1]);let p=N.computeConv2DInfo(r.shape,a.shape,i,c,o,l,!0),d=[{type:"int32",data:[p.padInfo.top,p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]},{type:"int32",data:[p.inHeight,p.inWidth]}],h;return p.batchSize===1&&p.inHeight===p.outHeight&&p.inWidth===p.outWidth&&p.strideHeight===1&&p.strideWidth===1&&p.filterHeight===p.filterWidth&&p.inChannels===p.outChannels&&p.filterHeight===3&&p.inChannels%4===0?h=new v2(p):(h=new x2(p),d.push({type:"int32",data:[p.filterHeight]},{type:"int32",data:[p.filterWidth]},{type:"int32",data:[p.outChannels/p.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,d)}var tre={kernelName:Aa,backendName:"webgpu",kernelFunc:ere},w2=fn({opSnippet:0,cpuKernelImpl:Fne,supportsComplex:!0}),nre={kernelName:Ka,backendName:"webgpu",kernelFunc:w2},sre=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
-         if (isNanCustom(candidate)) {
+         if (isnan(candidate)) {
           bestValue = uniforms.NAN;
-         } else if (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
+         } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
            {  bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputAtIndex(outputIndex, bestValue);";return`
        fn DIV_CEIL(a : u32, b : u32) -> u32 {
         return ((a - 1u) / b + 1u);
@@ -6418,13 +6416,13 @@ return a / b;`,rZ=`
           ${n}
         }
        }
-     `}};function rc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=vi({inputs:{x:e},attrs:{perm:l},backend:r}),u=N.getInnerMostAxes(u.length,a),i.push(c)),N.assertAxesAreInnerMostDims(s,u,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=N.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Ene(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=zne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":cp(e.dtype),x=[{type:"int32",data:[m]}],k=new sre(y,s),C=r.runWebGPUProgram(k,[c],v,x);i.push(C),f=Me({inputs:{x:C},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function $v(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"sum",n)}var rre={kernelName:ai,backendName:"webgpu",kernelFunc:$v};function are(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=vi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=w2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=$v({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var ire={kernelName:Qd,backendName:"webgpu",kernelFunc:are},ore=jt({opType:4}),ure={kernelName:Ra,backendName:"webgpu",kernelFunc:ore},lre=fn({opSnippet:4,dtype:"bool",cpuKernelImpl:xne}),cre={kernelName:ho,backendName:"webgpu",kernelFunc:lre},k2=jt({opType:5,cpuKernelImpl:wne,dtype:"float32"}),dre={kernelName:Da,backendName:"webgpu",kernelFunc:k2};function Ym(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var pre={kernelName:fo,backendName:"webgpu",kernelFunc:Ym},hre=jt({opType:6,cpuKernelImpl:kne}),fre={kernelName:mo,backendName:"webgpu",kernelFunc:hre},mre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+     `}};function sc(e,t,n,s,r){let a=e.shape.length,i=[],o=w.parseAxisParam(t,e.shape),u=o,l=N.getAxesPermutation(u,a),c=e;l!=null&&(c=vi({inputs:{x:e},attrs:{perm:l},backend:r}),u=N.getInnerMostAxes(u.length,a),i.push(c)),N.assertAxesAreInnerMostDims(s,u,a);let[p,d]=N.computeOutAndReduceShapes(c.shape,u),h=p;n&&(h=N.expandShapeToKeepDim(p,o));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=Ene(m,w.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:b,outShape:y,outDtype:v}=zne(c.shape,c.dtype,m,u);f=r.makeTensorInfo(y,v,b);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=w.sizeFromShape(d),b=w.sizeFromShape(c.shape)/m,y={windowSize:m,inSize:m,batchSize:b,outSize:1},v=s==="mean"?"float32":lp(e.dtype),x=[{type:"int32",data:[m]}],k=new sre(y,s),C=r.runWebGPUProgram(k,[c],v,x);i.push(C),f=Me({inputs:{x:C},attrs:{shape:h},backend:r})}return i.forEach(m=>r.disposeData(m.dataId)),f}function Tv(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"sum",n)}var rre={kernelName:ai,backendName:"webgpu",kernelFunc:Tv};function are(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:i,summedDims:o,idDims:u}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(i.length,u,a);let{path:l,steps:c}=N.getEinsumComputePath(o,u),p=c.length,d=null,h=i.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:b,expandDims:y}=N.getEinsumPermutation(h,u[g]),v;N.isIdentityPermutation(b)?v=a[g]:(v=vi({inputs:{x:a[g]},backend:n,attrs:{perm:b}}),f.push(v));let x=v.shape.slice();for(let k=0;k<y.length;++k)x.splice(y[k],0,1);w.arraysEqual(v.shape,x)||(v=Me({inputs:{x:v},backend:n,attrs:{shape:x}}),f.push(v)),d===null?d=v:(d=w2({inputs:{a:v,b:d},backend:n}),f.push(d))}m<p-1&&(l[m]>=0&&(d=Tv({inputs:{x:d},backend:n,attrs:{axis:l[m]-(i.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var ire={kernelName:Yd,backendName:"webgpu",kernelFunc:are},ore=jt({opType:4}),ure={kernelName:Ra,backendName:"webgpu",kernelFunc:ore},lre=fn({opSnippet:4,dtype:"bool",cpuKernelImpl:xne}),cre={kernelName:po,backendName:"webgpu",kernelFunc:lre},k2=jt({opType:5,cpuKernelImpl:wne,dtype:"float32"}),dre={kernelName:Da,backendName:"webgpu",kernelFunc:k2};function Xm(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,i=a.shape.length,o=a.shape.slice(),u=r;return r<0&&(w.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),Me({inputs:{x:a},backend:s,attrs:{shape:o}})}var pre={kernelName:ho,backendName:"webgpu",kernelFunc:Xm},hre=jt({opType:6,cpuKernelImpl:kne}),fre={kernelName:fo,backendName:"webgpu",kernelFunc:hre},mre=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
     ${Ue()}
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function du(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new mre(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var gre={kernelName:fl,backendName:"webgpu",kernelFunc:du},bre=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+  `}};function cu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||w.inferDtype(r),a==="string"){let i=w.getArrayFromDType(a,w.sizeFromShape(s));return i.fill(r),t.makeTensorInfo(s,a,i)}else{let i=new mre(s),o=[{type:"float32",data:[r]}];return t.runWebGPUProgram(i,[],a,o)}}var gre={kernelName:hl,backendName:"webgpu",kernelFunc:cu},bre=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
@@ -6433,7 +6431,7 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},yre={kernelName:go,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bre(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},vre=jt({opType:7,cpuKernelImpl:Ine}),xre={kernelName:Fa,backendName:"webgpu",kernelFunc:vre},wre=fn({opSnippet:12,dtype:"int32"}),kre={kernelName:Oa,backendName:"webgpu",kernelFunc:wre},Ire=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},I2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=Yee(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function S2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function Aw(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=S2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>I2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Sre={kernelName:fd,backendName:"webgpu",kernelFunc:Cre},Li;function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return Aw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Li==null&&(Li=document.createElement("canvas").getContext("2d")),Li.canvas.width=c,Li.canvas.height=p,Li.drawImage(r,0,0,c,p),r=Li.canvas),l||u||i||o)return Aw({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Nre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+    `}},yre={kernelName:mo,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new bre(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},vre=jt({opType:7,cpuKernelImpl:Ine}),xre={kernelName:Fa,backendName:"webgpu",kernelFunc:vre},wre=fn({opSnippet:12,dtype:"int32"}),kre={kernelName:Oa,backendName:"webgpu",kernelFunc:wre},Ire=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((i,o)=>({binding:o,resource:i}))})},I2=(e,t,n,s,r,a=!1)=>{let i={dtype:r.dtype,shape:r.shape},o=Yee(s,i,t,a),u=e.createShaderModule({code:o,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:u,entryPoint:"main"},label:t.constructor.name})};function S2(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(i=>i.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function _w(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:i}=s,o=w.sizeFromShape(r),u=w.computeStrides(r),l=n.makeTensorInfo(r,"int32"),c=n.getFromPixelsProgram(a?"import":"copyExternal");c.updateOutputShape(r);let p=[l.shape],d=[l.dtype,a?"import":"copyExternal"],h=S2(c,p,d),f=c.getLayout(n.device),m=n.getAndSavePipeline(h,()=>I2(n.device,c,f.pipelineLayout,[],l,!0));c.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:c.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(l.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let b=[o,i,...u,...c.dispatch];c.setUniform(n.device,b);let y;if(a){let v={source:t};y=n.device.importExternalTexture(v)}else y=c.inputTexture.createView();return n.runFromPixelsProgram(c,g.bufferInfo.buffer,f,y,l.dataId),l}var Sre={kernelName:hd,backendName:"webgpu",kernelFunc:Cre},Mi;function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,u=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,l=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a];if(X().getBool("WEBGPU_USE_IMPORT")&&i)return _w({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!0});if((i||o)&&(Mi==null&&(Mi=document.createElement("canvas").getContext("2d")),Mi.canvas.width=c,Mi.canvas.height=p,Mi.drawImage(r,0,0,c,p),r=Mi.canvas),l||u||i||o)return _w({externalImage:r,backend:n,attrs:s,outShape:d,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let b=h.length,y=0;for(let v=0;v<b;v++)v%4<a&&(f[y++]=h[v])}let m=n.makeTensorInfo(d,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Nre=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
       ${Ue()}
         if (index < uniforms.size)
         {
@@ -6460,14 +6458,14 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function Dre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Sne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Rre(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Fre={kernelName:yo,backendName:"webgpu",kernelFunc:Dre},Ore=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Pre(this.aShape,"i32");return`
+      `}};function Dre(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,i=a[a.length-1],o=w.sizeFromShape(s.shape),[u,l,c,p]=N.prepareAndValidate(s,r),d=Me({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=Me({inputs:{x:s},backend:n,attrs:{shape:[w.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),v=n.bufferSync(s),x=Sne(y,v,s.dtype,l,i,c,p,s.shape,o);return n.makeTensorInfo(u,s.dtype,x.values)}let f=new Rre(i,[l,c]),m=[{type:"int32",data:[i]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),b=Me({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),b}var Fre={kernelName:bo,backendName:"webgpu",kernelFunc:Dre},Ore=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=Pre(this.aShape,"i32");return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function Pre(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),C=n.tensorMap.get(d.dataId).values,T=De(d.shape,d.dtype,C),E=Cne(T,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Ore(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var zre={kernelName:bo,backendName:"webgpu",kernelFunc:C2},Mre=fn({opSnippet:5,cpuKernelImpl:Tne,dtype:"bool"}),Lre={kernelName:vo,backendName:"webgpu",kernelFunc:Mre},Bre=fn({opSnippet:6,dtype:"bool",cpuKernelImpl:Nne}),Vre={kernelName:za,backendName:"webgpu",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new nc(r.shape,15);return o.uniforms="alpha : f32;",n.runWebGPUProgram(o,[r],"float32",i)}var Ure={kernelName:La,backendName:"webgpu",kernelFunc:Wre},Gre=fn({opSnippet:7,dtype:"bool",cpuKernelImpl:_ne}),Hre={kernelName:xo,backendName:"webgpu",kernelFunc:Gre},qre=fn({opSnippet:8,dtype:"bool",cpuKernelImpl:$ne}),jre={kernelName:wo,backendName:"webgpu",kernelFunc:qre},Kre=jt({opType:9,cpuKernelImpl:Ane}),Xre={kernelName:Ba,backendName:"webgpu",kernelFunc:Kre},Yre=fn({opSnippet:9,dtype:"bool"}),Qre={kernelName:ko,backendName:"webgpu",kernelFunc:Yre},Zre=jt({opType:10}),Jre={kernelName:vl,backendName:"webgpu",kernelFunc:Zre};function N2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return rc(r,a,i,"max",n)}var eae={kernelName:Va,backendName:"webgpu",kernelFunc:N2},tae=fn({opSnippet:15,cpuKernelImpl:Rne}),nae={kernelName:Wa,backendName:"webgpu",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});p=new m2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new f2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var rae={kernelName:Ua,backendName:"webgpu",kernelFunc:sae};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return rc(r,i,a,"mean",n)}var iae={kernelName:Ga,backendName:"webgpu",kernelFunc:aae};function oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"min",n)}var uae={kernelName:Ha,backendName:"webgpu",kernelFunc:oae},lae=fn({opSnippet:16,cpuKernelImpl:Dne}),cae={kernelName:qa,backendName:"webgpu",kernelFunc:lae},dae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}};function Pre(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function C2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:i,batchDims:o}=s,u=w.parseAxisParam(i,r.shape)[0],l=N.segment_util.collectGatherOpShapeInfo(r,a,u,o),c=w.sizeFromShape(a.shape),p=[],d=Me({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=Me({inputs:{x:a},backend:n,attrs:{shape:[l.batchSize,c/l.batchSize]}});p.push(d),p.push(h);let f=[l.batchSize,l.outerSize,c/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let v=n.tensorMap.get(h.dataId).values,x=De(h.shape,h.dtype,v),C=n.tensorMap.get(d.dataId).values,T=De(d.shape,d.dtype,C),E=Cne(T,x,f);return p.forEach(A=>n.disposeData(A.dataId)),n.makeTensorInfo(l.outputShape,E.dtype,E.values)}let m=new Ore(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let b=Me({inputs:{x:g},backend:n,attrs:{shape:l.outputShape}});return p.forEach(y=>n.disposeData(y.dataId)),b}var zre={kernelName:go,backendName:"webgpu",kernelFunc:C2},Mre=fn({opSnippet:5,cpuKernelImpl:Tne,dtype:"bool"}),Lre={kernelName:yo,backendName:"webgpu",kernelFunc:Mre},Bre=fn({opSnippet:6,dtype:"bool",cpuKernelImpl:Nne}),Vre={kernelName:za,backendName:"webgpu",kernelFunc:Bre};function Wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,i=[{type:"float32",data:[a]}],o=new tc(r.shape,15);return o.uniforms="alpha : f32;",n.runWebGPUProgram(o,[r],"float32",i)}var Ure={kernelName:La,backendName:"webgpu",kernelFunc:Wre},Gre=fn({opSnippet:7,dtype:"bool",cpuKernelImpl:_ne}),Hre={kernelName:vo,backendName:"webgpu",kernelFunc:Gre},qre=fn({opSnippet:8,dtype:"bool",cpuKernelImpl:$ne}),jre={kernelName:xo,backendName:"webgpu",kernelFunc:qre},Kre=jt({opType:9,cpuKernelImpl:Ane}),Xre={kernelName:Ba,backendName:"webgpu",kernelFunc:Kre},Yre=fn({opSnippet:9,dtype:"bool"}),Qre={kernelName:wo,backendName:"webgpu",kernelFunc:Yre},Zre=jt({opType:10}),Jre={kernelName:yl,backendName:"webgpu",kernelFunc:Zre};function N2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:i}=s;return sc(r,a,i,"max",n)}var eae={kernelName:Va,backendName:"webgpu",kernelFunc:N2},tae=fn({opSnippet:15,cpuKernelImpl:Rne}),nae={kernelName:Wa,backendName:"webgpu",kernelFunc:tae};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:i,pad:o,dimRoundingMode:u}=s,l=1,c=N.computePool2DInfo(r.shape,a,i,l,o,u),p,d=[];if(c.filterHeight===1&&c.filterWidth===1){if(w.arraysEqual(c.inShape,c.outShape))return Zn({inputs:{x:r},backend:n});p=new m2(c),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]})}else p=new f2(c,"max"),d.push({type:"int32",data:[c.strideHeight,c.strideWidth]},{type:"int32",data:[c.padInfo.top,c.padInfo.left]},{type:"int32",data:[c.dilationHeight,c.dilationWidth]},{type:"int32",data:[c.inHeight,c.inWidth]},{type:"int32",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]});return n.runWebGPUProgram(p,[r],r.dtype,d)}var rae={kernelName:Ua,backendName:"webgpu",kernelFunc:sae};function aae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:i}=s;return sc(r,i,a,"mean",n)}var iae={kernelName:Ga,backendName:"webgpu",kernelFunc:aae};function oae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"min",n)}var uae={kernelName:Ha,backendName:"webgpu",kernelFunc:oae},lae=fn({opSnippet:16,cpuKernelImpl:Dne}),cae={kernelName:qa,backendName:"webgpu",kernelFunc:lae},dae=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,l)=>`uniforms.pad${l}[0]`).join(","),n=this.xShape.map((u,l)=>`uniforms.pad${l}[0] + uniforms.xShape${e>1?`[${l}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",i=Wt(e),o=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${i}(${t});
@@ -6484,7 +6482,7 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, getX(${o}));
         }
       }
-    `}},pae={kernelName:ja,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new dae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function hae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=One(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new nc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var fae={kernelName:Io,backendName:"webgpu",kernelFunc:hae};function mae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var gae={kernelName:Co,backendName:"webgpu",kernelFunc:mae};function bae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var yae={kernelName:No,backendName:"webgpu",kernelFunc:bae};function Ld(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=Ld({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var vae={kernelName:Ho,backendName:"webgpu",kernelFunc:Ld};function T2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=sc({inputs:{input:s},backend:n}),a=T2({inputs:{x:r},backend:n}),i=Zp({inputs:{input:s},backend:n}),o=Ld({inputs:{x:i},backend:n}),u=lu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return du({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xae={kernelName:To,backendName:"webgpu",kernelFunc:T2};function wae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Ym({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Ym({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=b2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var kae={kernelName:_o,backendName:"webgpu",kernelFunc:wae},Iae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+    `}},pae={kernelName:ja,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,i=n,o=r.map(c=>({type:"int32",data:[c[0],c[1]]})),u=new dae(s.shape,r,a);return i.runWebGPUProgram(u,[s],s.dtype,o)}};function hae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[i,o]=One(a.values,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,i)}let r=new tc(s.shape,11);return n.runWebGPUProgram(r,[s],s.dtype)}var fae={kernelName:ko,backendName:"webgpu",kernelFunc:hae};function mae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=s,l=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=xs.nonMaxSuppressionV3Impl(l,c,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var gae={kernelName:So,backendName:"webgpu",kernelFunc:mae};function bae(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=i,h=o,f=u,m=l,{selectedIndices:g,selectedScores:b}=xs.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([b.length],"float32",new Float32Array(b))]}var yae={kernelName:Co,backendName:"webgpu",kernelFunc:bae};function Md(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=Md({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var vae={kernelName:Go,backendName:"webgpu",kernelFunc:Md};function T2(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=nc({inputs:{input:s},backend:n}),a=T2({inputs:{x:r},backend:n}),i=Qp({inputs:{input:s},backend:n}),o=Md({inputs:{x:i},backend:n}),u=uu({inputs:{real:a,imag:o},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(i.dataId),n.disposeData(o.dataId),u}else return cu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var xae={kernelName:No,backendName:"webgpu",kernelFunc:T2};function wae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Xm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Xm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=b2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var kae={kernelName:$o,backendName:"webgpu",kernelFunc:wae},Iae=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Wt(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,i=e>1?"any(outC < start)":"outC < start",o=e>1?"any(outC >= end)":"outC >= end",u=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
       ${Ue()}
         if (index < uniforms.size) {
           let start = ${r};
@@ -6499,7 +6497,7 @@ return a / b;`,rZ=`
           }
         }
       }
-    `}},$2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Zn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return du({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Iae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Sae={kernelName:Xa,backendName:"webgpu",kernelFunc:$2},Cae=fn({opSnippet:13}),Nae={kernelName:Ya,backendName:"webgpu",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new p2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var $ae={kernelName:Qa,backendName:"webgpu",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return rc(r,a,i,"prod",n)}var Aae={kernelName:Ao,backendName:"webgpu",kernelFunc:_ae},Eae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Mne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Rae={kernelName:kl,backendName:"webgpu",kernelFunc:Eae},_2=fn({opSnippet:3}),Dae={kernelName:Ea,backendName:"webgpu",kernelFunc:_2},Fae=jt({opType:13}),Oae={kernelName:Za,backendName:"webgpu",kernelFunc:Fae},Pae=jt({opType:14}),zae={kernelName:ei,backendName:"webgpu",kernelFunc:Pae},Mae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+    `}},$2=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:i}=s;if(a.every(l=>w.arraysEqual(l,[0,0])))return Zn({inputs:{x:r},backend:n});if(w.sizeFromShape(r.shape)===0){let l=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return cu({backend:n,attrs:{shape:l,value:i,dtype:r.dtype}})}let o=[{type:"float32",data:[i]}];a.map(l=>o.push({type:"int32",data:[l[0],l[1]]}));let u=new Iae(r.shape,a);return n.runWebGPUProgram(u,[r],r.dtype,o)},Sae={kernelName:Xa,backendName:"webgpu",kernelFunc:$2},Cae=fn({opSnippet:13}),Nae={kernelName:Ya,backendName:"webgpu",kernelFunc:Cae};function Tae(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new p2(14,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var $ae={kernelName:Qa,backendName:"webgpu",kernelFunc:Tae};function _ae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:i}=s;return sc(r,a,i,"prod",n)}var Aae={kernelName:_o,backendName:"webgpu",kernelFunc:_ae},Eae=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=Mne(s,r,a,i);return t.makeTensorInfo([o.length],i,o)},Rae={kernelName:wl,backendName:"webgpu",kernelFunc:Eae},_2=fn({opSnippet:3}),Dae={kernelName:Ea,backendName:"webgpu",kernelFunc:_2},Fae=jt({opType:13}),Oae={kernelName:Za,backendName:"webgpu",kernelFunc:Fae},Pae=jt({opType:14}),zae={kernelName:ei,backendName:"webgpu",kernelFunc:Pae},Mae=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
       ${Ue()}
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
@@ -6573,7 +6571,7 @@ return a / b;`,rZ=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Vae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Uae={kernelName:Sl,backendName:"webgpu",kernelFunc:Wae},Gae=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
+    `}};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,c=a&&u>1?1:0,p=a&&l>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Vae(r.shape,u,l,i);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Uae={kernelName:Il,backendName:"webgpu",kernelFunc:Wae},Gae=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
           cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
         ${Ue()}
 
@@ -6595,7 +6593,7 @@ return a / b;`,rZ=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Hae={kernelName:qo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Gae(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},qae=jt({opType:16,cpuKernelImpl:Lne}),jae={kernelName:ti,backendName:"webgpu",kernelFunc:qae},Kae=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
+      `}},Hae={kernelName:Ho,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:i}=t,o=n,u=new Gae(s.shape,a),[l,c]=N.getImageCenter(i,s.shape[1],s.shape[2]),p=[{type:"float32",data:[l]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),o.runWebGPUProgram(u,[s],s.dtype,p)}},qae=jt({opType:16,cpuKernelImpl:Lne}),jae={kernelName:ti,backendName:"webgpu",kernelFunc:qae},Kae=class{constructor(e,t,n,s,r,a,i){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.dispatchLayout=Be(e),this.dispatch=_e(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;let o=Wt(r.length);this.uniforms=`sliceDim : i32; strides: ${o}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6632,7 +6630,7 @@ return a / b;`,rZ=`
 
          ${o}
         }
-      }`}};function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=du({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new Kae(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Yae={kernelName:Fo,backendName:"webgpu",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
+      }`}};function Xae(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:i}=s,{sliceRank:o,numUpdates:u,sliceSize:l,strides:c,outputSize:p}=N.calculateShapes(a,r,i),d=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=Me({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),f=Me({inputs:{x:a},backend:n,attrs:{shape:[u,l]}}),m=f.dtype,g=cu({backend:n,attrs:{shape:d,value:0,dtype:m}}),b=w.sizeFromShape(f.shape),y=[{type:"int32",data:[o]},{type:"int32",data:c},{type:"int32",data:[b]}],v=new Kae(f.shape,o,h.shape.length,f.shape.length,c,d,m),x=n.runWebGPUProgram(v,[f,h],m,y,g),k=Me({inputs:{x},backend:n,attrs:{shape:i}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(x.dataId),k}var Yae={kernelName:Do,backendName:"webgpu",kernelFunc:Xae},Qae=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let i=0;i<this.outputShape.length;i++)a.push(`${s[i]}`),i<this.cRank&&r.push(`${s[i]}`);e=r.join(),t=a.join()}return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6644,7 +6642,7 @@ return a / b;`,rZ=`
           }
         }
       }
-    `}};function Zae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Qae(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var Jae={kernelName:Oo,backendName:"webgpu",kernelFunc:Zae},eie=jt({opType:19}),tie={kernelName:si,backendName:"webgpu",kernelFunc:eie},nie=jt({opType:17}),sie={kernelName:ni,backendName:"webgpu",kernelFunc:nie},rie=jt({opType:18}),aie={kernelName:zo,backendName:"webgpu",kernelFunc:rie},A2=fn({opSnippet:2,cpuKernelImpl:Gne,supportsComplex:!0}),iie={kernelName:ui,backendName:"webgpu",kernelFunc:A2};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=N2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=A2({inputs:{a:r,b:l},backend:n}),p=k2({inputs:{x:c},backend:n}),d=$v({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=_2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var uie={kernelName:ii,backendName:"webgpu",kernelFunc:oie},lie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=$2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},cie={kernelName:Mo,backendName:"webgpu",kernelFunc:lie},die=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
+    `}};function Zae(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=new Qae(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(i,[s,r,a],yn(r.dtype,a.dtype))}var Jae={kernelName:Fo,backendName:"webgpu",kernelFunc:Zae},eie=jt({opType:19}),tie={kernelName:si,backendName:"webgpu",kernelFunc:eie},nie=jt({opType:17}),sie={kernelName:ni,backendName:"webgpu",kernelFunc:nie},rie=jt({opType:18}),aie={kernelName:Po,backendName:"webgpu",kernelFunc:rie},A2=fn({opSnippet:2,cpuKernelImpl:Gne,supportsComplex:!0}),iie={kernelName:ui,backendName:"webgpu",kernelFunc:A2};function oie(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,i=w.parseAxisParam([a],r.shape),o=N2({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=N.expandShapeToKeepDim(o.shape,i),l=Me({inputs:{x:o},backend:n,attrs:{shape:u}}),c=A2({inputs:{a:r,b:l},backend:n}),p=k2({inputs:{x:c},backend:n}),d=Tv({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=Me({inputs:{x:d},backend:n,attrs:{shape:u}}),f=_2({inputs:{a:p,b:h},backend:n});return n.disposeData(o.dataId),n.disposeData(l.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var uie={kernelName:ii,backendName:"webgpu",kernelFunc:oie},lie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s;w.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let o=a.reduce((b,y)=>b*y),u=[[0,0]];u.push(...i);for(let b=1+a.length;b<r.shape.length;++b)u.push([0,0]);let l=[],c=$2({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=N.getReshaped(c.shape,a,o,!1),d=N.getPermuted(p.length,a.length,!1),h=N.getReshapedPermuted(c.shape,a,o,!1),f=Me({inputs:{x:c},backend:n,attrs:{shape:p}}),m=vi({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Me({inputs:{x:m},backend:n,attrs:{shape:h}});return l.push(c),l.push(f),l.push(m),l.forEach(b=>n.disposeData(b.dataId)),g},cie={kernelName:zo,backendName:"webgpu",kernelFunc:lie},die=class{constructor(e,t,n,s,r,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let o=t>1;this.shaderKey=`scatter_${n}_${s}_${o}`;let u=Wt(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${u};`;let l="";n===1?l="i":n===2&&(l="i, j"),this.indicesSnippet=`getIndices(${l})`;let c="";s===1?c="i":s===2&&(c="i, coords[1]"),this.updatesSnippet=`getUpdates(${c})`,this.strideString=o?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
       ${Ue()}
 
         let globalIndex = index * ${this.workPerThread};
@@ -6674,14 +6672,14 @@ return a / b;`,rZ=`
             }
           }
         }
-      }`}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new die(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var hie={kernelName:ip,backendName:"webgpu",kernelFunc:pie};function fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=cu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var mie={kernelName:Lo,backendName:"webgpu",kernelFunc:fie},gie=jt({opType:20}),bie={kernelName:ri,backendName:"webgpu",kernelFunc:gie},yie={kernelName:_l,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new nc(n.shape,21);return s.runWebGPUProgram(r,[n],n.dtype)}},vie=fn({opSnippet:11}),xie={kernelName:oi,backendName:"webgpu",kernelFunc:vie},wie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+      }`}};function pie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:i}=t,{outputShape:o}=s,{sliceRank:u,numUpdates:l,strides:c,outputSize:p}=N.calculateShapes(a,r,o),d=!1,h=[{type:"int32",data:[l]},{type:"int32",data:[u]},{type:"int32",data:c}],f=new die(l,u,r.shape.length,a.shape.length,c,[p,1],d),m=n.runWebGPUProgram(f,[a,r,i],a.dtype,h),g=Me({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeData(m.dataId),g}var hie={kernelName:ap,backendName:"webgpu",kernelFunc:pie};function fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=s,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=r.shape.length,c=new Array(l).fill(0),p=r.shape.slice();return u.map(d=>{let h=[...p];h[o]=d;let f=lu({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[o]+=d,f})}var mie={kernelName:Mo,backendName:"webgpu",kernelFunc:fie},gie=jt({opType:20}),bie={kernelName:ri,backendName:"webgpu",kernelFunc:gie},yie={kernelName:$l,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new tc(n.shape,21);return s.runWebGPUProgram(r,[n],n.dtype)}},vie=fn({opSnippet:11}),xie={kernelName:oi,backendName:"webgpu",kernelFunc:vie},wie=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Wt(this.outputShape.length);this.uniforms=`begin : ${t};  strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
        ${Ue()}
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=wt.computeOutShape(y,v,x),T=cu({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});k=Me({inputs:{x:T},backend:n,attrs:{shape:f}}),n.disposeData(T.dataId)}else if(n.shouldExecuteOnCPU([r])){let T=n.readSync(r.dataId),E=De(r.shape,r.dtype,T),A=Wne(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let T=new wie(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(T,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Iie={kernelName:Bo,backendName:"webgpu",kernelFunc:kie};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Une(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Cie={kernelName:op,backendName:"webgpu",kernelFunc:Sie},Nie=jt({opType:22}),Tie={kernelName:li,backendName:"webgpu",kernelFunc:Nie},$ie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=_ie(this.rank,"uniforms.");return`
+     `}};function kie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=Me({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=wt.computeOutShape(y,v,x),T=lu({inputs:{x:r},backend:n,attrs:{begin:y,size:C}});k=Me({inputs:{x:T},backend:n,attrs:{shape:f}}),n.disposeData(T.dataId)}else if(n.shouldExecuteOnCPU([r])){let T=n.readSync(r.dataId),E=De(r.shape,r.dtype,T),A=Wne(h,E,x,y);k=n.makeTensorInfo(f,r.dtype,A.values)}else{let T=new wie(h),E=[{type:"int32",data:y},{type:"int32",data:x}],A=n.runWebGPUProgram(T,[r],r.dtype,E);k=Me({inputs:{x:A},backend:n,attrs:{shape:f}}),n.disposeData(A.dataId)}return k}var Iie={kernelName:Lo,backendName:"webgpu",kernelFunc:kie};function Sie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=Une(d,h,r,a,i,o,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Cie={kernelName:ip,backendName:"webgpu",kernelFunc:Sie},Nie=jt({opType:22}),Tie={kernelName:li,backendName:"webgpu",kernelFunc:Nie},$ie=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=_ie(this.rank,"uniforms.");return`
       ${Ue()}
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
@@ -6815,7 +6813,7 @@ return a / b;`,rZ=`
             }
           }
         }
-      `}};function Bi(e,t){t!==null&&e.disposeData(t.dataId)}function Ew(e){let t=1;for(;t<e;)t*=2;return t}function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[C,T]=qne(k,o,r.dtype,a,i);return[n.makeTensorInfo(C.shape,C.dtype,C.values),n.makeTensorInfo(T.shape,T.dtype,T.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,du({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Ew(a),h=Ew(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,C,T)=>{let E=m(),A=new Rie(T),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[C]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Bi(n,F)};for(let k=1;k<d;k*=2){let C=k*2;for(let T=k;T>=1;T/=2)g(C,T,[c,h])}for(let k=h;k>d;k/=2){let C=m(),T=new Die([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(T,C,"int32",A),Bi(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=cu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Bi(n,b);let y=C2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Bi(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Bi(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Bi(n,x),[y,f]}var Oie={kernelName:Wo,backendName:"webgpu",kernelFunc:Fie},Pie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function Li(e,t){t!==null&&e.disposeData(t.dataId)}function Aw(e){let t=1;for(;t<e;)t*=2;return t}function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:i}=s,o=r.shape,u=o[o.length-1];if(n.shouldExecuteOnCPU([r])){let k=n.readSync(r.dataId),[C,T]=qne(k,o,r.dtype,a,i);return[n.makeTensorInfo(C.shape,C.dtype,C.values),n.makeTensorInfo(T.shape,T.dtype,T.values)]}if(a===0)return o[o.length-1]=0,[n.makeTensorInfo(o,r.dtype,[]),n.makeTensorInfo(o,"int32",[])];if(u===1)return[r,cu({attrs:{shape:o,dtype:"int32",value:0},backend:n})];let c=w.sizeFromShape(o)/u,p=Me({inputs:{x:r},attrs:{shape:[c,u]},backend:n}),d=Aw(a),h=Aw(u),f=null,m=()=>f===null?[p,p]:[p,f],g=(k,C,T)=>{let E=m(),A=new Rie(T),R=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[k]},{type:"int32",data:[C]}],F=f;f=n.runWebGPUProgram(A,E,"int32",R),Li(n,F)};for(let k=1;k<d;k*=2){let C=k*2;for(let T=k;T>=1;T/=2)g(C,T,[c,h])}for(let k=h;k>d;k/=2){let C=m(),T=new Die([c,k/2]),A=[{type:"int32",data:[u]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],P=f;f=n.runWebGPUProgram(T,C,"int32",A),Li(n,P);let R=d/2,F=R*2;for(let $=R;$>=1;$/=2)g(F,$,f.shape)}let b=f;f=lu({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Li(n,b);let y=C2({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Li(n,p);let v=o.slice(0,-1);v.push(a),b=f,f=Me({inputs:{x:f},attrs:{shape:v},backend:n}),Li(n,b);let x=y;return y=Me({inputs:{x:y},attrs:{shape:v},backend:n}),Li(n,x),[y,f]}var Oie={kernelName:Vo,backendName:"webgpu",kernelFunc:Fie},Pie=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6931,7 +6929,7 @@ return a / b;`,rZ=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Pie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Mie={kernelName:Uo,backendName:"webgpu",kernelFunc:zie};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=cu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Bie={kernelName:Go,backendName:"webgpu",kernelFunc:Lie},Vie=[pne,Xne,Qne,ese,ise,use,cse,pse,bse,wse,Ise,Tse,gne,Ese,Lse,Use,Hse,jse,Yse,Jse,tre,ire,ure,cre,dre,pre,fre,gre,yre,Sre,xre,kre,Tre,_re,Ere,Fre,zre,Lre,Vre,mne,_se,Ure,Hre,jre,Xre,Qre,Jre,eae,nae,rae,iae,uae,cae,pae,nre,fae,gae,yae,yse,xae,kae,Sae,Nae,$ae,Aae,Rae,vse,Dae,Oae,zae,cne,Bae,Uae,Hae,jae,Yae,Jae,tie,sie,aie,mse,Iie,Cie,uie,cie,hie,mie,bie,yie,xie,iie,rre,Tie,Eie,Oie,Mie,rse,Bie,vae];for(let e of Vie)Al(e);var Wie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Rw(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Rw(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Rw(e,t){return`${e}_${t}`}var E2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+        `}};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=s,[c,p,d,h]=r.shape,[f,m]=l!=null?l:[p,d],g=[c,f,m,h],b=new Pie(g),y=i==="nearest"?1:2,v;switch(o){case"constant":v=1;break;case"reflect":v=2;break;case"wrap":v=3;break;case"nearest":v=4;break;default:v=1;break}let x=[{type:"int32",data:[y]},{type:"int32",data:[v]},{type:"float32",data:[u]}];return n.runWebGPUProgram(b,[r,a],"float32",x)}var Mie={kernelName:Wo,backendName:"webgpu",kernelFunc:zie};function Lie(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[a],l=new Array(o-1),c=0;for(let m=0;m<o;m++)m!==a&&(l[c++]=i.shape[m]);let p=[],d=new Array(o).fill(0),h=i.shape.slice();h[a]=1;let f=new Array(u);for(let m=0;m<f.length;m++){d[a]=m;let g=lu({inputs:{x:i},backend:n,attrs:{begin:d,size:h}}),b=Me({inputs:{x:g},backend:n,attrs:{shape:l}});f[m]=b,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var Bie={kernelName:Uo,backendName:"webgpu",kernelFunc:Lie},Vie=[pne,Xne,Qne,ese,ise,use,cse,pse,bse,wse,Ise,Tse,gne,Ese,Lse,Use,Hse,jse,Yse,Jse,tre,ire,ure,cre,dre,pre,fre,gre,yre,Sre,xre,kre,Tre,_re,Ere,Fre,zre,Lre,Vre,mne,_se,Ure,Hre,jre,Xre,Qre,Jre,eae,nae,rae,iae,uae,cae,pae,nre,fae,gae,yae,yse,xae,kae,Sae,Nae,$ae,Aae,Rae,vse,Dae,Oae,zae,cne,Bae,Uae,Hae,jae,Yae,Jae,tie,sie,aie,mse,Iie,Cie,uie,cie,hie,mie,bie,yie,xie,iie,rre,Tie,Eie,Oie,Mie,rse,Bie,vae];for(let e of Vie)_l(e);var Wie=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=Ew(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({mappedAtCreation:n,size:e,usage:t});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=Ew(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function Ew(e,t){return`${e}_${t}`}var E2=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){w.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=Be(this.outputShape),this.dispatch=_e(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
 
       ${Ue()}
@@ -6945,10 +6943,10 @@ return a / b;`,rZ=`
           }
         }
       }
-  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Uie=class extends E2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gie=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),R2=class extends nl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Cv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Wie(this.device),this.tensorMap=new Ud(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return R2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*qm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*qm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new E2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Uie),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=N.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=u2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let T=this.tensorMap.get(r.dataId);return T.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(T=>T.shape),o="int32";i.map(T=>{a.push({type:o,data:T})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let T=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?T/4:T]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((T,E)=>{if(T.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),p=c.map(T=>T.dtype).concat(r.dtype),d=c.map(T=>N.getBroadcastDims(T.shape,r.shape)),h=c.map(T=>w.arraysEqual(T.shape,r.shape)).join("_"),f=d.map(T=>T.join("_")).join(";"),m=S2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>I2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ire(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let C={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(C),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Gie){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},_v=R2;_v.nextDataId=0;var Hie={};Ae(Hie,{WebGPUBackend:()=>_v,webgpu_util:()=>i2});Cv()&&pp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new _v(r,s)},3);var Ct=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ct||{}),Jp=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Jp||{}),D2;function qie(e){D2=e.wasm.cwrap(na,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Jp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,C=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(a.shape).buffer);return D2(d,C,r.shape.length,h,T,a.shape.length,u,l,g,f,m,p||0,k),x}var Kie={kernelName:na,backendName:"wasm",setupFunc:qie,kernelFunc:jie};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,Ct[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Xie=Kt(io);function mn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=N.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,Ct[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Yie=!0,Qie=mn(kr,Yie),F2;function Zie(e){F2=e.wasm.cwrap(xa,null,["array","number","number","number"])}function Jie(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return F2(a,r.length,Ct[s.dtype],i),s}var eoe={kernelName:xa,backendName:"wasm",setupFunc:Zie,kernelFunc:Jie};function eh(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var toe={kernelName:Ma,backendName:"wasm",kernelFunc:eh},O2;function noe(e){O2=e.wasm.cwrap(ci,null,["number","array","number","number","number","array","number"])}function ro(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=roe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=soe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=eh({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return O2(c,h,u.shape.length,Ct[u.dtype],p,d,a.length),l}function soe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function roe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var aoe={kernelName:ci,backendName:"wasm",kernelFunc:ro,setupFunc:noe};function Dr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=N.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=N.getInnerMostAxes(i.length,r),u=ro({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var P2;function ioe(e){P2=e.wasm.cwrap(al,null,["number, number, number"])}function ooe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("all",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;P2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var uoe={kernelName:al,backendName:"wasm",setupFunc:ioe,kernelFunc:ooe},z2;function loe(e){z2=e.wasm.cwrap(il,null,["number, number, number"])}function coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("any",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;z2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var doe={kernelName:il,backendName:"wasm",setupFunc:loe,kernelFunc:coe},M2;function poe(e){M2=e.wasm.cwrap(wa,null,["number","number","number","number","number"])}function hoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Dr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return M2(o,Ct[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var foe={kernelName:wa,backendName:"wasm",kernelFunc:hoe,setupFunc:poe},L2;function moe(e){L2=e.wasm.cwrap(ka,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function goe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=N.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return L2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var boe={kernelName:ka,backendName:"wasm",setupFunc:moe,kernelFunc:goe};function bn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var yoe={kernelName:Eo,backendName:"wasm",kernelFunc:bn},B2;function voe(e){B2=e.wasm.cwrap(Ia,null,["number","array","number","number","array","number","number","number","number"])}function xoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=jo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],C=bn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=bn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(C.dataId).id,A=n.dataIdMap.get(T.dataId).id,P=i?C.shape[2]:C.shape[1],R=o?T.shape[1]:T.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],C.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(C.shape).buffer),q=new Uint8Array(new Int32Array(T.shape).buffer);return B2(E,W,C.shape.length,A,q,T.shape.length,i,o,z),n.disposeData(C.dataId),n.disposeData(T.dataId),$.shape=v,$}var woe={kernelName:Ia,backendName:"wasm",setupFunc:voe,kernelFunc:xoe};function ba(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Fd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)koe(u,c[0],d,a,i);else if(h===3)Ioe(u,c[0],c[1],d,a,i);else if(h===4)Soe(u,c[0],c[1],c[2],d,a,i);else{let f=Fd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function koe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ioe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Soe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Coe={kernelName:Po,backendName:"wasm",kernelFunc:ba};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=bn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=ro({inputs:{x:h},backend:n,attrs:{perm:l}}),m=bn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Toe={kernelName:oo,backendName:"wasm",kernelFunc:Noe};function ac(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var $oe={kernelName:Sa,backendName:"wasm",kernelFunc:ac},_oe=Kt(Ca),V2;function Aoe(e){V2=e.wasm.cwrap(Ir,null,["number","number","number","number"])}function Eoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:i}=s,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return V2(o,a,i,l),u}var Roe={kernelName:Ir,backendName:"wasm",setupFunc:Aoe,kernelFunc:Eoe};function W2(e){let{inputs:t,backend:n}=e,s=w.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>w.sizeFromShape(h.shape)>0);if(a.length===1)return eh({inputs:{x:a[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(w.sizeFromShape(r)===0)return i;let o=a.map(h=>h.shape);if(N.assertParamsConsistent(o,s),a[0].dtype==="string"){let h=a.map(v=>{let x=w.sizeFromShape(v.shape.slice(s));return bn({inputs:{x:v},backend:n,attrs:{shape:[-1,x]}})}),f=h.map(v=>({vals:n.readSync(v.dataId),shape:v.shape}));r=N.computeOutShape(h.map(v=>v.shape),1);let m=h[0].shape[0]===1,g=ev(f,r,t[0].dtype,m),b=N.computeOutShape(a.map(v=>v.shape),s);i.shape=b;let y=n.dataIdMap.get(i.dataId);return y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Doe={kernelName:uo,backendName:"wasm",kernelFunc:W2},U2;function Foe(e){U2=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ooe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,C=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let F=s.makeOutput(f.outShape,"float32"),$=s.dataIdMap.get(F.dataId).id;return U2(i,r.shape[0],r.shape[1],r.shape[2],o,m,g,b,y,v,x,R,k,C,T,E,A,P,$),F}var Poe={kernelName:Na,backendName:"wasm",setupFunc:Foe,kernelFunc:Ooe},G2;function zoe(e){G2=e.wasm.cwrap(Ta,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Moe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,inputShape:c}=s,p=1,d=N.convertConv2DDataFormat(u),h=N.computeConv2DInfo(c,a.shape,i,p,o,l,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:b,inHeight:y,inWidth:v,outChannels:x,outHeight:k,outWidth:C,strideHeight:T,strideWidth:E}=h,A=m-1-h.padInfo.top,P=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",F=w.computeStrides(h.inShape),$=w.computeStrides(r.shape),[z,W,q]=w.computeStrides(a.shape),K=F[0],Y=R?F[1]:F[2],Z=R?F[2]:1,te=R?1:F[1],ee=$[0],se=R?$[1]:$[2],ne=R?$[2]:1,oe=R?1:$[1],re=t.makeOutput(h.inShape,"float32"),le=t.dataIdMap.get(re.dataId).id,me=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return G2(me,we,f,m,g,y,v,b,k,C,x,T,E,A,P,z,W,q,K,Y,Z,te,ee,se,ne,oe,le),re}var Loe={kernelName:Ta,backendName:"wasm",setupFunc:zoe,kernelFunc:Moe},Boe=Kt($a),Voe=Kt(_a),H2=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(H2||{}),q2;function Woe(e){q2=e.wasm.cwrap(co,null,["number","number","number","number","array","number","number","number","number","number"])}function Uoe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:i}=s,{image:o,boxes:u,boxInd:l}=n,c=u.shape[0],[p,d]=i,h=[c,p,d,o.shape[3]],f=t.dataIdMap.get(o.dataId),m;o.dtype!=="float32"&&(m=ac({backend:t,inputs:{x:o},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,b=t.dataIdMap.get(u.dataId).id,y=t.dataIdMap.get(l.dataId).id,v=t.makeOutput(h,"float32"),x=t.dataIdMap.get(v.dataId).id,k=new Uint8Array(new Int32Array(o.shape).buffer);return q2(g,b,y,c,k,p,d,H2[r],a,x),m!=null&&t.disposeData(m.dataId),v}var Goe={kernelName:co,backendName:"wasm",setupFunc:Woe,kernelFunc:Uoe},j2;function Hoe(e){j2=e.wasm.cwrap(lo,null,["number","number","number","number","number","number"])}function qoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:i,reverse:o}=s,u=r.shape.length;w.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let l=N.getAxesPermutation([a],u),c=r;l!==null&&(c=ro({inputs:{x:r},attrs:{perm:l},backend:n}));let p=N.getInnerMostAxes(1,u)[0];N.assertAxesAreInnerMostDims("cumsum",[p],u);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;j2(f,i?1:0,o?1:0,h,m,Ct[r.dtype]);let g=d;if(l!==null){let b=N.getUndoAxesPermutation(l);g=ro({inputs:{x:d},attrs:{perm:b},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var joe={kernelName:lo,backendName:"wasm",setupFunc:Hoe,kernelFunc:qoe},K2;function Koe(e){K2=e.wasm.cwrap(po,null,["number","number","number","array","number","array","array","number","number"])}function Xoe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:i}=s,o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],c=i==="NHWC"?r.shape[3]:r.shape[1],p=u*a,d=l*a,h=c/(a*a),f=i==="NHWC"?[o,p,d,h]:[o,h,p,d],m=t.makeOutput(f,"float32"),b=t.dataIdMap.get(r.dataId).id,y=new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer),v=new Uint8Array(new Int32Array(f).buffer),x=new Uint8Array(new Int32Array(w.computeStrides(f)).buffer),k=t.dataIdMap.get(m.dataId).id;return K2(b,a,i==="NHWC"?1:0,y,r.shape.length-1,v,x,f.length,k),m}var Yoe={kernelName:po,backendName:"wasm",setupFunc:Koe,kernelFunc:Xoe},X2;function Qoe(e){X2=e.wasm.cwrap(Aa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Zoe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p}=n,d=l==null?[1,1]:l,h=N.computeConv2DInfo(r.shape,a.shape,u,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,b=h.padInfo.right,y=h.padInfo.bottom,v=h.padInfo.left,x=h.dilationHeight,k=h.dilationWidth,C=h.strideHeight,T=h.strideWidth,E=h.inChannels,A=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let R=s.makeOutput(h.outShape,"float32"),F=s.dataIdMap.get(R.dataId).id;return X2(i,r.shape[0],r.shape[1],r.shape[2],o,f,m,g,b,y,v,P,x,k,C,T,E,A,F),R}var Joe={kernelName:Aa,backendName:"wasm",setupFunc:Qoe,kernelFunc:Zoe},eue=Kt(Ra),tue=!1,nue=mn(ho,tue,"bool"),sue=Kt(Da,"float32");function Qm(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,i=r.shape.length,o=r.shape.slice(),u=a;return a<0&&(w.assert(-(i+1)<=a,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+a+1),o.splice(u,0,1),bn({inputs:{x:r},backend:s,attrs:{shape:o}})}var rue={kernelName:fo,backendName:"wasm",kernelFunc:Qm};function Y2(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var aue={kernelName:fl,backendName:"wasm",kernelFunc:Y2},Q2;function iue(e){Q2=e.wasm.cwrap(go,null,["number","number","number","number","number","number"])}function oue(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,[o,u,l,c]=s.shape;return Q2(a,o,u,l,c,i),r}var uue={kernelName:go,backendName:"wasm",kernelFunc:oue,setupFunc:iue},lue=Kt(Fa),cue=!1,due=mn(Oa,cue),Z2;function pue(e){Z2=e.wasm.cwrap(Pa,null,["number","number","number","number","number","number","number"])}function hue(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:i,variance:o,offset:u,scale:l}=n,c=t.dataIdMap.get(a.dataId).id,p=t.dataIdMap.get(i.dataId).id,d=t.dataIdMap.get(o.dataId).id,h=u!=null?t.dataIdMap.get(u.dataId).id:0,f=l!=null?t.dataIdMap.get(l.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(w.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return Z2(c,p,d,h,f,r,g),m}var fue={kernelName:Pa,backendName:"wasm",setupFunc:pue,kernelFunc:hue},J2;function mue(e){J2=e.wasm.cwrap(sa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function gue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type==="SAME"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return J2(b,K,Y,Z,y,k,C,x,T,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var bue={kernelName:sa,backendName:"wasm",setupFunc:mue,kernelFunc:gue},eN;function yue(e){eN=e.wasm.cwrap(ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Jp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type==="SAME"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. 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ele={kernelName:Ha,backendName:"wasm",setupFunc:Zue,kernelFunc:Jue},tle=!1,nle=mn(qa,tle),uN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(uN||{}),lN;function sle(e){lN=e.wasm.cwrap(ja,null,["number","array","number","number","array","array","number","number"])}function rle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return lN(i,l,t.shape.length,Ct[t.dtype],d,h,uN[r],u),o}var ale={kernelName:ja,backendName:"wasm",kernelFunc:rle,setupFunc:sle},ile=!0,ole=mn(Ka,ile),ule=Kt(Io);function Av(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var cN;function lle(e){cN=e.wasm.cwrap(Co,"number",["number","number","number","number","number"])}function cle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=cN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Av(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var dle={kernelName:Co,backendName:"wasm",setupFunc:lle,kernelFunc:cle},dN;function ple(e){dN=e.wasm.cwrap(wl,"number",["number","number","number","number","number","bool"])}function hle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=dN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Av(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var fle={kernelName:wl,backendName:"wasm",setupFunc:ple,kernelFunc:hle},pN;function mle(e){pN=e.wasm.cwrap(No,"number",["number","number","number","number","number","number"])}function gle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=pN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Av(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var ble={kernelName:No,backendName:"wasm",setupFunc:mle,kernelFunc:gle},yle=!1,vle=mn(So,yle,"bool"),hN;function xle(e){hN=e.wasm.cwrap($o,null,["number","number","number","number","number"])}function wle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return hN(p,a,i,o,l),u}var kle={kernelName:$o,backendName:"wasm",setupFunc:xle,kernelFunc:wle};function Ile(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Sle={kernelName:To,backendName:"wasm",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Qm({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Qm({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=W2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Nle={kernelName:_o,backendName:"wasm",kernelFunc:Cle},fN;function Tle(e){fN=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function $le(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return Y2({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return fN(i,c,t.shape.length,Ct[t.dtype],h,f,r,l),o}var mN={kernelName:Xa,backendName:"wasm",kernelFunc:$le,setupFunc:Tle},_le=!1,Ale=mn(Ya,_le),gN;function Ele(e){gN=e.wasm.cwrap(Qa,null,["number","number","number"])}function Rle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!=="float32"&&(l=ac({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return gN(o,i,p),u.dtype!=="float32"&&n.disposeData(l.dataId),c}var Dle={kernelName:Qa,backendName:"wasm",setupFunc:Ele,kernelFunc:Rle},bN;function Fle(e){bN=e.wasm.cwrap(Ao,null,["number","number","number","number"])}function Ole(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=N.getInnerMostAxes(f.length,l.shape.length))}N.assertAxesAreInnerMostDims("prod",f,l.shape.length);let[m,g]=N.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;bN(u,b,Ct[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ple={kernelName:Ao,backendName:"wasm",setupFunc:Fle,kernelFunc:Ole},zle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=sv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},Mle={kernelName:kl,backendName:"wasm",kernelFunc:zle},Lle=!0,Ble=mn(Ea,Lle),Vle=Kt(Za),Wle=Kt(ei),yN;function Ule(e){yN=e.wasm.cwrap(Ja,null,["number","number","number","number","number","number","number","number","number","number"])}function Gle(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=ac({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let v=t.dataIdMap.get(y.dataId).id;return yN(b,c,p,d,h,u,l,a?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),y}var Hle={kernelName:Ja,backendName:"wasm",setupFunc:Ule,kernelFunc:Gle},vN;function qle(e){vN=e.wasm.cwrap(Ro,null,["number","array","number","array","number","number"])}function jle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return eh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);vN(u,c,i.length,p,r.shape.length,l);let d=bn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Kle={kernelName:Ro,backendName:"wasm",kernelFunc:jle,setupFunc:qle},xN;function Xle(e){xN=e.wasm.cwrap(qo,null,["number","number","number","number","number","number","number","number","array","number","number"])}function Yle(e){let{inputs:t,backend:n,attrs:s}=e,{image:r}=t,{radians:a,fillValue:i,center:o}=s,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,c=n.dataIdMap.get(u.dataId).id,[p,d,h,f]=r.shape,[m,g]=N.getImageCenter(o,d,h),b=i===0,y=255,v=typeof i=="number"?[i,i,i,b?0:y]:[...i,y],x=new Uint8Array(new Int32Array(v).buffer);return xN(l,p,d,h,f,a,m,g,x,v.length,c),u}var Qle={kernelName:qo,backendName:"wasm",kernelFunc:Yle,setupFunc:Xle},Zle=Kt(Do),Jle=Kt(ti),wN;function ece(e){wN=e.wasm.cwrap(Fo,null,["number","number","number","number","number","number","array","number","number"])}function tce(e){let{backend:t,inputs:n,attrs:s}=e,{indices:r,updates:a}=n,{shape:i}=s,o=t.makeOutput(i,a.dtype);if(w.sizeFromShape(i)===0)return o;let{sliceRank:u,numUpdates:l,sliceSize:c,strides:p,outputSize:d}=Sk.calculateShapes(a,r,i),f=t.dataIdMap.get(r.dataId).id,g=t.dataIdMap.get(a.dataId).id,b=new Uint8Array(new Int32Array(p).buffer),y=t.dataIdMap.get(o.dataId).id;return wN(f,g,Ct[a.dtype],u,l,c,b,d,y),o}var nce={kernelName:Fo,backendName:"wasm",setupFunc:ece,kernelFunc:tce},kN;function sce(e){kN=e.wasm.cwrap("SelectV2",null,["number","number","number","number","number"])}function rce(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,i=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,u=n.dataIdMap.get(a.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id,p=s.shape.length,d=r.shape.length,h=p===0||p>1||d===1?1:w.sizeFromShape(r.shape.slice(1));return kN(i,o,u,h,c),l}var ace={kernelName:Oo,backendName:"wasm",kernelFunc:rce,setupFunc:sce},IN;function ice(e){IN=e.wasm.cwrap(si,null,["number","number"])}function oce(e){let{backend:t,inputs:{x:n}}=e,s=t.dataIdMap.get(n.dataId).id,r=t.makeOutput(n.shape,n.dtype),a=t.dataIdMap.get(r.dataId).id;return w.sizeFromShape(r.shape)===0||IN(s,a),r}var uce={kernelName:"Sigmoid",backendName:"wasm",setupFunc:ice,kernelFunc:oce},lce=Kt(ni),SN;function cce(e){SN=e.wasm.cwrap(ii,null,["number","number","number","number"])}function dce(e){let{backend:t,inputs:{logits:n},attrs:{dim:s}}=e,r=t.dataIdMap.get(n.dataId).id,a=t.makeOutput(n.shape,n.dtype),i=t.dataIdMap.get(a.dataId).id,o=n.shape[s],u=w.sizeFromShape(n.shape)/o;return w.sizeFromShape(a.shape)===0||SN(r,i,o,u),a}var pce={kernelName:ii,backendName:"wasm",setupFunc:cce,kernelFunc:dce};function hce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:i}=s,o=w.sizeFromShape(a),u=[[0,0]];u.push(...i);for(let C=1+a.length;C<r.shape.length;++C)u.push([0,0]);let l=mN.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),c=N.getReshaped(l.shape,a,o,!1),p=N.getPermuted(c.length,a.length,!1),d=N.getReshapedPermuted(l.shape,a,o,!1),m=bn({inputs:{x:l},backend:n,attrs:{shape:c}}),y=ro({inputs:{x:m},backend:n,attrs:{perm:p}}),k=bn({inputs:{x:y},backend:n,attrs:{shape:d}});return n.disposeData(l.dataId),n.disposeData(m.dataId),n.disposeData(y.dataId),k}var fce={kernelName:Mo,backendName:"wasm",kernelFunc:hce},CN;function mce(e){CN=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function gce(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:i}=n,o=s.shape[0],u=s.shape[1],l=t.readSync(a.dataId)[0],c=[o+l,u],p=t.dataIdMap.get(s.dataId).id,d=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(i.dataId).id,f=t.makeOutput(c,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(c.slice(0,1),r.dtype),b=t.dataIdMap.get(g.dataId).id,y=t.makeOutput([l],"bool"),v=t.dataIdMap.get(y.dataId).id,x=t.makeOutput([o],s.dtype),k=t.dataIdMap.get(x.dataId).id,C=t.makeOutput([4],"int32"),T=t.dataIdMap.get(C.dataId).id,E=CN(p,d,Ct[r.dtype],o,l,u,h,m,b,v,k,T),A=t.readSync(C.dataId),P;switch(A[0]){case 1:{P=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);break}case 2:{P=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(A[1],A[2]);break}case 3:P=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(A[1],A[2],A[3]);break;default:P=""}if(t.disposeData(C.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(y.dataId),t.disposeData(x.dataId),new Error(P);let R=f,F=g;return E!==c[0]&&(R=ba({inputs:{x:f},attrs:{begin:0,size:[E,u]},backend:t}),F=ba({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[R,F,y,x]}var bce={kernelName:sp,backendName:"wasm",setupFunc:mce,kernelFunc:gce},NN;function yce(e){NN=e.wasm.cwrap($l,null,["number","number","number","number","number","number","number"])}function vce(e){let{backend:t,inputs:n}=e,{inputIndices:s,inputShape:r,newShape:a}=n;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
+  `}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Uie=class extends E2{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},Gie=X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),Rw=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(i=>i<=n))return r;w.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),w.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},R2=class extends tl{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Sv())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new Wie(this.device),this.tensorMap=new Wd(this,Ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return R2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.stagingDisposalQueue.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.stagingDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=w.sizeFromShape(t)*Hm(n);return this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=w.sizeFromShape(n)*Hm(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new E2),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new Uie),this.fromPixelImportProgram;default:w.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),X().getBool("WEBGPU_USE_PROFILE_TOOL")&&(w.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],i=r[1];s=N.mergeRealAndImagArrays(a,i)}else{let r=await this.getBufferData(t);s=u2(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>w.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return De(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=w.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=w.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},o=await Promise.all(r);return i.kernelMs=w.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:a[l],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,i}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&w.isString(n[0])){let r=n.map(a=>w.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values)){let n=this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),s=n.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(s).set(t.values):new Float32Array(s).set(t.values),n.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(n,0,t.bufferInfo.buffer,0,t.bufferInfo.byteSize);let r={byteSize:t.bufferInfo.byteSize,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:n};this.stagingDisposalQueue.push(r)}}makeUniforms(e){let t=0,n=[];e.forEach(a=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 1:i=4;break;case 2:i=8;break;case 3:i=16;break;case 4:i=16;break;default:w.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}t=Math.ceil(t/i)*i,n.push(t),t+=a.data.length*4});let s=new ArrayBuffer(t);e.forEach((a,i)=>{let o=n[i];a.type==="int32"?new Int32Array(s,o,a.data.length).set(a.data):a.type==="uint32"?new Uint32Array(s,o,a.data.length).set(a.data):new Float32Array(s,o,a.data.length).set(a.data)});let r=this.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(r,0,s,0,t),{offset:0,size:t,buffer:r}}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),w.sizeFromShape(r.shape)===0){let T=this.tensorMap.get(r.dataId);return T.values=w.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}e.dispatch=Rw(this.device,e);let a=[{type:"float32",data:[NaN]}],i=t.concat(r).map(T=>T.shape),o="int32";i.map(T=>{a.push({type:o,data:T})});let u=w.computeStrides(r.shape);if(a.push({type:o,data:u}),e.size){let T=w.sizeFromShape(e.outputShape);a.push({type:o,data:[e.isVec4?T/4:T]})}s&&(a=[...a,...s]);let l=this.makeUniforms(a),c=t.map((T,E)=>{if(T.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(T.dataId),{dtype:this.tensorMap.get(T.dataId).dtype,shape:T.shape,name:e.variableNames[E]}}),p=c.map(T=>T.dtype).concat(r.dtype),d=c.map(T=>N.getBroadcastDims(T.shape,r.shape)),h=c.map(T=>w.arraysEqual(T.shape,r.shape)).join("_"),f=d.map(T=>T.join("_")).join(";"),m=S2(e,i,p,f,h),{bindGroupLayout:g,pipelineLayout:b}=this.getCachedOrCreateLayout(e.variableNames.length),y=this.getAndSavePipeline(m,()=>I2(this.device,e,b,c,r)),v=this.activeTimers!=null,x=Ire(this.device,g,t.map(T=>this.tensorToBinding(T)),this.tensorToBinding(r),l);this.ensureCommandEncoderReady();let k=this.getComputePass();v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,0),k.setPipeline(y),k.setBindGroup(0,x),k.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),v&&this.supportTimeQuery&&k.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(T=>{this.commandQueueOwnedIds.add(T.dataId)}),this.commandQueueOwnedIds.add(r.dataId);let C={byteSize:l.size,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:l.buffer};return this.uniformDisposalQueue.push(C),X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),v&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){e.dispatch=Rw(this.device,e);let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let i=this.getComputePass(),o=this.activeTimers!=null;o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,0),i.setPipeline(e.pipeline),i.setBindGroup(0,a),i.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),o&&this.supportTimeQuery&&i.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),o&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=Gie){return X().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&w.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},$v=R2;$v.nextDataId=0;var Hie={};Ae(Hie,{WebGPUBackend:()=>$v,webgpu_util:()=>i2});Sv()&&dp("webgpu",async()=>{X().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:X().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension},r?s.requiredFeatures=["timestamp-query"]:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let a=await t.requestDevice(s);return new $v(a,r)},3);var Ct=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(Ct||{}),Zp=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(Zp||{}),D2;function qie(e){D2=e.wasm.cwrap(na,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function jie(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(i!=null){let E=n.dataIdMap.get(i.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=o==null?0:n.dataIdMap.get(o.dataId).id,g=Zp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let b=u?r.shape[2]:r.shape[1],y=l?a.shape[1]:a.shape[2],v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),x=n.makeOutput([...v,b,y],r.dtype),k=n.dataIdMap.get(x.dataId).id,C=new Uint8Array(new Int32Array(r.shape).buffer),T=new Uint8Array(new Int32Array(a.shape).buffer);return D2(d,C,r.shape.length,h,T,a.shape.length,u,l,g,f,m,p||0,k),x}var Kie={kernelName:na,backendName:"wasm",setupFunc:qie,kernelFunc:jie};function Kt(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:i,inputs:{x:o}}=a,u=i.dataIdMap.get(o.dataId).id,l=i.makeOutput(o.shape,t||o.dtype),c=i.dataIdMap.get(l.dataId).id;return w.sizeFromShape(l.shape)===0||n(u,Ct[o.dtype],c),l}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var Xie=Kt(ao);function mn(e,t,n){let s;function r(i){s=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(i){let{backend:o,inputs:u}=i,{a:l,b:c}=u,p=o.dataIdMap.get(l.dataId).id,d=o.dataIdMap.get(c.dataId).id,h=n!=null?n:l.dtype,f=N.assertAndGetBroadcastShape(l.shape,c.shape),m=o.makeOutput(f,h);if(w.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(l.shape).buffer),b=new Uint8Array(new Int32Array(c.shape).buffer),y=o.dataIdMap.get(m.dataId).id;return(()=>s(p,g,l.shape.length,d,b,c.shape.length,Ct[l.dtype],y))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var Yie=!0,Qie=mn(kr,Yie),F2;function Zie(e){F2=e.wasm.cwrap(xa,null,["array","number","number","number"])}function Jie(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(w.sizeFromShape(s.shape)===0)return s;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(s.dataId).id;return F2(a,r.length,Ct[s.dtype],i),s}var eoe={kernelName:xa,backendName:"wasm",setupFunc:Zie,kernelFunc:Jie};function Jp(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var toe={kernelName:Ma,backendName:"wasm",kernelFunc:Jp},O2;function noe(e){O2=e.wasm.cwrap(ci,null,["number","array","number","number","number","array","number"])}function so(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=roe(t.x.shape,s.perm),i=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(i=!1);let o=soe(t.x.shape,s.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let f=Jp({inputs:t,backend:n});return f.shape=o,f}let l=n.makeOutput(o,u.dtype),c=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,d=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return O2(c,h,u.shape.length,Ct[u.dtype],p,d,a.length),l}function soe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function roe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let i=0;i<s.length;++i)s[i]>=r&&(a===-1||s[a]>s[i])&&(a=i);s[a]=r}return[n,s]}var aoe={kernelName:ci,backendName:"wasm",kernelFunc:so,setupFunc:noe};function Dr(e,t,n){let s=e.shape,r=e.shape.length,a=w.parseAxisParam(t,s),i=a,o=N.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let c=new Array(r);for(let h=0;h<c.length;h++)c[h]=s[o[h]];i=N.getInnerMostAxes(i.length,r),u=so({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:a,axes:i,inputWasTransposed:l}}var P2;function ioe(e){P2=e.wasm.cwrap(rl,null,["number, number, number"])}function ooe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("all",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;P2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var uoe={kernelName:rl,backendName:"wasm",setupFunc:ioe,kernelFunc:ooe},z2;function loe(e){z2=e.wasm.cwrap(al,null,["number, number, number"])}function coe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,u=t.dataIdMap.get(i.dataId).id,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t);if(h){let v=t.dataIdMap.get(c.dataId).id;l=c,u=v}let f=l.shape.length;N.assertAxesAreInnerMostDims("any",p,f);let[m,g]=N.computeOutAndReduceShapes(l.shape,p),b=w.sizeFromShape(g),y=t.makeOutput(m,i.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;z2(u,b,v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var doe={kernelName:al,backendName:"wasm",setupFunc:loe,kernelFunc:coe},M2;function poe(e){M2=e.wasm.cwrap(wa,null,["number","number","number","number","number"])}function hoe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=i,u=a,{transposed:l,axes:c,inputWasTransposed:p}=Dr(a,r,t);if(p){let b=t.dataIdMap.get(l.dataId).id;b!==i&&(u=l,o=b)}let d=u.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=w.sizeFromShape(h.shape),g=u.shape[c[0]];return M2(o,Ct[u.dtype],m,g,f),p&&t.disposeData(l.dataId),h}var foe={kernelName:wa,backendName:"wasm",kernelFunc:hoe,setupFunc:poe},L2;function moe(e){L2=e.wasm.cwrap(ka,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function goe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,c=N.computePool2DInfo(r.shape,i,o,1,u,l),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,b=c.strideHeight,y=c.strideWidth,v=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let x=s.makeOutput(c.outShape,"float32"),k=s.dataIdMap.get(x.dataId).id;return L2(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,b,y,v,k),x}var boe={kernelName:ka,backendName:"wasm",setupFunc:moe,kernelFunc:goe};function bn(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=w.sizeFromShape(s.shape),i=w.inferFromImplicitShape(r,a);return w.assert(a===w.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:i,dtype:s.dtype}}var yoe={kernelName:Ao,backendName:"wasm",kernelFunc:bn},B2;function voe(e){B2=e.wasm.cwrap(Ia,null,["number","array","number","number","array","number","number","number","number"])}function xoe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:i,transposeB:o}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=a.shape.length,c=i?r.shape[u-2]:r.shape[u-1],p=o?a.shape[l-1]:a.shape[l-2],d=i?r.shape[u-1]:r.shape[u-2],h=o?a.shape[l-2]:a.shape[l-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=w.sizeFromShape(f),b=w.sizeFromShape(m),v=qo.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);w.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${i} and transposeB=${o} must match.`);let x=i?[g,c,d]:[g,d,c],k=o?[b,h,p]:[b,p,h],C=bn({inputs:{x:r},backend:n,attrs:{shape:x}}),T=bn({inputs:{x:a},backend:n,attrs:{shape:k}}),E=n.dataIdMap.get(C.dataId).id,A=n.dataIdMap.get(T.dataId).id,P=i?C.shape[2]:C.shape[1],R=o?T.shape[1]:T.shape[2],F=Math.max(g,b),$=n.makeOutput([F,P,R],C.dtype),z=n.dataIdMap.get($.dataId).id,W=new Uint8Array(new Int32Array(C.shape).buffer),q=new Uint8Array(new Int32Array(T.shape).buffer);return B2(E,W,C.shape.length,A,q,T.shape.length,i,o,z),n.disposeData(C.dataId),n.disposeData(T.dataId),$.shape=v,$}var woe={kernelName:Ia,backendName:"wasm",setupFunc:voe,kernelFunc:xoe};function ba(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,i]=wt.parseSliceParams(t,n,s),o=wt.isSliceContinous(t.shape,a,i),u=r.readSync(t.dataId),l=r.makeOutput(i,t.dtype),c=w.computeStrides(t.shape),p=r.dataIdMap.get(l.dataId);if(o){let f=wt.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=u.slice(f,f+w.sizeFromShape(i)):r.typedArrayFromHeap(l).set(u.subarray(f,f+w.sizeFromShape(i))),l}if(t.dtype==="string"){let f=Dd(u,a,i,t.shape,t.dtype);return p.stringBytes=f,l}let d=r.typedArrayFromHeap(l),h=t.shape.length;if(h===2)koe(u,c[0],d,a,i);else if(h===3)Ioe(u,c[0],c[1],d,a,i);else if(h===4)Soe(u,c[0],c[1],c[2],d,a,i);else{let f=Dd(u,a,i,t.shape,t.dtype);d.set(f)}return l}function koe(e,t,n,s,r){let a=0,i=s[0],o=s[1],u=i+r[0];for(let l=i;l<u;l++){let c=l*t+o;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function Ioe(e,t,n,s,r,a){let i=0,o=r[0],u=r[1],l=r[2],c=o+a[0],p=u+a[1];for(let d=o;d<c;d++)for(let h=u;h<p;h++){let f=d*t+h*n+l;s.set(e.subarray(f,f+a[2]),i),i+=a[2]}}function Soe(e,t,n,s,r,a,i){let o=0,u=a[0],l=a[1],c=a[2],p=u+i[0],d=l+i[1],h=c+i[2],f=a[3];for(let m=u;m<p;m++)for(let g=l;g<d;g++)for(let b=c;b<h;b++){let y=m*t+g*n+b*s+f;r.set(e.subarray(y,y+i[3]),o),o+=i[3]}}var Coe={kernelName:Oo,backendName:"wasm",kernelFunc:ba};function Noe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:i}=s,o=a.reduce((b,y)=>b*y),u=N.getReshaped(r.shape,a,o),l=N.getPermuted(u.length,a.length),c=N.getReshapedPermuted(r.shape,a,o),p=N.getSliceBeginCoords(i,a.length),d=N.getSliceSize(c,i,a.length),h=bn({inputs:{x:r},backend:n,attrs:{shape:u}}),f=so({inputs:{x:h},backend:n,attrs:{perm:l}}),m=bn({inputs:{x:f},backend:n,attrs:{shape:c}}),g=ba({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Toe={kernelName:io,backendName:"wasm",kernelFunc:Noe};function rc(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var $oe={kernelName:Sa,backendName:"wasm",kernelFunc:rc},_oe=Kt(Ca),V2;function Aoe(e){V2=e.wasm.cwrap(Ir,null,["number","number","number","number"])}function 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y.stringBytes=N.fromStringArrayToUint8(g),h.forEach(v=>n.disposeData(v.dataId)),i}let u=w.sizeFromShape(a[0].shape.slice(0,s)),l=0,c=a.map(h=>{let f=w.sizeFromShape(h.shape.slice(s));return l+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let f=h*l;for(let m=0;m<p.length;m++){let g=c[m],b=h*g,y=p[m].subarray(b,b+g);d.set(y,f),f+=g}}return i}var Doe={kernelName:oo,backendName:"wasm",kernelFunc:W2},U2;function Foe(e){U2=e.wasm.cwrap(Na,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ooe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,i=s.dataIdMap.get(r.dataId).id,o=s.dataIdMap.get(a.dataId).id,{strides:u,dilations:l,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=N.convertConv2DDataFormat(d),f=N.computeConv2DInfo(r.shape,a.shape,u,l,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,b=f.padInfo.top,y=f.padInfo.right,v=f.padInfo.bottom,x=f.padInfo.left,k=f.dilationHeight,C=f.dilationWidth,T=f.strideHeight,E=f.strideWidth,A=f.inChannels,P=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Please use 'NHWC'.`);let te=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(te.dataId).id,se=o==null?0:s.dataIdMap.get(o.dataId).id;return J2(b,K,Y,Z,y,k,C,x,T,E,A,P,q,R,F,$,z,W,v,g,se,f||0,ee),te}var bue={kernelName:sa,backendName:"wasm",setupFunc:mue,kernelFunc:gue},eN;function yue(e){eN=e.wasm.cwrap(ra,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function vue(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,u,c,l,d,!0),g=Zp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let b=s.dataIdMap.get(r.dataId).id,y=s.dataIdMap.get(a.dataId).id,v=m.outChannels,x=0;if(i!=null){let ne=s.dataIdMap.get(i.dataId);if(ne.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ne.shape.length}.`);if(ne.shape[0]!==v)throw new Error(`FusedDepthwiseConv2D bias shape (${ne.shape}) does not match the number of output channels (${v})`);x=ne.id}let k=m.filterHeight,C=m.filterWidth,T=m.padInfo.top,E=m.padInfo.right,A=m.padInfo.bottom,P=m.padInfo.left,R=m.dilationHeight,F=m.dilationWidth,$=m.strideHeight,z=m.strideWidth,W=m.inChannels,q=m.padInfo.type==="SAME"?1:0,K=m.batchSize,Y=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. 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ele={kernelName:Ha,backendName:"wasm",setupFunc:Zue,kernelFunc:Jue},tle=!1,nle=mn(qa,tle),uN=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(uN||{}),lN;function sle(e){lN=e.wasm.cwrap(ja,null,["number","array","number","number","array","array","number","number"])}function rle(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return lN(i,l,t.shape.length,Ct[t.dtype],d,h,uN[r],u),o}var ale={kernelName:ja,backendName:"wasm",kernelFunc:rle,setupFunc:sle},ile=!0,ole=mn(Ka,ile),ule=Kt(ko);function _v(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:i}}var cN;function lle(e){cN=e.wasm.cwrap(So,"number",["number","number","number","number","number"])}function cle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i}=s,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,c=t.dataIdMap.get(u.dataId).id,p=cN(l,c,a,r,i),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=_v(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var dle={kernelName:So,backendName:"wasm",setupFunc:lle,kernelFunc:cle},dN;function ple(e){dN=e.wasm.cwrap(xl,"number",["number","number","number","number","number","bool"])}function hle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,padToMaxOutputSize:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=dN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_v(t,d);t.wasm._free(m);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([],"int32",g);return[b,y]}var fle={kernelName:xl,backendName:"wasm",setupFunc:ple,kernelFunc:hle},pN;function mle(e){pN=e.wasm.cwrap(Co,"number",["number","number","number","number","number","number"])}function gle(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:i,softNmsSigma:o}=s,{boxes:u,scores:l}=n,c=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,d=pN(c,p,a,r,i,o),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_v(t,d);t.wasm._free(g);let b=t.makeOutput([f],"int32",h),y=t.makeOutput([f],"float32",m);return[b,y]}var ble={kernelName:Co,backendName:"wasm",setupFunc:mle,kernelFunc:gle},yle=!1,vle=mn(Io,yle,"bool"),hN;function xle(e){hN=e.wasm.cwrap(To,null,["number","number","number","number","number"])}function wle(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:i,offValue:o}=s,u=n.makeOutput([...r.shape,a],"int32"),l=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(r.dataId).id;return hN(p,a,i,o,l),u}var kle={kernelName:To,backendName:"wasm",setupFunc:xle,kernelFunc:wle};function Ile(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Sle={kernelName:No,backendName:"wasm",kernelFunc:Ile};function Cle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Ym({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,i=t[0].dtype;t.forEach(c=>{w.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),w.assert(i===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(c=>{let p=Ym({inputs:{input:c},backend:n,attrs:{dim:r}});return o.push(p),p}),l=W2({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(c=>n.disposeData(c.dataId)),l}var Nle={kernelName:$o,backendName:"wasm",kernelFunc:Cle},fN;function Tle(e){fN=e.wasm.cwrap(Xa,null,["number","array","number","number","array","array","number","number"])}function $le(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(w.sizeFromShape(t.shape)===0)return Y2({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),p=s.map(m=>m[0]),d=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(p).buffer),f=new Uint8Array(new Int32Array(d).buffer);return fN(i,c,t.shape.length,Ct[t.dtype],h,f,r,l),o}var mN={kernelName:Xa,backendName:"wasm",kernelFunc:$le,setupFunc:Tle},_le=!1,Ale=mn(Ya,_le),gN;function Ele(e){gN=e.wasm.cwrap(Qa,null,["number","number","number"])}function Rle(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=n.dataIdMap.get(s.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=a,u=s,l=u;u.dtype!=="float32"&&(l=rc({backend:n,inputs:{x:s},attrs:{dtype:"float32"}}),o=n.dataIdMap.get(l.dataId).id);let c=n.makeOutput(s.shape,"float32"),p=n.dataIdMap.get(c.dataId).id;return gN(o,i,p),u.dtype!=="float32"&&n.disposeData(l.dataId),c}var Dle={kernelName:Qa,backendName:"wasm",setupFunc:Ele,kernelFunc:Rle},bN;function Fle(e){bN=e.wasm.cwrap(_o,null,["number","number","number","number"])}function Ole(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=Dr(i,r,t),f=p;if(h){let v=t.dataIdMap.get(c.dataId).id;v!==o&&(l=c,u=v,f=N.getInnerMostAxes(f.length,l.shape.length))}N.assertAxesAreInnerMostDims("prod",f,l.shape.length);let[m,g]=N.computeOutAndReduceShapes(l.shape,f),b=w.sizeFromShape(g),y=t.makeOutput(m,l.dtype);if(w.sizeFromShape(l.shape)!==0){let v=t.dataIdMap.get(y.dataId).id;bN(u,b,Ct[y.dtype],v)}if(h&&t.disposeData(c.dataId),a){let v=N.expandShapeToKeepDim(y.shape,d);y.shape=v}return y}var Ple={kernelName:_o,backendName:"wasm",setupFunc:Fle,kernelFunc:Ole},zle=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:i}=n,o=nv(s,r,a,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},Mle={kernelName:wl,backendName:"wasm",kernelFunc:zle},Lle=!0,Ble=mn(Ea,Lle),Vle=Kt(Za),Wle=Kt(ei),yN;function Ule(e){yN=e.wasm.cwrap(Ja,null,["number","number","number","number","number","number","number","number","number","number"])}function Gle(e){let{backend:t,inputs:n,attrs:s}=e,{images:r}=n,{alignCorners:a,halfPixelCenters:i,size:o}=s,[u,l]=o,[c,p,d,h]=r.shape,f=[c,u,l,h],m=t.dataIdMap.get(r.dataId),g;m.dtype!=="float32"&&(g=rc({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),m=t.dataIdMap.get(g.dataId));let b=m.id,y=t.makeOutput(f,"float32");if(w.sizeFromShape(r.shape)===0)return y;let v=t.dataIdMap.get(y.dataId).id;return yN(b,c,p,d,h,u,l,a?1:0,i?1:0,v),g!=null&&t.disposeData(g.dataId),y}var Hle={kernelName:Ja,backendName:"wasm",setupFunc:Ule,kernelFunc:Gle},vN;function qle(e){vN=e.wasm.cwrap(Eo,null,["number","array","number","array","number","number"])}function jle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,i=w.parseAxisParam(a,r.shape);if(r.shape.length===0)return Jp({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,c=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);vN(u,c,i.length,p,r.shape.length,l);let d=bn({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),d}var Kle={kernelName:Eo,backendName:"wasm",kernelFunc:jle,setupFunc:qle},xN;function Xle(e){xN=e.wasm.cwrap(Ho,null,["number","number","number","number","number","number","number","number","array","number","number"])}function 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as CallbackList,Sa as Cast,Ca as Ceil,Ir as ClipByValue,jd as Complex,Kd as ComplexAbs,uo as Concat,Na as Conv2D,lg as Conv2DBackpropFilter,Ta as Conv2DBackpropInput,Xd as Conv3D,cg as Conv3DBackpropFilterV2,dg as Conv3DBackpropInputV2,$a as Cos,_a as Cosh,co as CropAndResize,lo as Cumsum,AL as CustomCallback,Ud as DataStorage,pg as DenseBincount,po as DepthToSpace,Aa as DepthwiseConv2dNative,hg as DepthwiseConv2dNativeBackpropFilter,fg as DepthwiseConv2dNativeBackpropInput,mg as Diag,Yd as Dilation2D,Yf as Dilation2DBackpropFilter,Xf as Dilation2DBackpropInput,jw as ENV,gW as EarlyStopping,Qd as Einsum,Ra as Elu,gg as EluGrad,m$ as Environment,ho as Equal,hl as Erf,Da as Exp,fo as ExpandDims,mo as Expm1,bg as FFT,fl as Fill,go as FlipLeftRight,Fa as Floor,Oa as FloorDiv,fd as FromPixels,Pa as FusedBatchNorm,sa as FusedConv2D,ra as FusedDepthwiseConv2D,jf as GPGPUContext,yo as GatherNd,bo as GatherV2,w4 as GraphModel,vo as Greater,za as GreaterEqual,_L as History,yg as IFFT,Ma as Identity,Zd as Imag,Dt as InputSpec,ml as IsFinite,gl as IsInf,bl as IsNan,nl as KernelBackend,ep as LRN,xg as LRNGrad,xL as LayerVariable,pr as LayersModel,La as LeakyRelu,xo as Less,wo as LessEqual,vg as LinSpace,Ba as Log,yl as Log1p,k$ as LogSoftmax,ko as LogicalAnd,vl as LogicalNot,Jd as LogicalOr,RS as MathBackendCPU,A1 as MathBackendWebGL,Va as Max,Ua as MaxPool,tp as MaxPool3D,kg as MaxPool3DGrad,wg as MaxPoolGrad,Ig as MaxPoolWithArgmax,Wa as Maximum,Ga as Mean,Ha as Min,qa as Minimum,ja as MirrorPad,xl as Mod,bb as MomentumOptimizer,Sg as Multinomial,Ka as Multiply,Io as Neg,Co as NonMaxSuppressionV3,wl as NonMaxSuppressionV4,No as NonMaxSuppressionV5,So as NotEqual,e_ as OP_SCOPE_SUFFIX,$o as OneHot,To as OnesLike,$r as Optimizer,Vr as OptimizerConstructors,_o as Pack,Xa as PadV2,pde as Pool,Ya as Pow,Qa as Prelu,Ao as Prod,yb as RMSPropOptimizer,_r as RNN,kl as Range,H$ as Rank,np as Real,Ea as RealDiv,Il as Reciprocal,YF as Reduction,Za as Relu,ei as Relu6,Eo as Reshape,Ja as ResizeBilinear,Ng as ResizeBilinearGrad,Sl as ResizeNearestNeighbor,Cg as ResizeNearestNeighborGrad,Ro as Reverse,qo as RotateWithOffset,Do as Round,ti as Rsqrt,Ip as SGDOptimizer,Fo as ScatterNd,Oo as Select,Cl as Selu,Wb as Sequential,si as Sigmoid,Nl as Sign,ni as Sin,zo as Sinh,Po as Slice,ii as Softmax,Tl as Softplus,Mo as SpaceToBatchND,sp as SparseFillEmptyRows,$l as SparseReshape,rp as SparseSegmentMean,ap as SparseSegmentSum,ip as SparseToDense,Lo as SplitV,ri as Sqrt,_l as Square,oi as SquaredDifference,di as Step,Bo as StridedSlice,op as StringNGrams,Tg as StringSplit,$g as StringToHashBucketFast,ui as Sub,ai as Sum,$s as SymbolicTensor,Vo as Tan,li as Tanh,et as Tensor,Vt as TensorBuffer,Sr as Tile,Wo as TopK,Uo as Transform,ci as Transpose,_g as Unique,Go as Unpack,up as UnsortedSegmentSum,gd as Variable,Ho as ZerosLike,na as _FusedMatMul,Mt as abs,OA as acos,zA as acosh,ie as add,LA as addN,Bk as all,dm as any,Hu as argMax,GA as argMin,qA as asin,KA as asinh,YA as atan,ZA as atan2,eE as atanh,Ug as avgPool,Gk as avgPool3d,$A as backend,N as backend_util,Ade as basicLSTMCell,ju as batchNorm,vE as batchNorm2d,wE as batchNorm3d,IE as batchNorm4d,Gg as batchToSpaceND,Hk as bincount,rpe as booleanMaskAsync,NE as broadcastArgs,nd as broadcastTo,jo as broadcast_util,xk as browser,De as buffer,fpe as callbacks,ce as cast,_E as ceil,Bn as clipByValue,lr as clone,aa as complex,Ft as concat,RE as concat1d,FE as concat2d,PE as concat3d,ME as concat4d,$M as constraints,qk as conv1d,ua as conv2d,jk as conv2dTranspose,Kk as conv3d,qE as conv3dTranspose,mde as copyRegisteredKernels,qg as cos,Yk as cosh,wI as cosineWindow,Qk as cumsum,js as customGrad,k4 as data,QE as denseBincount,Mk as deprecationWarn,JE as depthToSpace,hp as depthwiseConv2d,gpe as deregisterOp,dp as device_util,Ede as diag,sR as dilation2d,vde as disableDeprecationWarnings,Re as dispose,xde as disposeVariables,xe as div,uR as divNoNan,Rde as dot,H3 as dropout,dR as einsum,fp as elu,yde as enableDebugMode,bde as enableProdMode,q3 as enclosingPowerOfTwo,Ss as engine,X as env,qn as equal,fR as erf,jn as exp,On as expandDims,yR as expm1,Zk as eye,ub as fft,Ol as fill,Tde as findBackend,$de as findBackendFactory,mp as floor,Lk as floorDiv,DX as forceHalfFloat,da as fused,Ku as gather,W3 as gatherND,kk as gather_util,Cde as getBackend,ex as getGradient,Qf as getKernel,Zf as getKernelsForBackend,Npe as getThreadsCount,wK as gpgpu_util,Ode as grad,Pde as grads,Wn as greater,Ko as greaterEqual,Id as ifft,jg as imag,ds as image,ipe as inTopKAsync,PM as initializers,CB as input,_n as io,fI as irfft,Dde as isFinite,Fde as isInf,_R as isNaN,Ht as keep,xs as kernel_impls,yL as layers,Kg as leakyRelu,Jk as less,Xo as lessEqual,AO as linalg,DR as linspace,bpe as loadGraphModel,ppe as loadLayersModel,OR as localResponseNormalization,Kn as log,Xg as log1p,Lde as logSigmoid,eI as logSoftmax,XR as logSumExp,Ds as logicalAnd,Zg as logicalNot,rI as logicalOr,Bde as logicalXor,lpe as losses,We as matMul,Q_ as math,As as max,Jg as maxPool,aI as maxPool3d,sD as maxPoolWithArgmax,Tr as maximum,It as mean,cm as memory,Vde as meshgrid,KV as metrics,hm as min,bp as minimum,lD as mirrorPad,dD as mod,cpe as model,cW as models,eb as moments,ape as movingAverage,V as mul,Wde as multiRNNCell,gD as multinomial,kt as neg,RO as nextFrame,vI as norm,Xu as notEqual,vd as oneHot,zn as ones,Xn as onesLike,L as op,Ude as outerProduct,pi as pad,Gde as pad1d,Hde as pad2d,qde as pad3d,jde as pad4d,Kde as pool,ca as pow,nb as prelu,F_ as print,iI as prod,wde as profile,Xde as rand,Yde as randomGamma,zD as randomNormal,zl as randomUniform,Yu as range,Sde as ready,wd as real,VD as reciprocal,pp as registerBackend,hpe as registerCallbackConstructor,S$ as registerGradient,Al as registerKernel,mpe as registerOp,dW as regularizers,Xs as relu,oI as relu6,Nde as removeBackend,G as reshape,Yn as reverse,Qde as reverse1d,Zde as reverse2d,Jde as reverse3d,epe as reverse4d,lb as rfft,uI as round,lI as rsqrt,Ie as scalar,M3 as scatterND,Sk as scatter_util,cI as selu,JD as separableConv2d,dpe as sequential,ae as serialization,Ide as setBackend,_de as setPlatform,Cpe as setThreadsCount,Ipe as setWasmPath,Spe as setWasmPaths,p5 as setWebGLContext,t3 as setdiff1dAsync,Qy as shared,qs as sigmoid,s3 as sign,upe as signal,dI as sin,pI as sinh,He as slice,ab as slice1d,hI as slice2d,ib as slice3d,kd as slice4d,wt as slice_util,ob as softmax,Pl as softplus,tb as spaceToBatchND,Wc as sparse,xI as sparseToDense,ope as spectral,Ln as split,ln as sqrt,ct as square,mI as squaredDifference,mr as squeeze,Qn as stack,yp as step,k3 as stridedSlice,zf as string,ge as sub,ye as sum,cp as sumOutType,S3 as tan,qu as tanh,hs as tensor,Qt as tensor1d,Ki as tensor2d,sA as tensor3d,tpe as tensor4d,npe as tensor5d,spe as tensor6d,_s as tensor_util,xA as test_util,j as tidy,cs as tile,kde as time,N3 as topk,Fi as train,qe as transpose,cb as truncatedNormal,dx as unique,fde as unregisterGradient,hde as unregisterKernel,A3 as unsortedSegmentSum,Fs as unstack,yn as upcastType,w as util,zde as valueAndGrad,Mde as valueAndGrads,R3 as variable,MR as variableGrads,$pe as version,ype as version_converter,gde as version_core,vpe as version_cpu,t0 as version_layers,Tpe as version_wasm,xpe as version_webgl,wpe as webgl,d5 as webgl_util,Hie as webgpu,vn as where,bI as whereAsync,$t as zeros,je as zerosLike};
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v=n.readSync(b.dataId),x;switch(v[0]){case 0:{x=N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();break}case 1:{x=N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();break}case 2:x=N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(v[1],v[2]);break;case 3:x=N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(v[1],v[2],v[3]);break;default:x=""}if(n.disposeData(b.dataId),x)throw n.disposeData(m.dataId),new Error(x);return m}function wce(e){return _N(e,!0)}var kce={kernelName:sp,backendName:"wasm",setupFunc:$N,kernelFunc:wce};function Ice(e){return _N(e,!1)}var Sce={kernelName:rp,backendName:"wasm",setupFunc:$N,kernelFunc:Ice};function Cce(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:i}=n,o=w.parseAxisParam(i,r.shape)[0],u=N.prepareSplitSize(r,a,o),l=new Array(r.shape.length).fill(0),c=r.shape.slice();return u.map(p=>{let d=[...c];d[o]=p;let h=ba({inputs:{x:r},attrs:{begin:l,size:d},backend:s});return l[o]+=p,h})}var Nce={kernelName:Mo,backendName:"wasm",kernelFunc:Cce},Tce=Kt(ri),$ce=Kt($l),_ce=!0,Ace=mn(oi,_ce),AN;function Ece(e){AN=e.wasm.cwrap(di,null,["number","number","number","number"])}function Rce(e){let{backend:t,inputs:n,attrs:s}=e,{alpha:r}=s,{x:a}=n,i=t.dataIdMap.get(a.dataId).id,o=t.makeOutput(a.shape,a.dtype),u=t.dataIdMap.get(o.dataId).id;return AN(i,r,Ct[a.dtype],u),o}var Dce={kernelName:di,backendName:"wasm",setupFunc:Ece,kernelFunc:Rce},EN;function Fce(e){EN=e.wasm.cwrap(Lo,null,["number","array","number","array","array","array","array","array","number","number"])}function Oce(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{begin:a,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:b,begin:y,end:v,strides:x}=wt.sliceInfo(r.shape,a,i,o,u,l,c,p,d),k;if(m)k=bn({inputs:{x:r},backend:t,attrs:{shape:f}});else if(g||b){w.assert(r.shape.length>=1,()=>`Input must have rank at least 1, 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Identity,Qd as Imag,Dt as InputSpec,fl as IsFinite,ml as IsInf,gl as IsNan,tl as KernelBackend,Jd as LRN,vg as LRNGrad,xL as LayerVariable,pr as LayersModel,La as LeakyRelu,vo as Less,xo as LessEqual,yg as LinSpace,Ba as Log,bl as Log1p,k$ as LogSoftmax,wo as LogicalAnd,yl as LogicalNot,Zd as LogicalOr,RS as MathBackendCPU,A1 as MathBackendWebGL,Va as Max,Ua as MaxPool,ep as MaxPool3D,wg as MaxPool3DGrad,xg as MaxPoolGrad,kg as MaxPoolWithArgmax,Wa as Maximum,Ga as Mean,Ha as Min,qa as Minimum,ja as MirrorPad,vl as Mod,gb as MomentumOptimizer,Ig as Multinomial,Ka as Multiply,ko as Neg,So as NonMaxSuppressionV3,xl as NonMaxSuppressionV4,Co as NonMaxSuppressionV5,Io as NotEqual,e_ as OP_SCOPE_SUFFIX,To as OneHot,No as OnesLike,$r as Optimizer,Vr as OptimizerConstructors,$o as Pack,Xa as PadV2,pde as Pool,Ya as Pow,Qa as Prelu,_o as Prod,bb as RMSPropOptimizer,_r as RNN,wl as Range,H$ as Rank,tp as Real,Ea as RealDiv,kl as Reciprocal,YF as Reduction,Za as Relu,ei as Relu6,Ao as Reshape,Ja 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atan,ZA as atan2,eE as atanh,Wg as avgPool,Gk as avgPool3d,$A as backend,N as backend_util,Ade as basicLSTMCell,qu as batchNorm,vE as batchNorm2d,wE as batchNorm3d,IE as batchNorm4d,Ug as batchToSpaceND,Hk as bincount,rpe as booleanMaskAsync,NE as broadcastArgs,td as broadcastTo,qo as broadcast_util,xk as browser,De as buffer,fpe as callbacks,ce as cast,_E as ceil,Bn as clipByValue,lr as clone,aa as complex,Ft as concat,RE as concat1d,FE as concat2d,PE as concat3d,ME as concat4d,$M as constraints,qk as conv1d,ua as conv2d,jk as conv2dTranspose,Kk as conv3d,qE as conv3dTranspose,mde as copyRegisteredKernels,Hg as cos,Yk as cosh,wI as cosineWindow,Qk as cumsum,js as customGrad,k4 as data,QE as denseBincount,Mk as deprecationWarn,JE as depthToSpace,pp as depthwiseConv2d,gpe as deregisterOp,cp as device_util,Ede as diag,sR as dilation2d,vde as disableDeprecationWarnings,Re as dispose,xde as disposeVariables,xe as div,uR as divNoNan,Rde as dot,H3 as dropout,dR as einsum,hp as elu,yde as enableDebugMode,bde as enableProdMode,q3 as enclosingPowerOfTwo,Ss as engine,X as env,qn as equal,fR as erf,jn as exp,On as expandDims,yR as expm1,Zk as eye,ob as fft,Fl as fill,Tde as findBackend,$de as findBackendFactory,fp as floor,Lk as floorDiv,DX as forceHalfFloat,da as fused,ju as gather,W3 as gatherND,kk as gather_util,Cde as getBackend,Jv as getGradient,Yf as getKernel,Qf as getKernelsForBackend,Npe as getThreadsCount,wK as gpgpu_util,Ode as grad,Pde as grads,Wn as greater,jo as greaterEqual,kd as ifft,qg as imag,ds as image,ipe as inTopKAsync,PM as initializers,CB as input,_n as io,fI as irfft,Dde as isFinite,Fde as isInf,_R as isNaN,Ht as keep,xs as kernel_impls,yL as layers,jg as leakyRelu,Jk as less,Ko as lessEqual,AO as linalg,DR as linspace,bpe as loadGraphModel,ppe as loadLayersModel,OR as localResponseNormalization,Kn as log,Kg as log1p,Lde as logSigmoid,eI as logSoftmax,XR as logSumExp,Ds as logicalAnd,Qg as logicalNot,rI as logicalOr,Bde as logicalXor,lpe as losses,We as matMul,Q_ as math,As as max,Zg as maxPool,aI as maxPool3d,sD as maxPoolWithArgmax,Tr as maximum,It as mean,lm as memory,Vde as meshgrid,KV as metrics,pm as min,gp as minimum,lD as mirrorPad,dD as mod,cpe as model,cW as models,Jg as moments,ape as movingAverage,V as mul,Wde as multiRNNCell,gD as multinomial,kt as neg,RO as nextFrame,vI as norm,Ku as notEqual,yd as oneHot,zn as ones,Xn as onesLike,L as op,Ude as outerProduct,pi as pad,Gde as pad1d,Hde as pad2d,qde as pad3d,jde as pad4d,Kde as pool,ca as pow,tb as prelu,F_ as print,iI as prod,wde as profile,Xde as rand,Yde as randomGamma,zD as randomNormal,Pl as randomUniform,Xu as range,Sde as ready,xd as real,VD as reciprocal,dp as registerBackend,hpe as registerCallbackConstructor,S$ as registerGradient,_l as registerKernel,mpe as registerOp,dW as regularizers,Xs as relu,oI as relu6,Nde as removeBackend,G as reshape,Yn as reverse,Qde as reverse1d,Zde as reverse2d,Jde as reverse3d,epe as reverse4d,ub as rfft,uI as round,lI as rsqrt,Ie as scalar,M3 as scatterND,Sk as scatter_util,cI as selu,JD as separableConv2d,dpe as sequential,ae as serialization,Ide as setBackend,_de as setPlatform,Cpe as setThreadsCount,Ipe as setWasmPath,Spe as setWasmPaths,p5 as setWebGLContext,t3 as setdiff1dAsync,Yy as shared,qs as sigmoid,s3 as sign,upe as signal,dI as sin,pI as sinh,He as slice,rb as slice1d,hI as slice2d,ab as slice3d,wd as slice4d,wt as slice_util,ib as softmax,Ol as softplus,eb as spaceToBatchND,Vc as sparse,xI as sparseToDense,ope as spectral,Ln as split,ln as sqrt,ct as square,mI as squaredDifference,mr as squeeze,Qn as stack,bp as step,k3 as stridedSlice,Pf as string,ge as sub,ye as sum,lp as sumOutType,S3 as tan,Hu as tanh,hs as tensor,Qt as tensor1d,ji as tensor2d,sA as tensor3d,tpe as tensor4d,npe as tensor5d,spe as tensor6d,_s as tensor_util,xA as test_util,j as tidy,cs as tile,kde as time,N3 as topk,Fi as train,qe as transpose,lb as truncatedNormal,cx as unique,fde as unregisterGradient,hde as unregisterKernel,A3 as unsortedSegmentSum,Fs as unstack,yn as upcastType,w as util,zde as valueAndGrad,Mde as valueAndGrads,R3 as variable,MR as variableGrads,$pe as version,ype as version_converter,gde as version_core,vpe as version_cpu,t0 as version_layers,Tpe as version_wasm,xpe as version_webgl,wpe as webgl,d5 as webgl_util,Hie as webgpu,vn as where,bI as whereAsync,$t as zeros,je as zerosLike};
 /**
  * @license
  * Copyright 2017 Google LLC. All Rights Reserved.
diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js
index 2afdc5d6..d947b575 100644
--- a/dist/tfjs.version.js
+++ b/dist/tfjs.version.js
@@ -4,4 +4,4 @@
   author: <https://github.com/vladmandic>'
 */
 
-var e="3.13.0";var s="3.13.0";var t="3.13.0";var r="3.13.0";var l="3.13.0";var i="3.13.0";var a="3.13.0";var n="3.13.0";var G={tfjs:e,"tfjs-core":s,"tfjs-data":t,"tfjs-layers":r,"tfjs-converter":l,"tfjs-backend-cpu":i,"tfjs-backend-webgl":a,"tfjs-backend-wasm":n};export{G as version};
+var e="3.14.0";var s="3.14.0";var t="3.14.0";var r="3.14.0";var l="3.14.0";var i="3.14.0";var a="3.14.0";var n="3.14.0";var G={tfjs:e,"tfjs-core":s,"tfjs-data":t,"tfjs-layers":r,"tfjs-converter":l,"tfjs-backend-cpu":i,"tfjs-backend-webgl":a,"tfjs-backend-wasm":n};export{G as version};
diff --git a/package.json b/package.json
index 26614a57..18301ed4 100644
--- a/package.json
+++ b/package.json
@@ -54,27 +54,27 @@
   ],
   "devDependencies": {
     "@microsoft/api-extractor": "^7.19.4",
-    "@tensorflow/tfjs": "^3.13.0",
-    "@tensorflow/tfjs-backend-cpu": "^3.13.0",
-    "@tensorflow/tfjs-backend-wasm": "^3.13.0",
-    "@tensorflow/tfjs-backend-webgl": "^3.13.0",
+    "@tensorflow/tfjs": "^3.14.0",
+    "@tensorflow/tfjs-backend-cpu": "^3.14.0",
+    "@tensorflow/tfjs-backend-wasm": "^3.14.0",
+    "@tensorflow/tfjs-backend-webgl": "^3.14.0",
     "@tensorflow/tfjs-backend-webgpu": "^0.0.1-alpha.8",
-    "@tensorflow/tfjs-converter": "^3.13.0",
-    "@tensorflow/tfjs-core": "^3.13.0",
-    "@tensorflow/tfjs-data": "^3.13.0",
-    "@tensorflow/tfjs-layers": "^3.13.0",
-    "@tensorflow/tfjs-node": "^3.13.0",
-    "@tensorflow/tfjs-node-gpu": "^3.13.0",
+    "@tensorflow/tfjs-converter": "^3.14.0",
+    "@tensorflow/tfjs-core": "^3.14.0",
+    "@tensorflow/tfjs-data": "^3.14.0",
+    "@tensorflow/tfjs-layers": "^3.14.0",
+    "@tensorflow/tfjs-node": "^3.14.0",
+    "@tensorflow/tfjs-node-gpu": "^3.14.0",
     "@types/node": "^17.0.21",
     "@types/offscreencanvas": "^2019.6.4",
-    "@typescript-eslint/eslint-plugin": "^5.12.1",
-    "@typescript-eslint/parser": "^5.12.1",
-    "@vladmandic/build": "^0.7.0",
-    "@vladmandic/pilogger": "^0.4.2",
+    "@typescript-eslint/eslint-plugin": "^5.14.0",
+    "@typescript-eslint/parser": "^5.14.0",
+    "@vladmandic/build": "^0.7.1",
+    "@vladmandic/pilogger": "^0.4.3",
     "@vladmandic/tfjs": "github:vladmandic/tfjs",
     "canvas": "^2.9.0",
-    "dayjs": "^1.10.7",
-    "esbuild": "^0.14.23",
+    "dayjs": "^1.10.8",
+    "esbuild": "^0.14.25",
     "eslint": "8.10.0",
     "eslint-config-airbnb-base": "^15.0.0",
     "eslint-plugin-html": "^6.2.0",
@@ -82,11 +82,11 @@
     "eslint-plugin-json": "^3.1.0",
     "eslint-plugin-node": "^11.1.0",
     "eslint-plugin-promise": "^6.0.0",
-    "node-fetch": "^3.2.0",
+    "node-fetch": "^3.2.2",
     "rimraf": "^3.0.2",
     "seedrandom": "^3.0.5",
     "tslib": "^2.3.1",
-    "typedoc": "0.22.12",
-    "typescript": "4.5.5"
+    "typedoc": "0.22.13",
+    "typescript": "4.6.2"
   }
 }
diff --git a/test/build.log b/test/build.log
index 253e2dbd..14f897ca 100644
--- a/test/build.log
+++ b/test/build.log
@@ -1,24 +1,24 @@
-2022-02-17 08:14:40 INFO:  Application: {"name":"@vladmandic/human","version":"2.6.3"} 
-2022-02-17 08:14:40 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2022-02-17 08:14:40 INFO:  Toolchain: {"build":"0.7.0","esbuild":"0.14.22","typescript":"4.5.5","typedoc":"0.22.11","eslint":"8.9.0"} 
-2022-02-17 08:14:40 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2022-02-17 08:14:40 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":644} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":64,"inputBytes":562644,"outputBytes":293608} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":648} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":64,"inputBytes":562648,"outputBytes":293612} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":685} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":64,"inputBytes":562685,"outputBytes":293651} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":394} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1068,"outputBytes":615} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":64,"inputBytes":562615,"outputBytes":292361} 
-2022-02-17 08:14:40 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1334770} 
-2022-02-17 08:14:41 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":64,"inputBytes":1896770,"outputBytes":1626294} 
-2022-02-17 08:14:41 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":64,"inputBytes":1896770,"outputBytes":2101597} 
-2022-02-17 08:14:46 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":112} 
-2022-02-17 08:14:48 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":69,"generated":true} 
-2022-02-17 08:14:48 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5863,"outputBytes":2915} 
-2022-02-17 08:14:48 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
-2022-02-17 08:14:55 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":95,"errors":0,"warnings":0} 
-2022-02-17 08:14:56 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2022-02-17 08:14:56 INFO:  Done... 
+2022-03-07 13:22:50 INFO:  Application: {"name":"@vladmandic/human","version":"2.6.4"} 
+2022-03-07 13:22:50 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-03-07 13:22:50 INFO:  Toolchain: {"build":"0.7.1","esbuild":"0.14.25","typescript":"4.6.2","typedoc":"0.22.13","eslint":"8.10.0"} 
+2022-03-07 13:22:50 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
+2022-03-07 13:22:50 STATE: Clean: {"locations":["dist/*","types/lib/*","typedoc/*"]} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":644} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":64,"inputBytes":562645,"outputBytes":293118} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":648} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":64,"inputBytes":562649,"outputBytes":293122} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":685} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":64,"inputBytes":562686,"outputBytes":293161} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":394} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1068,"outputBytes":615} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":64,"inputBytes":562616,"outputBytes":291871} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1335248} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":64,"inputBytes":1897249,"outputBytes":1626282} 
+2022-03-07 13:22:50 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":64,"inputBytes":1897249,"outputBytes":2102106} 
+2022-03-07 13:22:55 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":112} 
+2022-03-07 13:22:57 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":69,"generated":true} 
+2022-03-07 13:22:57 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5863,"outputBytes":2915} 
+2022-03-07 13:22:57 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":15174,"outputBytes":7820} 
+2022-03-07 13:23:05 STATE: Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":95,"errors":0,"warnings":0} 
+2022-03-07 13:23:06 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
+2022-03-07 13:23:06 INFO:  Done... 
diff --git a/test/test.log b/test/test.log
index e68323a4..be5dafce 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,684 +1,684 @@
-2022-02-17 08:15:04 INFO:  @vladmandic/human version 2.6.3 
-2022-02-17 08:15:04 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.4.0 
-2022-02-17 08:15:04 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
-2022-02-17 08:15:04 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
-2022-02-17 08:15:04 INFO:  
-2022-02-17 08:15:04 INFO:  test-node.js start 
-2022-02-17 08:15:04 INFO:  test-node.js test: configuration validation 
-2022-02-17 08:15:04 STATE: test-node.js passed: configuration default validation [] 
-2022-02-17 08:15:04 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-02-17 08:15:04 INFO:  test-node.js test: model load 
-2022-02-17 08:15:04 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-02-17 08:15:04 INFO:  test-node.js test: warmup 
-2022-02-17 08:15:04 STATE: test-node.js passed: create human 
-2022-02-17 08:15:04 INFO:  test-node.js human version: 2.6.3 
-2022-02-17 08:15:04 INFO:  test-node.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-02-17 08:15:04 INFO:  test-node.js tfjs version: 3.13.0 
-2022-02-17 08:15:04 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
-2022-02-17 08:15:04 STATE: test-node.js passed: set backend: tensorflow 
-2022-02-17 08:15:04 STATE: test-node.js tensors 1919 
-2022-02-17 08:15:04 STATE: test-node.js passed: load models 
-2022-02-17 08:15:04 STATE: test-node.js  result: defined models: 22 loaded models: 12 
-2022-02-17 08:15:04 STATE: test-node.js passed: warmup: none default 
-2022-02-17 08:15:04 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-02-17 08:15:04 DATA:  test-node.js  result: performance: load: null total: null 
-2022-02-17 08:15:04 STATE: test-node.js passed: warmup none result match 
-2022-02-17 08:15:04 STATE: test-node.js event: image 
-2022-02-17 08:15:05 STATE: test-node.js event: detect 
-2022-02-17 08:15:05 STATE: test-node.js event: warmup 
-2022-02-17 08:15:05 STATE: test-node.js passed: warmup: face default 
-2022-02-17 08:15:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-02-17 08:15:05 DATA:  test-node.js  result: performance: load: null total: 363 
-2022-02-17 08:15:05 STATE: test-node.js passed: warmup face result match 
-2022-02-17 08:15:05 STATE: test-node.js event: image 
-2022-02-17 08:15:05 STATE: test-node.js event: detect 
-2022-02-17 08:15:05 STATE: test-node.js event: warmup 
-2022-02-17 08:15:05 STATE: test-node.js passed: warmup: body default 
-2022-02-17 08:15:05 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:05 DATA:  test-node.js  result: performance: load: null total: 253 
-2022-02-17 08:15:05 STATE: test-node.js passed: warmup body result match 
-2022-02-17 08:15:05 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-02-17 08:15:05 INFO:  test-node.js test: details verification 
-2022-02-17 08:15:05 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:05 STATE: test-node.js event: image 
-2022-02-17 08:15:06 STATE: test-node.js event: detect 
-2022-02-17 08:15:06 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:06 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:06 DATA:  test-node.js  result: performance: load: null total: 246 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face length 1 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face score 1 0.93 1 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face age/gender 29.6 female 0.91 73.26 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face arrays 4 478 1024 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face anti-spoofing 0.78 
-2022-02-17 08:15:06 STATE: test-node.js passed: details face liveness 0.83 
-2022-02-17 08:15:06 STATE: test-node.js passed: details body length 1 
-2022-02-17 08:15:06 STATE: test-node.js passed: details body 0.92 17 6 
-2022-02-17 08:15:06 STATE: test-node.js passed: details hand length 1 
-2022-02-17 08:15:06 STATE: test-node.js passed: details hand 0.51 0.73 point 
-2022-02-17 08:15:06 STATE: test-node.js passed: details hand arrays 21 5 7 
-2022-02-17 08:15:06 STATE: test-node.js passed: details gesture length 6 
-2022-02-17 08:15:06 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-02-17 08:15:06 STATE: test-node.js passed: details object length 1 
-2022-02-17 08:15:06 STATE: test-node.js passed: details object 0.72 person 
-2022-02-17 08:15:06 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-02-17 08:15:06 STATE: test-node.js event: image 
-2022-02-17 08:15:06 STATE: test-node.js event: detect 
-2022-02-17 08:15:06 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-02-17 08:15:06 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-02-17 08:15:06 STATE: test-node.js event: image 
-2022-02-17 08:15:06 STATE: test-node.js event: detect 
-2022-02-17 08:15:06 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-02-17 08:15:07 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:07 STATE: test-node.js event: image 
-2022-02-17 08:15:07 STATE: test-node.js event: detect 
-2022-02-17 08:15:07 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-02-17 08:15:07 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:07 STATE: test-node.js event: image 
-2022-02-17 08:15:07 STATE: test-node.js event: detect 
-2022-02-17 08:15:07 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-02-17 08:15:07 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-02-17 08:15:07 STATE: test-node.js event: image 
-2022-02-17 08:15:08 STATE: test-node.js event: detect 
-2022-02-17 08:15:08 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-02-17 08:15:08 INFO:  test-node.js test default 
-2022-02-17 08:15:08 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:08 STATE: test-node.js event: image 
-2022-02-17 08:15:08 STATE: test-node.js event: detect 
-2022-02-17 08:15:08 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:08 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:08 DATA:  test-node.js  result: performance: load: null total: 207 
-2022-02-17 08:15:08 STATE: test-node.js passed: default result face match 1 female 0.91 
-2022-02-17 08:15:08 INFO:  test-node.js test sync 
-2022-02-17 08:15:08 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:08 STATE: test-node.js event: image 
-2022-02-17 08:15:08 STATE: test-node.js event: detect 
-2022-02-17 08:15:08 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:08 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:08 DATA:  test-node.js  result: performance: load: null total: 218 
-2022-02-17 08:15:08 STATE: test-node.js passed: default sync 1 female 0.91 
-2022-02-17 08:15:08 INFO:  test-node.js test: image process 
-2022-02-17 08:15:08 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:08 STATE: test-node.js passed: image input null [1,256,256,3] 
-2022-02-17 08:15:08 INFO:  test-node.js test: image null 
-2022-02-17 08:15:08 STATE: test-node.js passed: invalid input could not convert input to tensor 
-2022-02-17 08:15:08 INFO:  test-node.js test face similarity 
-2022-02-17 08:15:08 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:08 STATE: test-node.js event: image 
-2022-02-17 08:15:09 STATE: test-node.js event: detect 
-2022-02-17 08:15:09 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:09 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-02-17 08:15:09 DATA:  test-node.js  result: performance: load: null total: 208 
-2022-02-17 08:15:09 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:09 STATE: test-node.js event: image 
-2022-02-17 08:15:09 STATE: test-node.js event: detect 
-2022-02-17 08:15:09 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:09 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:09 DATA:  test-node.js  result: performance: load: null total: 219 
-2022-02-17 08:15:09 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-02-17 08:15:09 STATE: test-node.js event: image 
-2022-02-17 08:15:09 STATE: test-node.js event: detect 
-2022-02-17 08:15:09 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:09 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-02-17 08:15:09 DATA:  test-node.js  result: performance: load: null total: 232 
-2022-02-17 08:15:09 STATE: test-node.js passed: face descriptor 
-2022-02-17 08:15:09 STATE: test-node.js passed: face similarity {"similarity":[1,0.5632803981762933,0.5194114531612539],"descriptors":[1024,1024,1024]} 
-2022-02-17 08:15:09 INFO:  test-node.js test face matching 
-2022-02-17 08:15:09 STATE: test-node.js passed: face database 40 
-2022-02-17 08:15:09 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.8797924743573865}} {"second":{"index":4,"similarity":0.548081908581344}} {"third":{"index":4,"similarity":0.4999696807961951}} 
-2022-02-17 08:15:09 INFO:  test-node.js test object 
-2022-02-17 08:15:09 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:09 STATE: test-node.js event: image 
-2022-02-17 08:15:10 STATE: test-node.js event: detect 
-2022-02-17 08:15:10 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:10 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:10 DATA:  test-node.js  result: performance: load: null total: 227 
-2022-02-17 08:15:10 STATE: test-node.js passed: object result match 
-2022-02-17 08:15:10 INFO:  test-node.js test sensitive 
-2022-02-17 08:15:10 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:10 STATE: test-node.js event: image 
-2022-02-17 08:15:10 STATE: test-node.js event: detect 
-2022-02-17 08:15:10 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:10 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:10 DATA:  test-node.js  result: performance: load: null total: 222 
-2022-02-17 08:15:10 STATE: test-node.js passed: sensitive result match 
-2022-02-17 08:15:10 STATE: test-node.js passed: sensitive face result match 
-2022-02-17 08:15:10 STATE: test-node.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
-2022-02-17 08:15:10 STATE: test-node.js passed: sensitive body result match 
-2022-02-17 08:15:10 STATE: test-node.js passed: sensitive hand result match 
-2022-02-17 08:15:10 INFO:  test-node.js test detectors 
-2022-02-17 08:15:10 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:10 STATE: test-node.js event: image 
-2022-02-17 08:15:10 STATE: test-node.js event: detect 
-2022-02-17 08:15:10 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:10 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:10 DATA:  test-node.js  result: performance: load: null total: 147 
-2022-02-17 08:15:10 STATE: test-node.js passed: detector result face match 
-2022-02-17 08:15:10 STATE: test-node.js passed: detector result hand match 
-2022-02-17 08:15:10 INFO:  test-node.js test: multi-instance 
-2022-02-17 08:15:10 STATE: test-node.js event: image 
-2022-02-17 08:15:11 STATE: test-node.js event: detect 
-2022-02-17 08:15:11 STATE: test-node.js passed: detect: random default 
-2022-02-17 08:15:11 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-02-17 08:15:11 DATA:  test-node.js  result: performance: load: null total: 137 
-2022-02-17 08:15:11 INFO:  test-node.js test: first instance 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-02-17 08:15:11 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:11 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:11 DATA:  test-node.js  result: performance: load: null total: 138 
-2022-02-17 08:15:11 INFO:  test-node.js test: second instance 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-02-17 08:15:11 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:11 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:11 DATA:  test-node.js  result: performance: load: null total: 137 
-2022-02-17 08:15:11 INFO:  test-node.js test: concurrent 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:11 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-02-17 08:15:12 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-02-17 08:15:12 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:12 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:12 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-02-17 08:15:12 STATE: test-node.js event: image 
-2022-02-17 08:15:12 STATE: test-node.js event: image 
-2022-02-17 08:15:12 STATE: test-node.js event: image 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1379 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1379 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1490 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1489 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1490 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1489 
-2022-02-17 08:15:13 STATE: test-node.js event: detect 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1193 
-2022-02-17 08:15:13 STATE: test-node.js event: detect 
-2022-02-17 08:15:13 STATE: test-node.js event: detect 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1193 
-2022-02-17 08:15:13 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:13 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:13 DATA:  test-node.js  result: performance: load: null total: 1193 
-2022-02-17 08:15:13 INFO:  test-node.js test: monkey-patch 
-2022-02-17 08:15:13 STATE: test-node.js event: image 
-2022-02-17 08:15:13 STATE: test-node.js event: detect 
-2022-02-17 08:15:13 STATE: test-node.js passed: monkey patch 
-2022-02-17 08:15:13 STATE: test-node.js passed: segmentation [65536] 
-2022-02-17 08:15:13 STATE: test-node.js passeed: equal usage 
-2022-02-17 08:15:13 INFO:  test-node.js test: input compare 
-2022-02-17 08:15:13 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:13 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-02-17 08:15:13 STATE: test-node.js passed: image compare 0 23.275441687091504 
-2022-02-17 08:15:13 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
-2022-02-17 08:15:13 INFO:  test-node.js tensors 1925 
-2022-02-17 08:15:13 INFO:  test-node.js test complete: 9067 ms 
-2022-02-17 08:15:13 INFO:  
-2022-02-17 08:15:13 INFO:  test-node-gpu.js start 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js test: configuration validation 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: configuration default validation [] 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js test: model load 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js test: warmup 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: create human 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js human version: 2.6.3 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js tfjs version: 3.13.0 
-2022-02-17 08:15:14 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: set backend: tensorflow 
-2022-02-17 08:15:14 STATE: test-node-gpu.js tensors 1919 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: load models 
-2022-02-17 08:15:14 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: warmup: none default 
-2022-02-17 08:15:14 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-02-17 08:15:14 DATA:  test-node-gpu.js  result: performance: load: null total: null 
-2022-02-17 08:15:14 STATE: test-node-gpu.js passed: warmup none result match 
-2022-02-17 08:15:15 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:18 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:18 STATE: test-node-gpu.js event: warmup 
-2022-02-17 08:15:18 STATE: test-node-gpu.js passed: warmup: face default 
-2022-02-17 08:15:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-02-17 08:15:18 DATA:  test-node-gpu.js  result: performance: load: null total: 3280 
-2022-02-17 08:15:18 STATE: test-node-gpu.js passed: warmup face result match 
-2022-02-17 08:15:18 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:18 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:18 STATE: test-node-gpu.js event: warmup 
-2022-02-17 08:15:18 STATE: test-node-gpu.js passed: warmup: body default 
-2022-02-17 08:15:18 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:18 DATA:  test-node-gpu.js  result: performance: load: null total: 223 
-2022-02-17 08:15:18 STATE: test-node-gpu.js passed: warmup body result match 
-2022-02-17 08:15:18 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-02-17 08:15:18 INFO:  test-node-gpu.js test: details verification 
-2022-02-17 08:15:18 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:18 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:19 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:19 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:19 DATA:  test-node-gpu.js  result: performance: load: null total: 191 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face length 1 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face age/gender 29.6 female 0.91 73.26 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face anti-spoofing 0.78 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details face liveness 0.83 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details body length 1 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details body 0.92 17 6 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details hand length 1 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details gesture length 6 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details object length 1 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: details object 0.72 person 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-02-17 08:15:19 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:19 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-02-17 08:15:19 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:19 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:19 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-02-17 08:15:20 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:20 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:20 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:20 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-02-17 08:15:20 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:20 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:20 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:20 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-02-17 08:15:20 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-02-17 08:15:20 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:21 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-02-17 08:15:21 INFO:  test-node-gpu.js test default 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:21 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:21 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:21 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:21 DATA:  test-node-gpu.js  result: performance: load: null total: 182 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: default result face match 1 female 0.91 
-2022-02-17 08:15:21 INFO:  test-node-gpu.js test sync 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:21 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:21 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:21 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:21 DATA:  test-node-gpu.js  result: performance: load: null total: 197 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: default sync 1 female 0.91 
-2022-02-17 08:15:21 INFO:  test-node-gpu.js test: image process 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
-2022-02-17 08:15:21 INFO:  test-node-gpu.js test: image null 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
-2022-02-17 08:15:21 INFO:  test-node-gpu.js test face similarity 
-2022-02-17 08:15:21 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:21 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:22 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:22 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-02-17 08:15:22 DATA:  test-node-gpu.js  result: performance: load: null total: 190 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:22 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:22 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:22 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:22 DATA:  test-node-gpu.js  result: performance: load: null total: 202 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-02-17 08:15:22 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:22 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:22 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-02-17 08:15:22 DATA:  test-node-gpu.js  result: performance: load: null total: 182 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: face descriptor 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.5630556935525979,0.5193764698386258],"descriptors":[1024,1024,1024]} 
-2022-02-17 08:15:22 INFO:  test-node-gpu.js test face matching 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: face database 40 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.8799972012493786}} {"second":{"index":4,"similarity":0.5479612754605243}} {"third":{"index":4,"similarity":0.5000142632384791}} 
-2022-02-17 08:15:22 INFO:  test-node-gpu.js test object 
-2022-02-17 08:15:22 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:22 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: performance: load: null total: 199 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: object result match 
-2022-02-17 08:15:23 INFO:  test-node-gpu.js test sensitive 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: performance: load: null total: 200 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: sensitive result match 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: sensitive face result match 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: sensitive body result match 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: sensitive hand result match 
-2022-02-17 08:15:23 INFO:  test-node-gpu.js test detectors 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: performance: load: null total: 111 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: detector result face match 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: detector result hand match 
-2022-02-17 08:15:23 INFO:  test-node-gpu.js test: multi-instance 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:23 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: detect: random default 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-02-17 08:15:23 DATA:  test-node-gpu.js  result: performance: load: null total: 105 
-2022-02-17 08:15:23 INFO:  test-node-gpu.js test: first instance 
-2022-02-17 08:15:23 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:24 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:24 DATA:  test-node-gpu.js  result: performance: load: null total: 107 
-2022-02-17 08:15:24 INFO:  test-node-gpu.js test: second instance 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:24 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:24 DATA:  test-node-gpu.js  result: performance: load: null total: 98 
-2022-02-17 08:15:24 INFO:  test-node-gpu.js test: concurrent 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:24 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 1042 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 1042 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 1108 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 1108 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 1108 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 1108 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 870 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 870 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:25 DATA:  test-node-gpu.js  result: performance: load: null total: 870 
-2022-02-17 08:15:25 INFO:  test-node-gpu.js test: monkey-patch 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: image 
-2022-02-17 08:15:25 STATE: test-node-gpu.js event: detect 
-2022-02-17 08:15:25 STATE: test-node-gpu.js passed: monkey patch 
-2022-02-17 08:15:26 STATE: test-node-gpu.js passed: segmentation [65536] 
-2022-02-17 08:15:26 STATE: test-node-gpu.js passeed: equal usage 
-2022-02-17 08:15:26 INFO:  test-node-gpu.js test: input compare 
-2022-02-17 08:15:26 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-02-17 08:15:26 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-02-17 08:15:26 STATE: test-node-gpu.js passed: image compare 0 23.275441687091504 
-2022-02-17 08:15:26 INFO:  test-node-gpu.js events: {"image":21,"detect":21,"warmup":2} 
-2022-02-17 08:15:26 INFO:  test-node-gpu.js tensors 1925 
-2022-02-17 08:15:26 INFO:  test-node-gpu.js test complete: 11706 ms 
-2022-02-17 08:15:27 INFO:  
-2022-02-17 08:15:27 INFO:  test-node-wasm.js start 
-2022-02-17 08:15:27 STATE: test-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
-2022-02-17 08:15:27 INFO:  test-node-wasm.js test: configuration validation 
-2022-02-17 08:15:27 STATE: test-node-wasm.js passed: configuration default validation [] 
-2022-02-17 08:15:27 STATE: test-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-02-17 08:15:27 INFO:  test-node-wasm.js test: model load 
-2022-02-17 08:15:28 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
-2022-02-17 08:15:28 INFO:  test-node-wasm.js test: warmup 
-2022-02-17 08:15:28 STATE: test-node-wasm.js passed: create human 
-2022-02-17 08:15:28 INFO:  test-node-wasm.js human version: 2.6.3 
-2022-02-17 08:15:28 INFO:  test-node-wasm.js platform: linux x64 agent: NodeJS v17.4.0 
-2022-02-17 08:15:28 INFO:  test-node-wasm.js tfjs version: 3.13.0 
-2022-02-17 08:15:28 STATE: test-node-wasm.js passed: set backend: wasm 
-2022-02-17 08:15:28 STATE: test-node-wasm.js tensors 1919 
-2022-02-17 08:15:28 STATE: test-node-wasm.js passed: load models 
-2022-02-17 08:15:28 STATE: test-node-wasm.js  result: defined models: 22 loaded models: 12 
-2022-02-17 08:15:28 STATE: test-node-wasm.js passed: warmup: none default 
-2022-02-17 08:15:28 DATA:  test-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-02-17 08:15:28 DATA:  test-node-wasm.js  result: performance: load: null total: null 
-2022-02-17 08:15:28 STATE: test-node-wasm.js passed: warmup none result match 
-2022-02-17 08:15:28 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:29 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:29 STATE: test-node-wasm.js event: warmup 
-2022-02-17 08:15:29 STATE: test-node-wasm.js passed: warmup: face default 
-2022-02-17 08:15:29 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-02-17 08:15:29 DATA:  test-node-wasm.js  result: performance: load: null total: 531 
-2022-02-17 08:15:29 STATE: test-node-wasm.js passed: warmup face result match 
-2022-02-17 08:15:29 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:29 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:29 STATE: test-node-wasm.js event: warmup 
-2022-02-17 08:15:29 STATE: test-node-wasm.js passed: warmup: body default 
-2022-02-17 08:15:29 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:29 DATA:  test-node-wasm.js  result: performance: load: null total: 357 
-2022-02-17 08:15:29 STATE: test-node-wasm.js passed: warmup body result match 
-2022-02-17 08:15:29 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.91},"emotion":[{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-02-17 08:15:29 INFO:  test-node-wasm.js test: details verification 
-2022-02-17 08:15:29 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:29 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:30 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:30 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:30 DATA:  test-node-wasm.js  result: performance: load: null total: 341 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face length 1 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face age/gender 29.6 female 0.91 73.26 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face anti-spoofing 0.78 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details face liveness 0.83 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details body length 1 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details body 0.92 17 6 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details hand length 1 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details gesture length 6 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details object length 1 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: details object 0.72 person 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2022-02-17 08:15:30 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:30 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:30 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-02-17 08:15:31 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2022-02-17 08:15:31 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:31 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:31 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-02-17 08:15:31 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:31 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:31 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:31 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-02-17 08:15:32 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:32 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:32 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:32 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-02-17 08:15:32 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-02-17 08:15:32 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:33 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:33 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-02-17 08:15:33 INFO:  test-node-wasm.js test default 
-2022-02-17 08:15:33 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:33 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:33 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:33 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:33 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:33 DATA:  test-node-wasm.js  result: performance: load: null total: 315 
-2022-02-17 08:15:33 STATE: test-node-wasm.js passed: default result face match 1 female 0.93 
-2022-02-17 08:15:33 INFO:  test-node-wasm.js test sync 
-2022-02-17 08:15:33 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:33 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:34 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:34 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:34 DATA:  test-node-wasm.js  result: performance: load: null total: 319 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: default sync 1 female 0.93 
-2022-02-17 08:15:34 INFO:  test-node-wasm.js test: image process 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
-2022-02-17 08:15:34 INFO:  test-node-wasm.js test: image null 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
-2022-02-17 08:15:34 INFO:  test-node-wasm.js test face similarity 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-02-17 08:15:34 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:34 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:34 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-02-17 08:15:34 DATA:  test-node-wasm.js  result: performance: load: null total: 339 
-2022-02-17 08:15:34 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:34 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:35 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:35 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:35 DATA:  test-node-wasm.js  result: performance: load: null total: 331 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-02-17 08:15:35 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:35 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:35 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-02-17 08:15:35 DATA:  test-node-wasm.js  result: performance: load: null total: 282 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: face descriptor 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5888036339038205,0.4689331535670693],"descriptors":[1024,1024,1024]} 
-2022-02-17 08:15:35 INFO:  test-node-wasm.js test face matching 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: face database 40 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.8797924799049534}} {"second":{"index":4,"similarity":0.573642308237338}} {"third":{"index":4,"similarity":0.45582039900188964}} 
-2022-02-17 08:15:35 INFO:  test-node-wasm.js test object 
-2022-02-17 08:15:35 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:35 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:36 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:36 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:36 DATA:  test-node-wasm.js  result: performance: load: null total: 312 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: object result match 
-2022-02-17 08:15:36 INFO:  test-node-wasm.js test sensitive 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:36 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:36 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:36 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:36 DATA:  test-node-wasm.js  result: performance: load: null total: 366 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: sensitive result match 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: sensitive face result match 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.54,"emotion":"angry"},{"score":0.2,"emotion":"fear"},{"score":0.16,"emotion":"surprise"}] 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: sensitive body result match 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: sensitive hand result match 
-2022-02-17 08:15:36 INFO:  test-node-wasm.js test detectors 
-2022-02-17 08:15:36 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:36 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:37 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: performance: load: null total: 230 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: detector result face match 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: detector result hand match 
-2022-02-17 08:15:37 INFO:  test-node-wasm.js test: multi-instance 
-2022-02-17 08:15:37 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:37 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: detect: random default 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.07,"keypoints":15} 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: performance: load: null total: 206 
-2022-02-17 08:15:37 INFO:  test-node-wasm.js test: first instance 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: performance: load: null total: 209 
-2022-02-17 08:15:37 INFO:  test-node-wasm.js test: second instance 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-02-17 08:15:37 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:37 DATA:  test-node-wasm.js  result: performance: load: null total: 222 
-2022-02-17 08:15:37 INFO:  test-node-wasm.js test: concurrent 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-02-17 08:15:39 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:39 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:39 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2081 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2081 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2302 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2302 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2302 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2302 
-2022-02-17 08:15:40 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2009 
-2022-02-17 08:15:40 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:40 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2009 
-2022-02-17 08:15:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-02-17 08:15:40 DATA:  test-node-wasm.js  result: performance: load: null total: 2009 
-2022-02-17 08:15:40 INFO:  test-node-wasm.js test: monkey-patch 
-2022-02-17 08:15:40 STATE: test-node-wasm.js event: image 
-2022-02-17 08:15:41 STATE: test-node-wasm.js event: detect 
-2022-02-17 08:15:41 STATE: test-node-wasm.js passed: monkey patch 
-2022-02-17 08:15:41 STATE: test-node-wasm.js passed: segmentation [65536] 
-2022-02-17 08:15:41 STATE: test-node-wasm.js passeed: equal usage 
-2022-02-17 08:15:41 INFO:  test-node-wasm.js test: input compare 
-2022-02-17 08:15:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-02-17 08:15:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-02-17 08:15:41 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
-2022-02-17 08:15:41 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
-2022-02-17 08:15:41 INFO:  test-node-wasm.js tensors 1927 
-2022-02-17 08:15:41 INFO:  test-node-wasm.js test complete: 13868 ms 
-2022-02-17 08:15:41 INFO:  all tests complete 
-2022-02-17 08:15:41 INFO:  failed: {"count":0,"messages":[]} 
-2022-02-17 08:15:41 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
-2022-02-17 08:15:41 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
-2022-02-17 08:15:41 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
+2022-03-07 13:23:11 INFO:  @vladmandic/human version 2.6.4 
+2022-03-07 13:23:11 INFO:  User: vlado Platform: linux Arch: x64 Node: v17.4.0 
+2022-03-07 13:23:11 INFO:  tests: ["test-node.js","test-node-gpu.js","test-node-wasm.js"] 
+2022-03-07 13:23:11 INFO:  demos: ["../demo/nodejs/node.js","../demo/nodejs/node-canvas.js","../demo/nodejs/node-env.js","../demo/nodejs/node-event.js","../demo/nodejs/node-multiprocess.js"] 
+2022-03-07 13:23:11 INFO:  
+2022-03-07 13:23:11 INFO:  test-node.js start 
+2022-03-07 13:23:11 INFO:  test-node.js test: configuration validation 
+2022-03-07 13:23:11 STATE: test-node.js passed: configuration default validation [] 
+2022-03-07 13:23:11 STATE: test-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-03-07 13:23:11 INFO:  test-node.js test: model load 
+2022-03-07 13:23:11 STATE: test-node.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-03-07 13:23:11 INFO:  test-node.js test: warmup 
+2022-03-07 13:23:11 STATE: test-node.js passed: create human 
+2022-03-07 13:23:11 INFO:  test-node.js human version: 2.6.4 
+2022-03-07 13:23:11 INFO:  test-node.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-03-07 13:23:11 INFO:  test-node.js tfjs version: 3.14.0 
+2022-03-07 13:23:11 INFO:  test-node.js tensorflow binding version: 2.7.0-dev20211101 
+2022-03-07 13:23:11 STATE: test-node.js passed: set backend: tensorflow 
+2022-03-07 13:23:11 STATE: test-node.js tensors 1919 
+2022-03-07 13:23:11 STATE: test-node.js passed: load models 
+2022-03-07 13:23:11 STATE: test-node.js  result: defined models: 22 loaded models: 12 
+2022-03-07 13:23:11 STATE: test-node.js passed: warmup: none default 
+2022-03-07 13:23:11 DATA:  test-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-03-07 13:23:11 DATA:  test-node.js  result: performance: load: null total: null 
+2022-03-07 13:23:11 STATE: test-node.js passed: warmup none result match 
+2022-03-07 13:23:11 STATE: test-node.js event: image 
+2022-03-07 13:23:11 STATE: test-node.js event: detect 
+2022-03-07 13:23:11 STATE: test-node.js event: warmup 
+2022-03-07 13:23:11 STATE: test-node.js passed: warmup: face default 
+2022-03-07 13:23:11 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-03-07 13:23:11 DATA:  test-node.js  result: performance: load: null total: 353 
+2022-03-07 13:23:11 STATE: test-node.js passed: warmup face result match 
+2022-03-07 13:23:11 STATE: test-node.js event: image 
+2022-03-07 13:23:11 STATE: test-node.js event: detect 
+2022-03-07 13:23:11 STATE: test-node.js event: warmup 
+2022-03-07 13:23:11 STATE: test-node.js passed: warmup: body default 
+2022-03-07 13:23:11 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:11 DATA:  test-node.js  result: performance: load: null total: 253 
+2022-03-07 13:23:11 STATE: test-node.js passed: warmup body result match 
+2022-03-07 13:23:11 STATE: test-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-03-07 13:23:11 INFO:  test-node.js test: details verification 
+2022-03-07 13:23:12 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:12 STATE: test-node.js event: image 
+2022-03-07 13:23:12 STATE: test-node.js event: detect 
+2022-03-07 13:23:12 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:12 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:12 DATA:  test-node.js  result: performance: load: null total: 241 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face length 1 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face score 1 0.93 1 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face age/gender 29.6 female 0.91 73.26 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face arrays 4 478 1024 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face anti-spoofing 0.78 
+2022-03-07 13:23:12 STATE: test-node.js passed: details face liveness 0.83 
+2022-03-07 13:23:12 STATE: test-node.js passed: details body length 1 
+2022-03-07 13:23:12 STATE: test-node.js passed: details body 0.92 17 6 
+2022-03-07 13:23:12 STATE: test-node.js passed: details hand length 1 
+2022-03-07 13:23:12 STATE: test-node.js passed: details hand 0.51 0.73 point 
+2022-03-07 13:23:12 STATE: test-node.js passed: details hand arrays 21 5 7 
+2022-03-07 13:23:12 STATE: test-node.js passed: details gesture length 6 
+2022-03-07 13:23:12 STATE: test-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-03-07 13:23:12 STATE: test-node.js passed: details object length 1 
+2022-03-07 13:23:12 STATE: test-node.js passed: details object 0.72 person 
+2022-03-07 13:23:12 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-03-07 13:23:12 STATE: test-node.js event: image 
+2022-03-07 13:23:12 STATE: test-node.js event: detect 
+2022-03-07 13:23:12 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-03-07 13:23:13 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-03-07 13:23:13 STATE: test-node.js event: image 
+2022-03-07 13:23:13 STATE: test-node.js event: detect 
+2022-03-07 13:23:13 STATE: test-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-03-07 13:23:13 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:13 STATE: test-node.js event: image 
+2022-03-07 13:23:13 STATE: test-node.js event: detect 
+2022-03-07 13:23:13 STATE: test-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-03-07 13:23:13 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:13 STATE: test-node.js event: image 
+2022-03-07 13:23:14 STATE: test-node.js event: detect 
+2022-03-07 13:23:14 STATE: test-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-03-07 13:23:14 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-03-07 13:23:14 STATE: test-node.js event: image 
+2022-03-07 13:23:14 STATE: test-node.js event: detect 
+2022-03-07 13:23:14 STATE: test-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-03-07 13:23:14 INFO:  test-node.js test default 
+2022-03-07 13:23:14 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:14 STATE: test-node.js event: image 
+2022-03-07 13:23:14 STATE: test-node.js event: detect 
+2022-03-07 13:23:14 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:14 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:14 DATA:  test-node.js  result: performance: load: null total: 220 
+2022-03-07 13:23:14 STATE: test-node.js passed: default result face match 1 female 0.91 
+2022-03-07 13:23:14 INFO:  test-node.js test sync 
+2022-03-07 13:23:15 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:15 STATE: test-node.js event: image 
+2022-03-07 13:23:15 STATE: test-node.js event: detect 
+2022-03-07 13:23:15 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:15 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:15 DATA:  test-node.js  result: performance: load: null total: 214 
+2022-03-07 13:23:15 STATE: test-node.js passed: default sync 1 female 0.91 
+2022-03-07 13:23:15 INFO:  test-node.js test: image process 
+2022-03-07 13:23:15 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:15 STATE: test-node.js passed: image input null [1,256,256,3] 
+2022-03-07 13:23:15 INFO:  test-node.js test: image null 
+2022-03-07 13:23:15 STATE: test-node.js passed: invalid input could not convert input to tensor 
+2022-03-07 13:23:15 INFO:  test-node.js test face similarity 
+2022-03-07 13:23:15 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:15 STATE: test-node.js event: image 
+2022-03-07 13:23:15 STATE: test-node.js event: detect 
+2022-03-07 13:23:15 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:15 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-03-07 13:23:15 DATA:  test-node.js  result: performance: load: null total: 211 
+2022-03-07 13:23:15 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:15 STATE: test-node.js event: image 
+2022-03-07 13:23:15 STATE: test-node.js event: detect 
+2022-03-07 13:23:15 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:15 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:15 DATA:  test-node.js  result: performance: load: null total: 219 
+2022-03-07 13:23:15 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-03-07 13:23:15 STATE: test-node.js event: image 
+2022-03-07 13:23:16 STATE: test-node.js event: detect 
+2022-03-07 13:23:16 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:16 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-03-07 13:23:16 DATA:  test-node.js  result: performance: load: null total: 188 
+2022-03-07 13:23:16 STATE: test-node.js passed: face descriptor 
+2022-03-07 13:23:16 STATE: test-node.js passed: face similarity {"similarity":[1,0.5632803981762933,0.5194114531612539],"descriptors":[1024,1024,1024]} 
+2022-03-07 13:23:16 INFO:  test-node.js test face matching 
+2022-03-07 13:23:16 STATE: test-node.js passed: face database 40 
+2022-03-07 13:23:16 STATE: test-node.js passed: face match {"first":{"index":4,"similarity":0.8797924743573865}} {"second":{"index":4,"similarity":0.548081908581344}} {"third":{"index":4,"similarity":0.4999696807961951}} 
+2022-03-07 13:23:16 INFO:  test-node.js test object 
+2022-03-07 13:23:16 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:16 STATE: test-node.js event: image 
+2022-03-07 13:23:16 STATE: test-node.js event: detect 
+2022-03-07 13:23:16 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:16 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:16 DATA:  test-node.js  result: performance: load: null total: 217 
+2022-03-07 13:23:16 STATE: test-node.js passed: object result match 
+2022-03-07 13:23:16 INFO:  test-node.js test sensitive 
+2022-03-07 13:23:16 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:16 STATE: test-node.js event: image 
+2022-03-07 13:23:16 STATE: test-node.js event: detect 
+2022-03-07 13:23:16 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:16 DATA:  test-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:16 DATA:  test-node.js  result: performance: load: null total: 245 
+2022-03-07 13:23:16 STATE: test-node.js passed: sensitive result match 
+2022-03-07 13:23:16 STATE: test-node.js passed: sensitive face result match 
+2022-03-07 13:23:16 STATE: test-node.js passed: sensitive face emotion result [{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}] 
+2022-03-07 13:23:16 STATE: test-node.js passed: sensitive body result match 
+2022-03-07 13:23:16 STATE: test-node.js passed: sensitive hand result match 
+2022-03-07 13:23:16 INFO:  test-node.js test detectors 
+2022-03-07 13:23:17 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:17 STATE: test-node.js event: image 
+2022-03-07 13:23:17 STATE: test-node.js event: detect 
+2022-03-07 13:23:17 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:17 DATA:  test-node.js  result: performance: load: null total: 141 
+2022-03-07 13:23:17 STATE: test-node.js passed: detector result face match 
+2022-03-07 13:23:17 STATE: test-node.js passed: detector result hand match 
+2022-03-07 13:23:17 INFO:  test-node.js test: multi-instance 
+2022-03-07 13:23:17 STATE: test-node.js event: image 
+2022-03-07 13:23:17 STATE: test-node.js event: detect 
+2022-03-07 13:23:17 STATE: test-node.js passed: detect: random default 
+2022-03-07 13:23:17 DATA:  test-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.05,"keypoints":15} 
+2022-03-07 13:23:17 DATA:  test-node.js  result: performance: load: null total: 150 
+2022-03-07 13:23:17 INFO:  test-node.js test: first instance 
+2022-03-07 13:23:17 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-03-07 13:23:17 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:17 DATA:  test-node.js  result: performance: load: null total: 146 
+2022-03-07 13:23:17 INFO:  test-node.js test: second instance 
+2022-03-07 13:23:17 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-03-07 13:23:17 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:17 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:17 DATA:  test-node.js  result: performance: load: null total: 157 
+2022-03-07 13:23:17 INFO:  test-node.js test: concurrent 
+2022-03-07 13:23:17 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:17 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:18 STATE: test-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-03-07 13:23:18 STATE: test-node.js event: image 
+2022-03-07 13:23:18 STATE: test-node.js event: image 
+2022-03-07 13:23:18 STATE: test-node.js event: image 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1326 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1326 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1437 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1437 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1437 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1437 
+2022-03-07 13:23:19 STATE: test-node.js event: detect 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1194 
+2022-03-07 13:23:19 STATE: test-node.js event: detect 
+2022-03-07 13:23:19 STATE: test-node.js event: detect 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1194 
+2022-03-07 13:23:19 STATE: test-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:19 DATA:  test-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:19 DATA:  test-node.js  result: performance: load: null total: 1194 
+2022-03-07 13:23:19 INFO:  test-node.js test: monkey-patch 
+2022-03-07 13:23:19 STATE: test-node.js event: image 
+2022-03-07 13:23:20 STATE: test-node.js event: detect 
+2022-03-07 13:23:20 STATE: test-node.js passed: monkey patch 
+2022-03-07 13:23:20 STATE: test-node.js passed: segmentation [65536] 
+2022-03-07 13:23:20 STATE: test-node.js passeed: equal usage 
+2022-03-07 13:23:20 INFO:  test-node.js test: input compare 
+2022-03-07 13:23:20 STATE: test-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:20 STATE: test-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-03-07 13:23:20 STATE: test-node.js passed: image compare 0 23.275441687091504 
+2022-03-07 13:23:20 INFO:  test-node.js events: {"image":21,"detect":21,"warmup":2} 
+2022-03-07 13:23:20 INFO:  test-node.js tensors 1925 
+2022-03-07 13:23:20 INFO:  test-node.js test complete: 9066 ms 
+2022-03-07 13:23:20 INFO:  
+2022-03-07 13:23:20 INFO:  test-node-gpu.js start 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js test: configuration validation 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: configuration default validation [] 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js test: model load 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js test: warmup 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: create human 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js human version: 2.6.4 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js platform: linux x64 agent: NodeJS v17.4.0 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js tfjs version: 3.14.0 
+2022-03-07 13:23:21 INFO:  test-node-gpu.js tensorflow binding version: 2.7.0-dev20211101 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: set backend: tensorflow 
+2022-03-07 13:23:21 STATE: test-node-gpu.js tensors 1919 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: load models 
+2022-03-07 13:23:21 STATE: test-node-gpu.js  result: defined models: 22 loaded models: 12 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: warmup: none default 
+2022-03-07 13:23:21 DATA:  test-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-03-07 13:23:21 DATA:  test-node-gpu.js  result: performance: load: null total: null 
+2022-03-07 13:23:21 STATE: test-node-gpu.js passed: warmup none result match 
+2022-03-07 13:23:21 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:25 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:25 STATE: test-node-gpu.js event: warmup 
+2022-03-07 13:23:25 STATE: test-node-gpu.js passed: warmup: face default 
+2022-03-07 13:23:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-03-07 13:23:25 DATA:  test-node-gpu.js  result: performance: load: null total: 3339 
+2022-03-07 13:23:25 STATE: test-node-gpu.js passed: warmup face result match 
+2022-03-07 13:23:25 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:25 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:25 STATE: test-node-gpu.js event: warmup 
+2022-03-07 13:23:25 STATE: test-node-gpu.js passed: warmup: body default 
+2022-03-07 13:23:25 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:25 DATA:  test-node-gpu.js  result: performance: load: null total: 203 
+2022-03-07 13:23:25 STATE: test-node-gpu.js passed: warmup body result match 
+2022-03-07 13:23:25 STATE: test-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.89},"emotion":[{"score":0.35,"emotion":"fear"},{"score":0.28,"emotion":"angry"},{"score":0.15,"emotion":"sad"},{"score":0.1,"emotion":"surprise"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-03-07 13:23:25 INFO:  test-node-gpu.js test: details verification 
+2022-03-07 13:23:25 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:25 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:26 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:26 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:26 DATA:  test-node-gpu.js  result: performance: load: null total: 165 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face length 1 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face score 1 0.93 1 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face age/gender 29.6 female 0.91 73.26 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face arrays 4 478 1024 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face anti-spoofing 0.78 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details face liveness 0.83 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details body length 1 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details body 0.92 17 6 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details hand length 1 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details hand 0.51 0.73 point 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details hand arrays 21 5 7 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details gesture length 6 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details object length 1 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: details object 0.72 person 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-03-07 13:23:26 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:26 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-03-07 13:23:26 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:26 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:26 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-03-07 13:23:27 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:27 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:27 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:27 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-03-07 13:23:27 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:27 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:27 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:27 STATE: test-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-03-07 13:23:27 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-03-07 13:23:27 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:28 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-03-07 13:23:28 INFO:  test-node-gpu.js test default 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:28 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:28 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:28 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:28 DATA:  test-node-gpu.js  result: performance: load: null total: 165 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: default result face match 1 female 0.91 
+2022-03-07 13:23:28 INFO:  test-node-gpu.js test sync 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:28 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:28 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:28 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:28 DATA:  test-node-gpu.js  result: performance: load: null total: 164 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: default sync 1 female 0.91 
+2022-03-07 13:23:28 INFO:  test-node-gpu.js test: image process 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: image input null [1,256,256,3] 
+2022-03-07 13:23:28 INFO:  test-node-gpu.js test: image null 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: invalid input could not convert input to tensor 
+2022-03-07 13:23:28 INFO:  test-node-gpu.js test face similarity 
+2022-03-07 13:23:28 STATE: test-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-03-07 13:23:28 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:29 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-03-07 13:23:29 DATA:  test-node-gpu.js  result: performance: load: null total: 152 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:29 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:29 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:29 DATA:  test-node-gpu.js  result: performance: load: null total: 175 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
+2022-03-07 13:23:29 STATE: test-node-gpu.js event: image 
+2022-03-07 13:23:29 STATE: test-node-gpu.js event: detect 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:29 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-03-07 13:23:29 DATA:  test-node-gpu.js  result: performance: load: null total: 154 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: face descriptor 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: face similarity {"similarity":[1,0.5630857211196999,0.5193140083083794],"descriptors":[1024,1024,1024]} 
+2022-03-07 13:23:29 INFO:  test-node-gpu.js test face matching 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: face database 40 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.8799993617830532}} {"second":{"index":4,"similarity":0.5479640431307572}} {"third":{"index":4,"similarity":0.49994916272803597}} 
+2022-03-07 13:23:29 INFO:  test-node-gpu.js test object 
+2022-03-07 13:23:29 STATE: test-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-03-07 13:23:29 STATE: test-node-gpu.js event: image 
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+2022-03-07 13:23:30 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
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+2022-03-07 13:23:31 DATA:  test-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:31 DATA:  test-node-gpu.js  result: performance: load: null total: 104 
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+2022-03-07 13:23:34 INFO:  
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+2022-03-07 13:23:35 STATE: test-node-wasm.js passed: models loaded 22 12 [{"name":"ssrnetage","loaded":false},{"name":"gear","loaded":false},{"name":"blazeposedetect","loaded":false},{"name":"blazepose","loaded":false},{"name":"centernet","loaded":true},{"name":"efficientpose","loaded":false},{"name":"mobilefacenet","loaded":false},{"name":"emotion","loaded":true},{"name":"facedetect","loaded":true},{"name":"faceiris","loaded":true},{"name":"facemesh","loaded":true},{"name":"faceres","loaded":true},{"name":"ssrnetgender","loaded":false},{"name":"handpose","loaded":false},{"name":"handskeleton","loaded":true},{"name":"handtrack","loaded":true},{"name":"liveness","loaded":true},{"name":"movenet","loaded":true},{"name":"nanodet","loaded":false},{"name":"posenet","loaded":false},{"name":"segmentation","loaded":true},{"name":"antispoof","loaded":true}] 
+2022-03-07 13:23:35 INFO:  test-node-wasm.js test: warmup 
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+2022-03-07 13:23:36 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-03-07 13:23:36 DATA:  test-node-wasm.js  result: performance: load: null total: 491 
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+2022-03-07 13:23:36 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:36 DATA:  test-node-wasm.js  result: performance: load: null total: 362 
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+2022-03-07 13:23:36 STATE: test-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":29.6,"gender":"female","genderScore":0.91},"emotion":[{"score":0.34,"emotion":"fear"},{"score":0.29,"emotion":"angry"},{"score":0.15,"emotion":"sad"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-03-07 13:23:36 INFO:  test-node-wasm.js test: details verification 
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+2022-03-07 13:23:37 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:37 DATA:  test-node-wasm.js  result: performance: load: null total: 358 
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+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details face score 1 0.93 1 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details face age/gender 29.6 female 0.91 73.26 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details face arrays 4 478 1024 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details face emotion 3 {"score":0.34,"emotion":"fear"} 
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+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details body 0.92 17 6 
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+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details hand 0.51 0.73 point 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details hand arrays 21 5 7 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details gesture length 6 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details object length 1 
+2022-03-07 13:23:37 STATE: test-node-wasm.js passed: details object 0.72 person 
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+2022-03-07 13:23:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
+2022-03-07 13:23:38 STATE: test-node-wasm.js event: image 
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+2022-03-07 13:23:38 STATE: test-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-03-07 13:23:38 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:38 STATE: test-node-wasm.js event: image 
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+2022-03-07 13:23:39 STATE: test-node-wasm.js event: image 
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+2022-03-07 13:23:40 STATE: test-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-03-07 13:23:40 INFO:  test-node-wasm.js test default 
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+2022-03-07 13:23:40 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:40 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:40 DATA:  test-node-wasm.js  result: performance: load: null total: 359 
+2022-03-07 13:23:40 STATE: test-node-wasm.js passed: default result face match 1 female 0.93 
+2022-03-07 13:23:40 INFO:  test-node-wasm.js test sync 
+2022-03-07 13:23:40 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
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+2022-03-07 13:23:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:41 DATA:  test-node-wasm.js  result: performance: load: null total: 331 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: default sync 1 female 0.93 
+2022-03-07 13:23:41 INFO:  test-node-wasm.js test: image process 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: image input null [1,256,256,3] 
+2022-03-07 13:23:41 INFO:  test-node-wasm.js test: image null 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: invalid input could not convert input to tensor 
+2022-03-07 13:23:41 INFO:  test-node-wasm.js test face similarity 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-03-07 13:23:41 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:41 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:41 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.6,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-03-07 13:23:41 DATA:  test-node-wasm.js  result: performance: load: null total: 309 
+2022-03-07 13:23:41 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:41 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:42 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:42 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:42 DATA:  test-node-wasm.js  result: performance: load: null total: 319 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-03-07 13:23:42 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:42 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:42 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":29.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-03-07 13:23:42 DATA:  test-node-wasm.js  result: performance: load: null total: 307 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: face descriptor 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: face similarity {"similarity":[1,0.5888036339038205,0.4689331535670693],"descriptors":[1024,1024,1024]} 
+2022-03-07 13:23:42 INFO:  test-node-wasm.js test face matching 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: face database 40 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.8797924799049534}} {"second":{"index":4,"similarity":0.573642308237338}} {"third":{"index":4,"similarity":0.45582039900188964}} 
+2022-03-07 13:23:42 INFO:  test-node-wasm.js test object 
+2022-03-07 13:23:42 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:42 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:43 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:43 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:43 DATA:  test-node-wasm.js  result: performance: load: null total: 346 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: object result match 
+2022-03-07 13:23:43 INFO:  test-node-wasm.js test sensitive 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:43 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:43 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:43 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:43 DATA:  test-node-wasm.js  result: performance: load: null total: 350 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: sensitive result match 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: sensitive face result match 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: sensitive face emotion result [{"score":0.54,"emotion":"angry"},{"score":0.2,"emotion":"fear"},{"score":0.16,"emotion":"surprise"}] 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: sensitive body result match 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: sensitive hand result match 
+2022-03-07 13:23:43 INFO:  test-node-wasm.js test detectors 
+2022-03-07 13:23:43 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:43 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:44 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:44 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:44 DATA:  test-node-wasm.js  result: performance: load: null total: 234 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: detector result face match 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: detector result hand match 
+2022-03-07 13:23:44 INFO:  test-node-wasm.js test: multi-instance 
+2022-03-07 13:23:44 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:44 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: detect: random default 
+2022-03-07 13:23:44 DATA:  test-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0.08,"keypoints":15} 
+2022-03-07 13:23:44 DATA:  test-node-wasm.js  result: performance: load: null total: 211 
+2022-03-07 13:23:44 INFO:  test-node-wasm.js test: first instance 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:44 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:44 DATA:  test-node-wasm.js  result: performance: load: null total: 224 
+2022-03-07 13:23:44 INFO:  test-node-wasm.js test: second instance 
+2022-03-07 13:23:44 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:45 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:45 DATA:  test-node-wasm.js  result: performance: load: null total: 220 
+2022-03-07 13:23:45 INFO:  test-node-wasm.js test: concurrent 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-03-07 13:23:45 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:46 STATE: test-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-03-07 13:23:46 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:46 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:46 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2067 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2067 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2274 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2274 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2274 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 2274 
+2022-03-07 13:23:47 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-upper.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 1 person: 1 {"score":0.96} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":16} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 1956 
+2022-03-07 13:23:47 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:47 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-face.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 2 object: 1 person: 1 {"score":0.91} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":17} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 1956 
+2022-03-07 13:23:47 STATE: test-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 1 person: 1 {"score":0.93} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-03-07 13:23:47 DATA:  test-node-wasm.js  result: performance: load: null total: 1956 
+2022-03-07 13:23:47 INFO:  test-node-wasm.js test: monkey-patch 
+2022-03-07 13:23:47 STATE: test-node-wasm.js event: image 
+2022-03-07 13:23:48 STATE: test-node-wasm.js event: detect 
+2022-03-07 13:23:48 STATE: test-node-wasm.js passed: monkey patch 
+2022-03-07 13:23:48 STATE: test-node-wasm.js passed: segmentation [65536] 
+2022-03-07 13:23:48 STATE: test-node-wasm.js passeed: equal usage 
+2022-03-07 13:23:48 INFO:  test-node-wasm.js test: input compare 
+2022-03-07 13:23:48 STATE: test-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-03-07 13:23:48 STATE: test-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-03-07 13:23:48 STATE: test-node-wasm.js passed: image compare 0 23.280073018790848 
+2022-03-07 13:23:48 INFO:  test-node-wasm.js events: {"image":21,"detect":21,"warmup":2} 
+2022-03-07 13:23:48 INFO:  test-node-wasm.js tensors 1927 
+2022-03-07 13:23:48 INFO:  test-node-wasm.js test complete: 13750 ms 
+2022-03-07 13:23:48 INFO:  all tests complete 
+2022-03-07 13:23:48 INFO:  failed: {"count":0,"messages":[]} 
+2022-03-07 13:23:48 INFO:  status: {"test":"test-node.js","passed":101,"failed":0} 
+2022-03-07 13:23:48 INFO:  status: {"test":"test-node-gpu.js","passed":101,"failed":0} 
+2022-03-07 13:23:48 INFO:  status: {"test":"test-node-wasm.js","passed":102,"failed":0} 
diff --git a/typedoc/assets/main.js b/typedoc/assets/main.js
index 99f331b2..54869f42 100644
--- a/typedoc/assets/main.js
+++ b/typedoc/assets/main.js
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e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",l="^("+s+")?"+o+s+o+s,h="^("+s+")?"+i,p=new RegExp(a),v=new RegExp(l),b=new RegExp(u),y=new RegExp(h),E=/^(.+?)(ss|i)es$/,f=/^(.+?)([^s])s$/,m=/^(.+?)eed$/,T=/^(.+?)(ed|ing)$/,w=/.$/,k=/(at|bl|iz)$/,M=new RegExp("([^aeiouylsz])\\1$"),j=new 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r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var l=s.node.edges["*"];else{var l=new t.TokenSet;s.node.edges["*"]=l}s.str.length==1&&(l.final=!0),i.push({node:l,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var h=s.str.charAt(0),p=s.str.charAt(1),v;p in s.node.edges?v=s.node.edges[p]:(v=new t.TokenSet,s.node.edges[p]=v),s.str.length==1&&(v.final=!0),i.push({node:v,editsRemaining:s.editsRemaining-1,str:h+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,l=0;l<o;l++)for(var h=s[l],p=0;p<u;p++){var v=a[p];if(v==h||h=="*"){var b=n.node.edges[v],y=n.qNode.edges[h],E=b.final&&y.final,f=void 0;v in n.output.edges?(f=n.output.edges[v],f.final=f.final||E):(f=new t.TokenSet,f.final=E,n.output.edges[v]=f),i.push({qNode:y,output:f,node:b})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var l=r.clauses[u],h=null,p=t.Set.empty;l.usePipeline?h=this.pipeline.runString(l.term,{fields:l.fields}):h=[l.term];for(var v=0;v<h.length;v++){var b=h[v];l.term=b;var y=t.TokenSet.fromClause(l),E=this.tokenSet.intersect(y).toArray();if(E.length===0&&l.presence===t.Query.presence.REQUIRED){for(var f=0;f<l.fields.length;f++){var m=l.fields[f];o[m]=t.Set.empty}break}for(var T=0;T<E.length;T++)for(var w=E[T],k=this.invertedIndex[w],M=k._index,f=0;f<l.fields.length;f++){var m=l.fields[f],j=k[m],V=Object.keys(j),B=w+"/"+m,q=new t.Set(V);if(l.presence==t.Query.presence.REQUIRED&&(p=p.union(q),o[m]===void 0&&(o[m]=t.Set.complete)),l.presence==t.Query.presence.PROHIBITED){a[m]===void 0&&(a[m]=t.Set.empty),a[m]=a[m].union(q);continue}if(i[m].upsert(M,l.boost,function(Qe,Ie){return Qe+Ie}),!s[B]){for(var H=0;H<V.length;H++){var $=V[H],P=new t.FieldRef($,m),W=j[$],U;(U=n[P])===void 0?n[P]=new t.MatchData(w,m,W):U.add(w,m,W)}s[B]=!0}}}if(l.presence===t.Query.presence.REQUIRED)for(var f=0;f<l.fields.length;f++){var m=l.fields[f];o[m]=o[m].intersect(p)}}for(var z=t.Set.complete,F=t.Set.empty,u=0;u<this.fields.length;u++){var m=this.fields[u];o[m]&&(z=z.intersect(o[m])),a[m]&&(F=F.union(a[m]))}var c=Object.keys(n),g=[],O=Object.create(null);if(r.isNegated()){c=Object.keys(this.fieldVectors);for(var u=0;u<c.length;u++){var P=c[u],S=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var u=0;u<c.length;u++){var S=t.FieldRef.fromString(c[u]),d=S.docRef;if(!!z.contains(d)&&!F.contains(d)){var x=this.fieldVectors[S],R=i[S.fieldName].similarity(x),D;if((D=O[d])!==void 0)D.score+=R,D.matchData.combine(n[S]);else{var L={ref:d,score:R,matchData:n[S]};O[d]=L,g.push(L)}}}return g.sort(function(Se,ke){return ke.score-Se.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. 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c=Object.keys(n),g=[],O=Object.create(null);if(r.isNegated()){c=Object.keys(this.fieldVectors);for(var u=0;u<c.length;u++){var P=c[u],S=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var u=0;u<c.length;u++){var S=t.FieldRef.fromString(c[u]),d=S.docRef;if(!!z.contains(d)&&!F.contains(d)){var x=this.fieldVectors[S],R=i[S.fieldName].similarity(x),D;if((D=O[d])!==void 0)D.score+=R,D.matchData.combine(n[S]);else{var L={ref:d,score:R,matchData:n[S]};O[d]=L,g.push(L)}}}return g.sort(function(Se,ke){return ke.score-Se.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var l=0;l<i.length;l++){var h=i[l],p=h[0],v=h[1];n[p]=new t.Vector(v)}for(var l=0;l<o.length;l++){var h=o[l],b=h[0],y=h[1];a.insert(b),s[b]=y}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],l=this.tokenizer(u,{fields:[o]}),h=this.pipeline.run(l),p=new t.FieldRef(n,o),v=Object.create(null);this.fieldTermFrequencies[p]=v,this.fieldLengths[p]=0,this.fieldLengths[p]+=h.length;for(var b=0;b<h.length;b++){var y=h[b];if(v[y]==null&&(v[y]=0),v[y]+=1,this.invertedIndex[y]==null){var E=Object.create(null);E._index=this.termIndex,this.termIndex+=1;for(var f=0;f<i.length;f++)E[i[f]]=Object.create(null);this.invertedIndex[y]=E}this.invertedIndex[y][o][n]==null&&(this.invertedIndex[y][o][n]=Object.create(null));for(var m=0;m<this.metadataWhitelist.length;m++){var T=this.metadataWhitelist[m],w=y.metadata[T];this.invertedIndex[y][o][n][T]==null&&(this.invertedIndex[y][o][n][T]=[]),this.invertedIndex[y][o][n][T].push(w)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var l=u[s];n[l]=n[l]/i[l]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],l=new t.Vector,h=this.fieldTermFrequencies[o],p=Object.keys(h),v=p.length,b=this._fields[a].boost||1,y=this._documents[o.docRef].boost||1,E=0;E<v;E++){var f=p[E],m=h[f],T=this.invertedIndex[f]._index,w,k,M;i[f]===void 0?(w=t.idf(this.invertedIndex[f],this.documentCount),i[f]=w):w=i[f],k=w*((this._k1+1)*m)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+m),k*=b,k*=y,M=Math.round(k*1e3)/1e3,l.insert(T,M)}e[o]=l}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var l=0;l<u.length;l++){var h=u[l];this.metadata[i][a][h]==null?this.metadata[i][a][h]=e.metadata[i][a][h]:this.metadata[i][a][h]=this.metadata[i][a][h].concat(e.metadata[i][a][h])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ce=="object"?he.exports=r():e.lunr=r()}(this,function(){return 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 /*!
  * lunr.Builder
  * Copyright (C) 2020 Oliver Nightingale
diff --git a/typedoc/classes/Env.html b/typedoc/classes/Env.html
index 5a49f2b6..90e88d96 100644
--- a/typedoc/classes/Env.html
+++ b/typedoc/classes/Env.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Env | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Env.html">Env</a></li></ul><h1>Class Env</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Env | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Env.html">Env</a></li></ul><h1>Class Env</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Env class that holds detected capabilities</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Env</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class"><a href="Env.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#Canvas" class="tsd-kind-icon">Canvas</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#Image" class="tsd-kind-icon">Image</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#ImageData" class="tsd-kind-icon">Image<wbr/>Data</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#agent" class="tsd-kind-icon">agent</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#backends" class="tsd-kind-icon">backends</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#browser" class="tsd-kind-icon">browser</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#cpu" class="tsd-kind-icon">cpu</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#filter" class="tsd-kind-icon">filter</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#initial" class="tsd-kind-icon">initial</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#kernels" class="tsd-kind-icon">kernels</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#node" class="tsd-kind-icon">node</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#offscreen" class="tsd-kind-icon">offscreen</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#perfadd" class="tsd-kind-icon">perfadd</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#platform" class="tsd-kind-icon">platform</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#tfjs" class="tsd-kind-icon">tfjs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#wasm" class="tsd-kind-icon">wasm</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#webgl" class="tsd-kind-icon">webgl</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#webgpu" class="tsd-kind-icon">webgpu</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Env.html#worker" class="tsd-kind-icon">worker</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="Env.html#updateBackend" class="tsd-kind-icon">update<wbr/>Backend</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Env.html#updateCPU" class="tsd-kind-icon">updateCPU</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">new <wbr/>Env<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L81">src/util/env.ts:81</a></li></ul></aside><h4 class="tsd-returns-title">Returns <a href="Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="Canvas" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Canvas<a href="#Canvas" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Canvas<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">undefined</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L75">src/util/env.ts:75</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>MonkeyPatch for Canvas</p>
diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html
index 065348fb..a386c248 100644
--- a/typedoc/classes/GraphModel.html
+++ b/typedoc/classes/GraphModel.html
@@ -1,20 +1,20 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GraphModel | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="GraphModel.html">GraphModel</a></li></ul><h1>Class GraphModel</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GraphModel | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="GraphModel.html">GraphModel</a></li></ul><h1>Class GraphModel</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>A <code>tf.GraphModel</code> is a directed, acyclic graph built from a
 SavedModel GraphDef and allows inference execution.</p>
 </div><div><p>A <code>tf.GraphModel</code> can only be created by loading from a model converted from
 a <a href="https://www.tensorflow.org/guide/saved_model">TensorFlow SavedModel</a> using
 the command line converter tool and loaded via <code>tf.loadGraphModel</code>.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GraphModel</span></li></ul></section><section class="tsd-panel"><h3>Implements</h3><ul class="tsd-hierarchy"><li><span class="tsd-signature-type">InferenceModel</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class"><a href="GraphModel.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputNodes" class="tsd-kind-icon">input<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputs" class="tsd-kind-icon">inputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#metadata" class="tsd-kind-icon">metadata</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelSignature" class="tsd-kind-icon">model<wbr/>Signature</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelVersion" class="tsd-kind-icon">model<wbr/>Version</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputNodes" class="tsd-kind-icon">output<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputs" class="tsd-kind-icon">outputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#weights" class="tsd-kind-icon">weights</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#disposeIntermediateTensors" class="tsd-kind-icon">dispose<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#execute" class="tsd-kind-icon">execute</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#executeAsync" class="tsd-kind-icon">execute<wbr/>Async</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#getIntermediateTensors" class="tsd-kind-icon">get<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#load" class="tsd-kind-icon">load</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#loadSync" class="tsd-kind-icon">load<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#predict" class="tsd-kind-icon">predict</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#save" class="tsd-kind-icon">save</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">new <wbr/>Graph<wbr/>Model<span class="tsd-signature-symbol">(</span>modelUrl<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span>, loadOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">LoadOptions</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:59</li></ul></aside><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>modelUrl: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GraphModel</span></li></ul></section><section class="tsd-panel"><h3>Implements</h3><ul class="tsd-hierarchy"><li><span class="tsd-signature-type">InferenceModel</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class"><a href="GraphModel.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputNodes" class="tsd-kind-icon">input<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#inputs" class="tsd-kind-icon">inputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#metadata" class="tsd-kind-icon">metadata</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelSignature" class="tsd-kind-icon">model<wbr/>Signature</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#modelVersion" class="tsd-kind-icon">model<wbr/>Version</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputNodes" class="tsd-kind-icon">output<wbr/>Nodes</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#outputs" class="tsd-kind-icon">outputs</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="GraphModel.html#weights" class="tsd-kind-icon">weights</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#disposeIntermediateTensors" class="tsd-kind-icon">dispose<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#execute" class="tsd-kind-icon">execute</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#executeAsync" class="tsd-kind-icon">execute<wbr/>Async</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#getIntermediateTensors" class="tsd-kind-icon">get<wbr/>Intermediate<wbr/>Tensors</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#load" class="tsd-kind-icon">load</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#loadSync" class="tsd-kind-icon">load<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#predict" class="tsd-kind-icon">predict</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="GraphModel.html#save" class="tsd-kind-icon">save</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">new <wbr/>Graph<wbr/>Model<span class="tsd-signature-symbol">(</span>modelUrl<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span>, loadOptions<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">LoadOptions</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:59</li></ul></aside><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>modelUrl: <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>url for the model, or an <code>io.IOHandler</code>.</p>
-</div></div></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> loadOptions: <span class="tsd-signature-type">LoadOptions</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputNodes" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> input<wbr/>Nodes<a href="#inputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">input<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:43</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputs" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> inputs<a href="#inputs" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">inputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><p>Implementation of InferenceModel.inputs</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:45</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="metadata" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> metadata<a href="#metadata" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">metadata<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:48</li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelSignature" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> model<wbr/>Signature<a href="#modelSignature" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Signature<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:49</li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelVersion" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> model<wbr/>Version<a href="#modelVersion" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Version<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:42</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputNodes" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> output<wbr/>Nodes<a href="#outputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">output<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:44</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputs" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> outputs<a href="#outputs" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">outputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><p>Implementation of InferenceModel.outputs</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:46</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="weights" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> weights<a href="#weights" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">weights<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">NamedTensorsMap</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:47</li></ul></aside></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:213</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> loadOptions: <span class="tsd-signature-type">LoadOptions</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="GraphModel.html" class="tsd-signature-type" data-tsd-kind="Class">GraphModel</a></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputNodes" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> input<wbr/>Nodes<a href="#inputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">input<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:43</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="inputs" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> inputs<a href="#inputs" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">inputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><p>Implementation of InferenceModel.inputs</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:45</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="metadata" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> metadata<a href="#metadata" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">metadata<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:48</li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelSignature" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> model<wbr/>Signature<a href="#modelSignature" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Signature<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{}</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:49</li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"></ul></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="modelVersion" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> model<wbr/>Version<a href="#modelVersion" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">model<wbr/>Version<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:42</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputNodes" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> output<wbr/>Nodes<a href="#outputNodes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">output<wbr/>Nodes<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:44</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="outputs" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> outputs<a href="#outputs" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">outputs<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TensorInfo</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><p>Implementation of InferenceModel.outputs</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:46</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="weights" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> weights<a href="#weights" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">weights<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">NamedTensorsMap</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:47</li></ul></aside></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:213</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Releases the memory used by the weight tensors and resourceManager.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="disposeIntermediateTensors" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<wbr/>Intermediate<wbr/>Tensors<a href="#disposeIntermediateTensors" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<wbr/>Intermediate<wbr/>Tensors<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:206</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="disposeIntermediateTensors" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<wbr/>Intermediate<wbr/>Tensors<a href="#disposeIntermediateTensors" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<wbr/>Intermediate<wbr/>Tensors<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:206</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Dispose intermediate tensors for model debugging mode (flag
 KEEP_INTERMEDIATE_TENSORS is true).</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="execute" class="tsd-anchor"></a><h3 class="tsd-anchor-link">execute<a href="#execute" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">execute<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, outputs<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><p>Implementation of InferenceModel.execute</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:175</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="execute" class="tsd-anchor"></a><h3 class="tsd-anchor-link">execute<a href="#execute" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">execute<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, outputs<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><p>Implementation of InferenceModel.execute</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:175</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Executes inference for the model for given input tensors.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
 </dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inputs: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
@@ -29,7 +29,7 @@ outputs array.</p>
 are provided and there is only one default output, otherwise return a
 tensor array. The order of the tensor array is the same as the outputs
 if provided, otherwise the order of outputNodes attribute of the model.</p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="executeAsync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">execute<wbr/>Async<a href="#executeAsync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">execute<wbr/>Async<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, outputs<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:192</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="executeAsync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">execute<wbr/>Async<a href="#executeAsync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">execute<wbr/>Async<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, outputs<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:192</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Executes inference for the model for given input tensors in async
 fashion, use this method when your model contains control flow ops.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
@@ -44,18 +44,18 @@ array.</p>
 </div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></h4><div><p>A Promise of single tensor if provided with a single output or
 no outputs are provided and there is only one default output, otherwise
 return a tensor map.</p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="getIntermediateTensors" class="tsd-anchor"></a><h3 class="tsd-anchor-link">get<wbr/>Intermediate<wbr/>Tensors<a href="#getIntermediateTensors" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">get<wbr/>Intermediate<wbr/>Tensors<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">NamedTensorsMap</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:199</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="getIntermediateTensors" class="tsd-anchor"></a><h3 class="tsd-anchor-link">get<wbr/>Intermediate<wbr/>Tensors<a href="#getIntermediateTensors" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">get<wbr/>Intermediate<wbr/>Tensors<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">NamedTensorsMap</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:199</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Get intermediate tensors for model debugging mode (flag
 KEEP_INTERMEDIATE_TENSORS is true).</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">NamedTensorsMap</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="load" class="tsd-anchor"></a><h3 class="tsd-anchor-link">load<a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">load<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:65</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">NamedTensorsMap</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="load" class="tsd-anchor"></a><h3 class="tsd-anchor-link">load<a href="#load" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">load<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:65</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Loads the model and weight files, construct the in memory weight map and
 compile the inference graph.</p>
-</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="loadSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">load<wbr/>Sync<a href="#loadSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">load<wbr/>Sync<span class="tsd-signature-symbol">(</span>artifacts<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ModelArtifacts</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:72</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="loadSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">load<wbr/>Sync<a href="#loadSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">load<wbr/>Sync<span class="tsd-signature-symbol">(</span>artifacts<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ModelArtifacts</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">boolean</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:72</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Synchronously construct the in memory weight map and
 compile the inference graph. Also initialize hashtable if any.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;, ignoreCI: true}</p>
-</dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>artifacts: <span class="tsd-signature-type">ModelArtifacts</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">boolean</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="predict" class="tsd-anchor"></a><h3 class="tsd-anchor-link">predict<a href="#predict" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">predict<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, config<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">ModelPredictConfig</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><p>Implementation of InferenceModel.predict</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:156</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>artifacts: <span class="tsd-signature-type">ModelArtifacts</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">boolean</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="predict" class="tsd-anchor"></a><h3 class="tsd-anchor-link">predict<a href="#predict" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">predict<span class="tsd-signature-symbol">(</span>inputs<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span>, config<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">ModelPredictConfig</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><p>Implementation of InferenceModel.predict</p><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:156</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Execute the inference for the input tensors.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Models&#39;, subheading: &#39;Classes&#39;}</p>
 </dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>inputs: <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> config: <span class="tsd-signature-type">ModelPredictConfig</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
@@ -65,7 +65,7 @@ model.</p>
 </div></div></li></ul><h4 class="tsd-returns-title">Returns <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">NamedTensorMap</span></h4><div><p>Inference result tensors. The output would be single <code>tf.Tensor</code>
 if model has single output node, otherwise Tensor[] or NamedTensorMap[]
 will be returned for model with multiple outputs.</p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="save" class="tsd-anchor"></a><h3 class="tsd-anchor-link">save<a href="#save" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">save<span class="tsd-signature-symbol">(</span>handlerOrURL<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span>, config<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">SaveConfig</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">SaveResult</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.13.0_@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:117</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="save" class="tsd-anchor"></a><h3 class="tsd-anchor-link">save<a href="#save" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">save<span class="tsd-signature-symbol">(</span>handlerOrURL<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">IOHandler</span>, config<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">SaveConfig</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">SaveResult</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-converter@3.14.0_@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.d.ts:117</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Save the configuration and/or weights of the GraphModel.</p>
 </div><div><p>An <code>IOHandler</code> is an object that has a <code>save</code> method of the proper
 signature defined. The <code>save</code> method manages the storing or
diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html
index b06d87ff..bc2236ad 100644
--- a/typedoc/classes/Human.html
+++ b/typedoc/classes/Human.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Human | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Human.html">Human</a></li></ul><h1>Class Human</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Human | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Human.html">Human</a></li></ul><h1>Class Human</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p><em>Human</em>* library main class</p>
 </div><div><p>All methods and properties are available only as members of Human class</p>
 <ul>
diff --git a/typedoc/classes/Models.html b/typedoc/classes/Models.html
index 2931b179..ea9da2c9 100644
--- a/typedoc/classes/Models.html
+++ b/typedoc/classes/Models.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Models | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Models.html">Models</a></li></ul><h1>Class Models</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Models | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Models.html">Models</a></li></ul><h1>Class Models</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Instances of all possible TFJS Graph Models used by Human</p>
 <ul>
 <li>loaded as needed based on configuration</li>
diff --git a/typedoc/classes/Tensor.html b/typedoc/classes/Tensor.html
index 23caedaf..68ff13b1 100644
--- a/typedoc/classes/Tensor.html
+++ b/typedoc/classes/Tensor.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Tensor | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Tensor.html">Tensor</a></li></ul><h1>Class Tensor&lt;R&gt;</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Tensor | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Tensor.html">Tensor</a></li></ul><h1>Class Tensor&lt;R&gt;</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>A <code>tf.Tensor</code> object represents an immutable, multidimensional array of
 numbers that has a shape and a data type.</p>
 </div><div><p>For performance reasons, functions that create tensors do not necessarily
@@ -8,55 +8,55 @@ a feature and is not supported. To avoid this behavior, use the tensor before
 changing the input data or create a copy with <code>copy = tf.add(yourTensor, 0)</code>.</p>
 <p>See <code>tf.tensor</code> for details on how to create a <code>tf.Tensor</code>.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div></section><section class="tsd-panel tsd-type-parameters"><h3>Type parameters</h3><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol">: </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Tensor</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dataId" class="tsd-kind-icon">data<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dtype" class="tsd-kind-icon">dtype</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#isDisposed" class="tsd-kind-icon">is<wbr/>Disposed</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#kept" class="tsd-kind-icon">kept</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rank" class="tsd-kind-icon">rank</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rankType" class="tsd-kind-icon">rank<wbr/>Type</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#scopeId" class="tsd-kind-icon">scope<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#shape" class="tsd-kind-icon">shape</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#size" class="tsd-kind-icon">size</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#strides" class="tsd-kind-icon">strides</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#array" class="tsd-kind-icon">array</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#arraySync" class="tsd-kind-icon">array<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#buffer" class="tsd-kind-icon">buffer</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#bufferSync" class="tsd-kind-icon">buffer<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#bytes" class="tsd-kind-icon">bytes</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#clone" class="tsd-kind-icon">clone</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#data" class="tsd-kind-icon">data</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#dataSync" class="tsd-kind-icon">data<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dataToGPU" class="tsd-kind-icon">data<wbr/>ToGPU</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#print" class="tsd-kind-icon">print</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#throwIfDisposed" class="tsd-kind-icon">throw<wbr/>If<wbr/>Disposed</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#toString" class="tsd-kind-icon">to<wbr/>String</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#variable" class="tsd-kind-icon">variable</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">new <wbr/>Tensor<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span>, dtype<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span>, dataId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">object</span>, id<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:162</li></ul></aside><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol">: </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>shape: <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h5></li><li><h5>dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li><li><h5>dataId: <span class="tsd-signature-type">object</span></h5></li><li><h5>id: <span class="tsd-signature-type">number</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dataId" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Id<a href="#dataId" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">data<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:143</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div></section><section class="tsd-panel tsd-type-parameters"><h3>Type parameters</h3><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol">: </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Tensor</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Constructors</h3><ul class="tsd-index-list"><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#constructor" class="tsd-kind-icon">constructor</a></li></ul></section><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dataId" class="tsd-kind-icon">data<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dtype" class="tsd-kind-icon">dtype</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#isDisposed" class="tsd-kind-icon">is<wbr/>Disposed</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#kept" class="tsd-kind-icon">kept</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rank" class="tsd-kind-icon">rank</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rankType" class="tsd-kind-icon">rank<wbr/>Type</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#scopeId" class="tsd-kind-icon">scope<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#shape" class="tsd-kind-icon">shape</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#size" class="tsd-kind-icon">size</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#strides" class="tsd-kind-icon">strides</a></li></ul></section><section class="tsd-index-section "><h3>Methods</h3><ul class="tsd-index-list"><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#array" class="tsd-kind-icon">array</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#arraySync" class="tsd-kind-icon">array<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#buffer" class="tsd-kind-icon">buffer</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#bufferSync" class="tsd-kind-icon">buffer<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#bytes" class="tsd-kind-icon">bytes</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#clone" class="tsd-kind-icon">clone</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#data" class="tsd-kind-icon">data</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#dataSync" class="tsd-kind-icon">data<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dataToGPU" class="tsd-kind-icon">data<wbr/>ToGPU</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#print" class="tsd-kind-icon">print</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#throwIfDisposed" class="tsd-kind-icon">throw<wbr/>If<wbr/>Disposed</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#toString" class="tsd-kind-icon">to<wbr/>String</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#variable" class="tsd-kind-icon">variable</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Constructors</h2><section class="tsd-panel tsd-member tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a id="constructor" class="tsd-anchor"></a><h3 class="tsd-anchor-link">constructor<a href="#constructor" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">new <wbr/>Tensor<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>shape<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span>, dtype<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span>, dataId<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">object</span>, id<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:162</li></ul></aside><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>R<span class="tsd-signature-symbol">: </span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a> = <a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>shape: <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h5></li><li><h5>dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li><li><h5>dataId: <span class="tsd-signature-type">object</span></h5></li><li><h5>id: <span class="tsd-signature-type">number</span></h5></li></ul><h4 class="tsd-returns-title">Returns <a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dataId" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Id<a href="#dataId" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">data<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">object</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:143</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Id of the bucket holding the data for this tensor. Multiple arrays can
 point to the same bucket (e.g. when calling array.reshape()).</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dtype" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> dtype<a href="#dtype" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:149</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="dtype" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> dtype<a href="#dtype" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">dtype<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:149</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>The data type for the array.</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="id" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> id<a href="#id" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:138</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="id" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> id<a href="#id" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:138</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Unique id of this tensor.</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="isDisposed" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> is<wbr/>Disposed<a href="#isDisposed" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">is<wbr/>Disposed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:237</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="kept" class="tsd-anchor"></a><h3 class="tsd-anchor-link">kept<a href="#kept" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">kept<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:153</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="isDisposed" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> is<wbr/>Disposed<a href="#isDisposed" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">is<wbr/>Disposed<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:237</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="kept" class="tsd-anchor"></a><h3 class="tsd-anchor-link">kept<a href="#kept" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">kept<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:153</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Whether this tensor has been globally kept.</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="rank" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> rank<a href="#rank" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">rank<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:163</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="rankType" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> rank<wbr/>Type<a href="#rankType" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">rank<wbr/>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:151</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="rank" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> rank<a href="#rank" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">rank<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:163</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="rankType" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> rank<wbr/>Type<a href="#rankType" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">rank<wbr/>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:151</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>The rank type for the array (see <code>Rank</code> enum).</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="scopeId" class="tsd-anchor"></a><h3 class="tsd-anchor-link">scope<wbr/>Id<a href="#scopeId" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">scope<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:155</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="scopeId" class="tsd-anchor"></a><h3 class="tsd-anchor-link">scope<wbr/>Id<a href="#scopeId" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">scope<wbr/>Id<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:155</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>The id of the scope this tensor is being tracked in.</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="shape" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> shape<a href="#shape" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">shape<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:145</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="shape" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> shape<a href="#shape" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">shape<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">ShapeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:145</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>The shape of the tensor.</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="size" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> size<a href="#size" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">size<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:147</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="size" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> size<a href="#size" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">size<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:147</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Number of elements in the tensor.</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="strides" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> strides<a href="#strides" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">strides<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:161</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-class"><a id="strides" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagReadonly">Readonly</span> strides<a href="#strides" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">strides<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:161</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Number of elements to skip in each dimension when indexing. See
 <a href="https://docs.scipy.org/doc/numpy/reference/generated/%5C">https://docs.scipy.org/doc/numpy/reference/generated/\</a>
 numpy.ndarray.strides.html</p>
-</div></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="array" class="tsd-anchor"></a><h3 class="tsd-anchor-link">array<a href="#array" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">array<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:181</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Methods</h2><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="array" class="tsd-anchor"></a><h3 class="tsd-anchor-link">array<a href="#array" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">array<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:181</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns the tensor data as a nested array. The transfer of data is done
 asynchronously.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="arraySync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">array<wbr/>Sync<a href="#arraySync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">array<wbr/>Sync<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:188</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="arraySync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">array<wbr/>Sync<a href="#arraySync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">array<wbr/>Sync<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:188</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns the tensor data as a nested array. The transfer of data is done
 synchronously.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="buffer" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<a href="#buffer" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:169</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">ArrayMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="buffer" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<a href="#buffer" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:169</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a promise of <code>tf.TensorBuffer</code> that holds the underlying data.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="bufferSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<wbr/>Sync<a href="#bufferSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:174</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="bufferSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">buffer<wbr/>Sync<a href="#bufferSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">buffer<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:174</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a <code>tf.TensorBuffer</code> that holds the underlying data.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="bytes" class="tsd-anchor"></a><h3 class="tsd-anchor-link">bytes<a href="#bytes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">bytes<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:229</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">&quot;float32&quot;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">TensorBuffer</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="bytes" class="tsd-anchor"></a><h3 class="tsd-anchor-link">bytes<a href="#bytes" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">bytes<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:229</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns the underlying bytes of the tensor&#39;s data.</p>
-</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="clone" class="tsd-anchor"></a><h3 class="tsd-anchor-link">clone<a href="#clone" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">clone<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:252</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="clone" class="tsd-anchor"></a><h3 class="tsd-anchor-link">clone<a href="#clone" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">clone<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span>this<span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:252</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a copy of the tensor. See <code>tf.clone</code> for details.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>T<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="data" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<a href="#data" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:195</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>T<span class="tsd-signature-symbol">: </span><a href="Tensor.html" class="tsd-signature-type" data-tsd-kind="Class">Tensor</a><span class="tsd-signature-symbol">&lt;</span><a href="../enums/Rank.html" class="tsd-signature-type" data-tsd-kind="Enumeration">Rank</a><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5>this: <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type" data-tsd-kind="Type parameter">T</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="data" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<a href="#data" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:195</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Asynchronously downloads the values from the <code>tf.Tensor</code>. Returns a
 promise of <code>TypedArray</code> that resolves when the computation has finished.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="dataSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Sync<a href="#dataSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:227</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Promise</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a id="dataSync" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>Sync<a href="#dataSync" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><li class="tsd-signature tsd-kind-icon">data<wbr/>Sync<span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:227</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Synchronously downloads the values from the <code>tf.Tensor</code>. This blocks the
 UI thread until the values are ready, which can cause performance issues.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dataToGPU" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>ToGPU<a href="#dataToGPU" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">data<wbr/>ToGPU<span class="tsd-signature-symbol">(</span>options<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">DataToGPUWebGLOption</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">GPUData</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:220</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-type-parameters-title">Type parameters</h4><ul class="tsd-type-parameters"><li><h4>D<span class="tsd-signature-symbol">: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span> = <span class="tsd-signature-type">NumericDataType</span></h4></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">[</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">D</span><span class="tsd-signature-symbol">]</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dataToGPU" class="tsd-anchor"></a><h3 class="tsd-anchor-link">data<wbr/>ToGPU<a href="#dataToGPU" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">data<wbr/>ToGPU<span class="tsd-signature-symbol">(</span>options<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">DataToGPUWebGLOption</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">GPUData</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:220</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Copy the tensor&#39;s data to a new GPU resource. Comparing to the <code>dataSync()</code>
 and <code>data()</code>, this method prevents data from being downloaded to CPU.</p>
 </div><div><p>For WebGL backend, the data will be stored on a densely packed texture.
@@ -71,16 +71,16 @@ This means that the texture will use the RGBA channels to store value.</p>
     }
     Remember to dispose the GPUData after it is used by
     <code>res.tensorRef.dispose()</code>.</p>
-</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:235</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="dispose" class="tsd-anchor"></a><h3 class="tsd-anchor-link">dispose<a href="#dispose" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">dispose<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:235</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Disposes <code>tf.Tensor</code> from memory.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="print" class="tsd-anchor"></a><h3 class="tsd-anchor-link">print<a href="#print" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">print<span class="tsd-signature-symbol">(</span>verbose<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:247</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</dd></dl></div><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="print" class="tsd-anchor"></a><h3 class="tsd-anchor-link">print<a href="#print" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">print<span class="tsd-signature-symbol">(</span>verbose<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:247</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Prints the <code>tf.Tensor</code>. See <code>tf.print</code> for details.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
 </dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> verbose: <span class="tsd-signature-type">boolean</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Whether to print verbose information about the tensor,
    including dtype and size.</p>
-</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="throwIfDisposed" class="tsd-anchor"></a><h3 class="tsd-anchor-link">throw<wbr/>If<wbr/>Disposed<a href="#throwIfDisposed" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">throw<wbr/>If<wbr/>Disposed<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:238</li></ul></aside><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="toString" class="tsd-anchor"></a><h3 class="tsd-anchor-link">to<wbr/>String<a href="#toString" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">to<wbr/>String<span class="tsd-signature-symbol">(</span>verbose<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:258</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="throwIfDisposed" class="tsd-anchor"></a><h3 class="tsd-anchor-link">throw<wbr/>If<wbr/>Disposed<a href="#throwIfDisposed" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">throw<wbr/>If<wbr/>Disposed<span class="tsd-signature-symbol">(</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">void</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:238</li></ul></aside><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">void</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="toString" class="tsd-anchor"></a><h3 class="tsd-anchor-link">to<wbr/>String<a href="#toString" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">to<wbr/>String<span class="tsd-signature-symbol">(</span>verbose<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">string</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:258</li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Returns a human-readable description of the tensor. Useful for logging.</p>
 </div><dl class="tsd-comment-tags"><dt>doc</dt><dd><p>{heading: &#39;Tensors&#39;, subheading: &#39;Classes&#39;}</p>
-</dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> verbose: <span class="tsd-signature-type">boolean</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="variable" class="tsd-anchor"></a><h3 class="tsd-anchor-link">variable<a href="#variable" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">variable<span class="tsd-signature-symbol">(</span>trainable<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span>, name<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span>, dtype<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Variable</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:259</li></ul></aside><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> trainable: <span class="tsd-signature-type">boolean</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> name: <span class="tsd-signature-type">string</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Variable</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-class tsd-has-type-parameter"><a href="Tensor.html" class="tsd-kind-icon">Tensor</a><ul><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#constructor" class="tsd-kind-icon">constructor</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dataId" class="tsd-kind-icon">data<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dtype" class="tsd-kind-icon">dtype</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#isDisposed" class="tsd-kind-icon">is<wbr/>Disposed</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#kept" class="tsd-kind-icon">kept</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rank" class="tsd-kind-icon">rank</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rankType" class="tsd-kind-icon">rank<wbr/>Type</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#scopeId" class="tsd-kind-icon">scope<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#shape" class="tsd-kind-icon">shape</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#size" class="tsd-kind-icon">size</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#strides" class="tsd-kind-icon">strides</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#array" class="tsd-kind-icon">array</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#arraySync" class="tsd-kind-icon">array<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#buffer" class="tsd-kind-icon">buffer</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#bufferSync" class="tsd-kind-icon">buffer<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#bytes" class="tsd-kind-icon">bytes</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#clone" class="tsd-kind-icon">clone</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#data" class="tsd-kind-icon">data</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#dataSync" class="tsd-kind-icon">data<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dataToGPU" class="tsd-kind-icon">data<wbr/>ToGPU</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#print" class="tsd-kind-icon">print</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#throwIfDisposed" class="tsd-kind-icon">throw<wbr/>If<wbr/>Disposed</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#toString" class="tsd-kind-icon">to<wbr/>String</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#variable" class="tsd-kind-icon">variable</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
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+</dd></dl></div><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> verbose: <span class="tsd-signature-type">boolean</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">string</span></h4></li></ul></section><section class="tsd-panel tsd-member tsd-kind-method tsd-parent-kind-class"><a id="variable" class="tsd-anchor"></a><h3 class="tsd-anchor-link">variable<a href="#variable" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><ul class="tsd-signatures tsd-kind-method tsd-parent-kind-class"><li class="tsd-signature tsd-kind-icon">variable<span class="tsd-signature-symbol">(</span>trainable<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">boolean</span>, name<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span>, dtype<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span><span class="tsd-signature-symbol">)</span><span class="tsd-signature-symbol">: </span><span class="tsd-signature-type">Variable</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></li></ul><ul class="tsd-descriptions"><li class="tsd-description"><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts:259</li></ul></aside><h4 class="tsd-parameters-title">Parameters</h4><ul class="tsd-parameters"><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> trainable: <span class="tsd-signature-type">boolean</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> name: <span class="tsd-signature-type">string</span></h5></li><li><h5><span class="tsd-flag ts-flagOptional">Optional</span> dtype: <span class="tsd-signature-symbol">keyof </span><span class="tsd-signature-type">DataTypeMap</span></h5></li></ul><h4 class="tsd-returns-title">Returns <span class="tsd-signature-type">Variable</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type" data-tsd-kind="Type parameter">R</span><span class="tsd-signature-symbol">&gt;</span></h4></li></ul></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-class tsd-has-type-parameter"><a href="Tensor.html" class="tsd-kind-icon">Tensor</a><ul><li class="tsd-kind-constructor tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#constructor" class="tsd-kind-icon">constructor</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dataId" class="tsd-kind-icon">data<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#dtype" class="tsd-kind-icon">dtype</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#isDisposed" class="tsd-kind-icon">is<wbr/>Disposed</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#kept" class="tsd-kind-icon">kept</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rank" class="tsd-kind-icon">rank</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#rankType" class="tsd-kind-icon">rank<wbr/>Type</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#scopeId" class="tsd-kind-icon">scope<wbr/>Id</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#shape" class="tsd-kind-icon">shape</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#size" class="tsd-kind-icon">size</a></li><li class="tsd-kind-property tsd-parent-kind-class"><a href="Tensor.html#strides" class="tsd-kind-icon">strides</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#array" class="tsd-kind-icon">array</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#arraySync" class="tsd-kind-icon">array<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#buffer" class="tsd-kind-icon">buffer</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#bufferSync" class="tsd-kind-icon">buffer<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#bytes" class="tsd-kind-icon">bytes</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#clone" class="tsd-kind-icon">clone</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#data" class="tsd-kind-icon">data</a></li><li class="tsd-kind-method tsd-parent-kind-class tsd-has-type-parameter"><a href="Tensor.html#dataSync" class="tsd-kind-icon">data<wbr/>Sync</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dataToGPU" class="tsd-kind-icon">data<wbr/>ToGPU</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#dispose" class="tsd-kind-icon">dispose</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#print" class="tsd-kind-icon">print</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#throwIfDisposed" class="tsd-kind-icon">throw<wbr/>If<wbr/>Disposed</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#toString" class="tsd-kind-icon">to<wbr/>String</a></li><li class="tsd-kind-method tsd-parent-kind-class"><a href="Tensor.html#variable" class="tsd-kind-icon">variable</a></li></ul></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
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-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Rank | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Rank.html">Rank</a></li></ul><h1>Enumeration Rank</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Enumeration members</h3><ul class="tsd-index-list"><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R2" class="tsd-kind-icon">R2</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R3" class="tsd-kind-icon">R3</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R4" class="tsd-kind-icon">R4</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R5" class="tsd-kind-icon">R5</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R6" class="tsd-kind-icon">R6</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Enumeration members</h2><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R0" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R0<a href="#R0" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R0<span class="tsd-signature-symbol"> = &quot;R0&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:61</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R1" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R1<a href="#R1" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R1<span class="tsd-signature-symbol"> = &quot;R1&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:62</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R2" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R2<a href="#R2" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R2<span class="tsd-signature-symbol"> = &quot;R2&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:63</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R3" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R3<a href="#R3" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R3<span class="tsd-signature-symbol"> = &quot;R3&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:64</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R4" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R4<a href="#R4" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R4<span class="tsd-signature-symbol"> = &quot;R4&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in 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node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:67</li></ul></aside></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-enum"><a href="Rank.html" class="tsd-kind-icon">Rank</a><ul><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R2" class="tsd-kind-icon">R2</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R3" class="tsd-kind-icon">R3</a></li><li 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+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Rank | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Rank.html">Rank</a></li></ul><h1>Enumeration Rank</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Enumeration members</h3><ul class="tsd-index-list"><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R2" class="tsd-kind-icon">R2</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R3" class="tsd-kind-icon">R3</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R4" class="tsd-kind-icon">R4</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R5" class="tsd-kind-icon">R5</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R6" class="tsd-kind-icon">R6</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Enumeration members</h2><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R0" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R0<a href="#R0" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R0<span class="tsd-signature-symbol"> = &quot;R0&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:61</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R1" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R1<a href="#R1" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R1<span class="tsd-signature-symbol"> = &quot;R1&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:62</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R2" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R2<a href="#R2" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R2<span class="tsd-signature-symbol"> = &quot;R2&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:63</li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-enum-member tsd-parent-kind-enum"><a id="R3" class="tsd-anchor"></a><h3 class="tsd-anchor-link">R3<a href="#R3" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">R3<span class="tsd-signature-symbol"> = &quot;R3&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in 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node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:67</li></ul></aside></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="../modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="current tsd-kind-enum"><a href="Rank.html" class="tsd-kind-icon">Rank</a><ul><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R0" class="tsd-kind-icon">R0</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R1" class="tsd-kind-icon">R1</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R2" class="tsd-kind-icon">R2</a></li><li class="tsd-kind-enum-member tsd-parent-kind-enum"><a href="Rank.html#R3" class="tsd-kind-icon">R3</a></li><li 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value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
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diff --git a/typedoc/index.html b/typedoc/index.html
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-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>@vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="assets/style.css"/><link rel="stylesheet" href="assets/highlight.css"/><script async src="assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base="."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><h1>@vladmandic/human - v2.6.3</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>References</h3><ul class="tsd-index-list"><li class="tsd-kind-reference"><a href="index.html#default" class="tsd-kind-icon">default</a></li></ul></section><section class="tsd-index-section "><h3>Namespaces</h3><ul class="tsd-index-list"><li class="tsd-kind-namespace"><a href="modules/Tensor.html" class="tsd-kind-icon">Tensor</a></li></ul></section><section class="tsd-index-section "><h3>Enumerations</h3><ul class="tsd-index-list"><li class="tsd-kind-enum"><a href="enums/Rank.html" class="tsd-kind-icon">Rank</a></li></ul></section><section class="tsd-index-section "><h3>Classes</h3><ul class="tsd-index-list"><li class="tsd-kind-class"><a href="classes/Env.html" class="tsd-kind-icon">Env</a></li><li class="tsd-kind-class"><a href="classes/GraphModel.html" class="tsd-kind-icon">Graph<wbr/>Model</a></li><li class="tsd-kind-class"><a href="classes/Human.html" class="tsd-kind-icon">Human</a></li><li class="tsd-kind-class"><a href="classes/Models.html" class="tsd-kind-icon">Models</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/Tensor.html" class="tsd-kind-icon">Tensor</a></li></ul></section><section class="tsd-index-section "><h3>Interfaces</h3><ul class="tsd-index-list"><li class="tsd-kind-interface"><a href="interfaces/BodyConfig.html" class="tsd-kind-icon">Body<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyKeypoint.html" class="tsd-kind-icon">Body<wbr/>Keypoint</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyResult.html" class="tsd-kind-icon">Body<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Config.html" class="tsd-kind-icon">Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAntiSpoofConfig.html" class="tsd-kind-icon">Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceConfig.html" class="tsd-kind-icon">Face<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDescriptionConfig.html" class="tsd-kind-icon">Face<wbr/>Description<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDetectorConfig.html" class="tsd-kind-icon">Face<wbr/>Detector<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceEmotionConfig.html" class="tsd-kind-icon">Face<wbr/>Emotion<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceIrisConfig.html" class="tsd-kind-icon">Face<wbr/>Iris<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceLivenessConfig.html" class="tsd-kind-icon">Face<wbr/>Liveness<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceMeshConfig.html" class="tsd-kind-icon">Face<wbr/>Mesh<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceResult.html" class="tsd-kind-icon">Face<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/FilterConfig.html" class="tsd-kind-icon">Filter<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GenericConfig.html" class="tsd-kind-icon">Generic<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandConfig.html" class="tsd-kind-icon">Hand<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandResult.html" class="tsd-kind-icon">Hand<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectConfig.html" class="tsd-kind-icon">Object<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectResult.html" class="tsd-kind-icon">Object<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/PersonResult.html" class="tsd-kind-icon">Person<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Result.html" class="tsd-kind-icon">Result</a></li><li class="tsd-kind-interface"><a href="interfaces/SegmentationConfig.html" class="tsd-kind-icon">Segmentation<wbr/>Config</a></li></ul></section><section class="tsd-index-section "><h3>Type aliases</h3><ul class="tsd-index-list"><li class="tsd-kind-type-alias"><a href="index.html#AnyCanvas" class="tsd-kind-icon">Any<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyImage" class="tsd-kind-icon">Any<wbr/>Image</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyVideo" class="tsd-kind-icon">Any<wbr/>Video</a></li><li class="tsd-kind-type-alias"><a href="index.html#BackendType" class="tsd-kind-icon">Backend<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotation" class="tsd-kind-icon">Body<wbr/>Annotation</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationBlazePose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationEfficientPose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyGesture" class="tsd-kind-icon">Body<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmark" class="tsd-kind-icon">Body<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkBlazePose" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkEfficientNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Efficient<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkMoveNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Move<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkPoseNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Pose<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#Box" class="tsd-kind-icon">Box</a></li><li class="tsd-kind-type-alias"><a href="index.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-type-alias"><a href="index.html#DrawOptions" class="tsd-kind-icon">Draw<wbr/>Options</a></li><li class="tsd-kind-type-alias"><a href="index.html#Emotion" class="tsd-kind-icon">Emotion</a></li><li class="tsd-kind-type-alias"><a href="index.html#Events" class="tsd-kind-icon">Events</a></li><li class="tsd-kind-type-alias"><a href="index.html#ExternalCanvas" class="tsd-kind-icon">External<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceGesture" class="tsd-kind-icon">Face<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceLandmark" class="tsd-kind-icon">Face<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#Finger" class="tsd-kind-icon">Finger</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerCurl" class="tsd-kind-icon">Finger<wbr/>Curl</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerDirection" class="tsd-kind-icon">Finger<wbr/>Direction</a></li><li class="tsd-kind-type-alias"><a href="index.html#Gender" class="tsd-kind-icon">Gender</a></li><li class="tsd-kind-type-alias"><a href="index.html#GestureResult" class="tsd-kind-icon">Gesture<wbr/>Result</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandGesture" class="tsd-kind-icon">Hand<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandType" class="tsd-kind-icon">Hand<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#ImageObjects" class="tsd-kind-icon">Image<wbr/>Objects</a></li><li class="tsd-kind-type-alias"><a href="index.html#Input" class="tsd-kind-icon">Input</a></li><li class="tsd-kind-type-alias"><a href="index.html#IrisGesture" class="tsd-kind-icon">Iris<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#ObjectType" class="tsd-kind-icon">Object<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#Point" class="tsd-kind-icon">Point</a></li><li class="tsd-kind-type-alias"><a href="index.html#Race" class="tsd-kind-icon">Race</a></li><li class="tsd-kind-type-alias"><a href="index.html#TensorLike" class="tsd-kind-icon">Tensor<wbr/>Like</a></li><li class="tsd-kind-type-alias"><a href="index.html#WarmupType" class="tsd-kind-icon">Warmup<wbr/>Type</a></li></ul></section><section class="tsd-index-section "><h3>Variables</h3><ul class="tsd-index-list"><li class="tsd-kind-variable"><a href="index.html#defaults" class="tsd-kind-icon">defaults</a></li><li class="tsd-kind-variable"><a href="index.html#env" class="tsd-kind-icon">env</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>References</h2><section class="tsd-panel tsd-member tsd-kind-reference"><a id="default" class="tsd-anchor"></a><h3 class="tsd-anchor-link">default<a href="#default" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Renames and re-exports <a href="classes/Human.html">Human</a></section></section><section class="tsd-panel-group tsd-member-group "><h2>Type aliases</h2><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="AnyCanvas" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Any<wbr/>Canvas<a href="#AnyCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Any<wbr/>Canvas<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/exports.ts#L27">src/exports.ts:27</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>@vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="assets/style.css"/><link rel="stylesheet" href="assets/highlight.css"/><script async src="assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base="."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><h1>@vladmandic/human - v2.6.4</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>References</h3><ul class="tsd-index-list"><li class="tsd-kind-reference"><a href="index.html#default" class="tsd-kind-icon">default</a></li></ul></section><section class="tsd-index-section "><h3>Namespaces</h3><ul class="tsd-index-list"><li class="tsd-kind-namespace"><a href="modules/Tensor.html" class="tsd-kind-icon">Tensor</a></li></ul></section><section class="tsd-index-section "><h3>Enumerations</h3><ul class="tsd-index-list"><li class="tsd-kind-enum"><a href="enums/Rank.html" class="tsd-kind-icon">Rank</a></li></ul></section><section class="tsd-index-section "><h3>Classes</h3><ul class="tsd-index-list"><li class="tsd-kind-class"><a href="classes/Env.html" class="tsd-kind-icon">Env</a></li><li class="tsd-kind-class"><a href="classes/GraphModel.html" class="tsd-kind-icon">Graph<wbr/>Model</a></li><li class="tsd-kind-class"><a href="classes/Human.html" class="tsd-kind-icon">Human</a></li><li class="tsd-kind-class"><a href="classes/Models.html" class="tsd-kind-icon">Models</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/Tensor.html" class="tsd-kind-icon">Tensor</a></li></ul></section><section class="tsd-index-section "><h3>Interfaces</h3><ul class="tsd-index-list"><li class="tsd-kind-interface"><a href="interfaces/BodyConfig.html" class="tsd-kind-icon">Body<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyKeypoint.html" class="tsd-kind-icon">Body<wbr/>Keypoint</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyResult.html" class="tsd-kind-icon">Body<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Config.html" class="tsd-kind-icon">Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAntiSpoofConfig.html" class="tsd-kind-icon">Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceConfig.html" class="tsd-kind-icon">Face<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDescriptionConfig.html" class="tsd-kind-icon">Face<wbr/>Description<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDetectorConfig.html" class="tsd-kind-icon">Face<wbr/>Detector<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceEmotionConfig.html" class="tsd-kind-icon">Face<wbr/>Emotion<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceIrisConfig.html" class="tsd-kind-icon">Face<wbr/>Iris<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceLivenessConfig.html" class="tsd-kind-icon">Face<wbr/>Liveness<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceMeshConfig.html" class="tsd-kind-icon">Face<wbr/>Mesh<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceResult.html" class="tsd-kind-icon">Face<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/FilterConfig.html" class="tsd-kind-icon">Filter<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GenericConfig.html" class="tsd-kind-icon">Generic<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandConfig.html" class="tsd-kind-icon">Hand<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandResult.html" class="tsd-kind-icon">Hand<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectConfig.html" class="tsd-kind-icon">Object<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectResult.html" class="tsd-kind-icon">Object<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/PersonResult.html" class="tsd-kind-icon">Person<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Result.html" class="tsd-kind-icon">Result</a></li><li class="tsd-kind-interface"><a href="interfaces/SegmentationConfig.html" class="tsd-kind-icon">Segmentation<wbr/>Config</a></li></ul></section><section class="tsd-index-section "><h3>Type aliases</h3><ul class="tsd-index-list"><li class="tsd-kind-type-alias"><a href="index.html#AnyCanvas" class="tsd-kind-icon">Any<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyImage" class="tsd-kind-icon">Any<wbr/>Image</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyVideo" class="tsd-kind-icon">Any<wbr/>Video</a></li><li class="tsd-kind-type-alias"><a href="index.html#BackendType" class="tsd-kind-icon">Backend<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotation" class="tsd-kind-icon">Body<wbr/>Annotation</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationBlazePose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationEfficientPose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyGesture" class="tsd-kind-icon">Body<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmark" class="tsd-kind-icon">Body<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkBlazePose" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkEfficientNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Efficient<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkMoveNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Move<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkPoseNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Pose<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#Box" class="tsd-kind-icon">Box</a></li><li class="tsd-kind-type-alias"><a href="index.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-type-alias"><a href="index.html#DrawOptions" class="tsd-kind-icon">Draw<wbr/>Options</a></li><li class="tsd-kind-type-alias"><a href="index.html#Emotion" class="tsd-kind-icon">Emotion</a></li><li class="tsd-kind-type-alias"><a href="index.html#Events" class="tsd-kind-icon">Events</a></li><li class="tsd-kind-type-alias"><a href="index.html#ExternalCanvas" class="tsd-kind-icon">External<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceGesture" class="tsd-kind-icon">Face<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceLandmark" class="tsd-kind-icon">Face<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#Finger" class="tsd-kind-icon">Finger</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerCurl" class="tsd-kind-icon">Finger<wbr/>Curl</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerDirection" class="tsd-kind-icon">Finger<wbr/>Direction</a></li><li class="tsd-kind-type-alias"><a href="index.html#Gender" class="tsd-kind-icon">Gender</a></li><li class="tsd-kind-type-alias"><a href="index.html#GestureResult" class="tsd-kind-icon">Gesture<wbr/>Result</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandGesture" class="tsd-kind-icon">Hand<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandType" class="tsd-kind-icon">Hand<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#ImageObjects" class="tsd-kind-icon">Image<wbr/>Objects</a></li><li class="tsd-kind-type-alias"><a href="index.html#Input" class="tsd-kind-icon">Input</a></li><li class="tsd-kind-type-alias"><a href="index.html#IrisGesture" class="tsd-kind-icon">Iris<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#ObjectType" class="tsd-kind-icon">Object<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#Point" class="tsd-kind-icon">Point</a></li><li class="tsd-kind-type-alias"><a href="index.html#Race" class="tsd-kind-icon">Race</a></li><li class="tsd-kind-type-alias"><a href="index.html#TensorLike" class="tsd-kind-icon">Tensor<wbr/>Like</a></li><li class="tsd-kind-type-alias"><a href="index.html#WarmupType" class="tsd-kind-icon">Warmup<wbr/>Type</a></li></ul></section><section class="tsd-index-section "><h3>Variables</h3><ul class="tsd-index-list"><li class="tsd-kind-variable"><a href="index.html#defaults" class="tsd-kind-icon">defaults</a></li><li class="tsd-kind-variable"><a href="index.html#env" class="tsd-kind-icon">env</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>References</h2><section class="tsd-panel tsd-member tsd-kind-reference"><a id="default" class="tsd-anchor"></a><h3 class="tsd-anchor-link">default<a href="#default" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3>Renames and re-exports <a href="classes/Human.html">Human</a></section></section><section class="tsd-panel-group tsd-member-group "><h2>Type aliases</h2><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="AnyCanvas" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Any<wbr/>Canvas<a href="#AnyCanvas" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Any<wbr/>Canvas<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">HTMLCanvasElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">OffscreenCanvas</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/exports.ts#L27">src/exports.ts:27</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Defines all possible canvas types</p>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="AnyImage" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Any<wbr/>Image<a href="#AnyImage" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Any<wbr/>Image<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">HTMLImageElement</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-symbol">typeof </span><span class="tsd-signature-type">Image</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/exports.ts#L29">src/exports.ts:29</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Defines all possible image types</p>
@@ -81,11 +81,11 @@ Each result has:</p>
 <p>iris gesture type</p>
 </div></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="ObjectType" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Object<wbr/>Type<a href="#ObjectType" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Object<wbr/>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;person&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bicycle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;car&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;motorcycle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;airplane&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bus&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;train&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;truck&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;boat&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;traffic light&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;fire hydrant&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;stop sign&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;parking meter&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bench&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bird&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cat&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;dog&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;horse&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sheep&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cow&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;elephant&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bear&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;zebra&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;giraffe&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;backpack&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;umbrella&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;handbag&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tie&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;suitcase&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;frisbee&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;skis&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;snowboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sports ball&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;kite&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;baseball bat&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;baseball glove&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;skateboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;surfboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tennis racket&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bottle&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;wine glass&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cup&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;fork&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;knife&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;spoon&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bowl&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;banana&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;apple&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sandwich&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;orange&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;broccoli&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;carrot&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;hot dog&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;pizza&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;donut&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cake&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;chair&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;couch&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;potted plant&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;bed&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;dining table&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;toilet&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;tv&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;laptop&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;mouse&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;remote&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;keyboard&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;cell phone&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;microwave&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;oven&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;toaster&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;sink&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;refrigerator&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;book&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;clock&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;vase&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;scissors&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;teddy bear&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;hair drier&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;toothbrush&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L144">src/result.ts:144</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="Point" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Point<a href="#Point" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Point<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">, </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">?</span><span class="tsd-signature-symbol">]</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L12">src/result.ts:12</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>generic point as [x, y, z?]</p>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="Race" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Race<a href="#Race" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Race<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;white&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;black&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;asian&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;indian&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;other&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L16">src/result.ts:16</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="TensorLike" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Tensor<wbr/>Like<a href="#TensorLike" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Tensor<wbr/>Like<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TypedArray</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">RecursiveArray</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TypedArray</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">RecursiveArray</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">RecursiveArray</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.13.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:78</li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>docalias</dt><dd><p>TypedArray|Array</p>
+</div></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="Race" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Race<a href="#Race" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Race<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">&quot;white&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;black&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;asian&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;indian&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;other&quot;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L16">src/result.ts:16</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="TensorLike" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Tensor<wbr/>Like<a href="#TensorLike" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Tensor<wbr/>Like<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">TypedArray</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">RecursiveArray</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">number</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">TypedArray</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">RecursiveArray</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">boolean</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">RecursiveArray</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol">&gt;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">Uint8Array</span><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in node_modules/.pnpm/@tensorflow+tfjs-core@3.14.0/node_modules/@tensorflow/tfjs-core/dist/types.d.ts:78</li></ul></aside><div class="tsd-comment tsd-typography"><dl class="tsd-comment-tags"><dt>docalias</dt><dd><p>TypedArray|Array</p>
 </dd></dl></div></section><section class="tsd-panel tsd-member tsd-kind-type-alias"><a id="WarmupType" class="tsd-anchor"></a><h3 class="tsd-anchor-link">Warmup<wbr/>Type<a href="#WarmupType" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">Warmup<wbr/>Type<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">[</span><span class="tsd-signature-type">&quot;&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;none&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;face&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;full&quot;</span><span class="tsd-signature-symbol"> | </span><span class="tsd-signature-type">&quot;body&quot;</span><span class="tsd-signature-symbol">]</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L202">src/config.ts:202</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Possible values for <code>human.warmup</code></p>
-</div></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Variables</h2><section class="tsd-panel tsd-member tsd-kind-variable"><a id="defaults" class="tsd-anchor"></a><h3 class="tsd-anchor-link">defaults<a href="#defaults" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">defaults<span class="tsd-signature-symbol">:</span> <a href="interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L299">src/config.ts:299</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
+</div></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Variables</h2><section class="tsd-panel tsd-member tsd-kind-variable"><a id="defaults" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagConst">Const</span> defaults<a href="#defaults" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">defaults<span class="tsd-signature-symbol">:</span> <a href="interfaces/Config.html" class="tsd-signature-type" data-tsd-kind="Interface">Config</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L299">src/config.ts:299</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <ul>
 <li><a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">See all default Config values...</a></li>
 </ul>
-</div></div></section><section class="tsd-panel tsd-member tsd-kind-variable"><a id="env" class="tsd-anchor"></a><h3 class="tsd-anchor-link">env<a href="#env" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">env<span class="tsd-signature-symbol">:</span> <a href="classes/Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L175">src/util/env.ts:175</a></li></ul></aside></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class="current"><a href="index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="tsd-kind-reference"><a href="index.html#default" class="tsd-kind-icon">default</a></li><li class="tsd-kind-enum"><a href="enums/Rank.html" class="tsd-kind-icon">Rank</a></li><li class="tsd-kind-class"><a href="classes/Env.html" class="tsd-kind-icon">Env</a></li><li class="tsd-kind-class"><a href="classes/GraphModel.html" class="tsd-kind-icon">Graph<wbr/>Model</a></li><li class="tsd-kind-class"><a href="classes/Human.html" class="tsd-kind-icon">Human</a></li><li class="tsd-kind-class"><a href="classes/Models.html" class="tsd-kind-icon">Models</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/Tensor.html" class="tsd-kind-icon">Tensor</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyConfig.html" class="tsd-kind-icon">Body<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyKeypoint.html" class="tsd-kind-icon">Body<wbr/>Keypoint</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyResult.html" class="tsd-kind-icon">Body<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Config.html" class="tsd-kind-icon">Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAntiSpoofConfig.html" class="tsd-kind-icon">Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceConfig.html" class="tsd-kind-icon">Face<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDescriptionConfig.html" class="tsd-kind-icon">Face<wbr/>Description<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDetectorConfig.html" class="tsd-kind-icon">Face<wbr/>Detector<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceEmotionConfig.html" class="tsd-kind-icon">Face<wbr/>Emotion<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceIrisConfig.html" class="tsd-kind-icon">Face<wbr/>Iris<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceLivenessConfig.html" class="tsd-kind-icon">Face<wbr/>Liveness<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceMeshConfig.html" class="tsd-kind-icon">Face<wbr/>Mesh<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceResult.html" class="tsd-kind-icon">Face<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/FilterConfig.html" class="tsd-kind-icon">Filter<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GenericConfig.html" class="tsd-kind-icon">Generic<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandConfig.html" class="tsd-kind-icon">Hand<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandResult.html" class="tsd-kind-icon">Hand<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectConfig.html" class="tsd-kind-icon">Object<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectResult.html" class="tsd-kind-icon">Object<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/PersonResult.html" class="tsd-kind-icon">Person<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Result.html" class="tsd-kind-icon">Result</a></li><li class="tsd-kind-interface"><a href="interfaces/SegmentationConfig.html" class="tsd-kind-icon">Segmentation<wbr/>Config</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyCanvas" class="tsd-kind-icon">Any<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyImage" class="tsd-kind-icon">Any<wbr/>Image</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyVideo" class="tsd-kind-icon">Any<wbr/>Video</a></li><li class="tsd-kind-type-alias"><a href="index.html#BackendType" class="tsd-kind-icon">Backend<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotation" class="tsd-kind-icon">Body<wbr/>Annotation</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationBlazePose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationEfficientPose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyGesture" 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href="index.html#DrawOptions" class="tsd-kind-icon">Draw<wbr/>Options</a></li><li class="tsd-kind-type-alias"><a href="index.html#Emotion" class="tsd-kind-icon">Emotion</a></li><li class="tsd-kind-type-alias"><a href="index.html#Events" class="tsd-kind-icon">Events</a></li><li class="tsd-kind-type-alias"><a href="index.html#ExternalCanvas" class="tsd-kind-icon">External<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceGesture" class="tsd-kind-icon">Face<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceLandmark" class="tsd-kind-icon">Face<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#Finger" class="tsd-kind-icon">Finger</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerCurl" class="tsd-kind-icon">Finger<wbr/>Curl</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerDirection" class="tsd-kind-icon">Finger<wbr/>Direction</a></li><li class="tsd-kind-type-alias"><a href="index.html#Gender" class="tsd-kind-icon">Gender</a></li><li class="tsd-kind-type-alias"><a href="index.html#GestureResult" class="tsd-kind-icon">Gesture<wbr/>Result</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandGesture" class="tsd-kind-icon">Hand<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandType" class="tsd-kind-icon">Hand<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#ImageObjects" class="tsd-kind-icon">Image<wbr/>Objects</a></li><li class="tsd-kind-type-alias"><a href="index.html#Input" class="tsd-kind-icon">Input</a></li><li class="tsd-kind-type-alias"><a href="index.html#IrisGesture" class="tsd-kind-icon">Iris<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#ObjectType" class="tsd-kind-icon">Object<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#Point" class="tsd-kind-icon">Point</a></li><li class="tsd-kind-type-alias"><a href="index.html#Race" 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+</div></div></section><section class="tsd-panel tsd-member tsd-kind-variable"><a id="env" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagConst">Const</span> env<a href="#env" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">env<span class="tsd-signature-symbol">:</span> <a href="classes/Env.html" class="tsd-signature-type" data-tsd-kind="Class">Env</a><span class="tsd-signature-symbol"> = ...</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/util/env.ts#L175">src/util/env.ts:175</a></li></ul></aside></section></section></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class="current"><a href="index.html">Exports</a></li><li class=" tsd-kind-namespace"><a href="modules/Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul><li class="tsd-kind-reference"><a href="index.html#default" class="tsd-kind-icon">default</a></li><li class="tsd-kind-enum"><a href="enums/Rank.html" class="tsd-kind-icon">Rank</a></li><li class="tsd-kind-class"><a href="classes/Env.html" class="tsd-kind-icon">Env</a></li><li class="tsd-kind-class"><a href="classes/GraphModel.html" class="tsd-kind-icon">Graph<wbr/>Model</a></li><li class="tsd-kind-class"><a href="classes/Human.html" class="tsd-kind-icon">Human</a></li><li class="tsd-kind-class"><a href="classes/Models.html" class="tsd-kind-icon">Models</a></li><li class="tsd-kind-class tsd-has-type-parameter"><a href="classes/Tensor.html" class="tsd-kind-icon">Tensor</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyConfig.html" class="tsd-kind-icon">Body<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyKeypoint.html" class="tsd-kind-icon">Body<wbr/>Keypoint</a></li><li class="tsd-kind-interface"><a href="interfaces/BodyResult.html" class="tsd-kind-icon">Body<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Config.html" class="tsd-kind-icon">Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceAntiSpoofConfig.html" class="tsd-kind-icon">Face<wbr/>Anti<wbr/>Spoof<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceConfig.html" class="tsd-kind-icon">Face<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDescriptionConfig.html" class="tsd-kind-icon">Face<wbr/>Description<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceDetectorConfig.html" class="tsd-kind-icon">Face<wbr/>Detector<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceEmotionConfig.html" class="tsd-kind-icon">Face<wbr/>Emotion<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceIrisConfig.html" class="tsd-kind-icon">Face<wbr/>Iris<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceLivenessConfig.html" class="tsd-kind-icon">Face<wbr/>Liveness<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceMeshConfig.html" class="tsd-kind-icon">Face<wbr/>Mesh<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/FaceResult.html" class="tsd-kind-icon">Face<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/FilterConfig.html" class="tsd-kind-icon">Filter<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GenericConfig.html" class="tsd-kind-icon">Generic<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/GestureConfig.html" class="tsd-kind-icon">Gesture<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandConfig.html" class="tsd-kind-icon">Hand<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/HandResult.html" class="tsd-kind-icon">Hand<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectConfig.html" class="tsd-kind-icon">Object<wbr/>Config</a></li><li class="tsd-kind-interface"><a href="interfaces/ObjectResult.html" class="tsd-kind-icon">Object<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/PersonResult.html" class="tsd-kind-icon">Person<wbr/>Result</a></li><li class="tsd-kind-interface"><a href="interfaces/Result.html" class="tsd-kind-icon">Result</a></li><li class="tsd-kind-interface"><a href="interfaces/SegmentationConfig.html" class="tsd-kind-icon">Segmentation<wbr/>Config</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyCanvas" class="tsd-kind-icon">Any<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyImage" class="tsd-kind-icon">Any<wbr/>Image</a></li><li class="tsd-kind-type-alias"><a href="index.html#AnyVideo" class="tsd-kind-icon">Any<wbr/>Video</a></li><li class="tsd-kind-type-alias"><a href="index.html#BackendType" class="tsd-kind-icon">Backend<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotation" class="tsd-kind-icon">Body<wbr/>Annotation</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationBlazePose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyAnnotationEfficientPose" class="tsd-kind-icon">Body<wbr/>Annotation<wbr/>Efficient<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyGesture" class="tsd-kind-icon">Body<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmark" class="tsd-kind-icon">Body<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkBlazePose" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Blaze<wbr/>Pose</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkEfficientNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Efficient<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkMoveNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Move<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#BodyLandmarkPoseNet" class="tsd-kind-icon">Body<wbr/>Landmark<wbr/>Pose<wbr/>Net</a></li><li class="tsd-kind-type-alias"><a href="index.html#Box" class="tsd-kind-icon">Box</a></li><li class="tsd-kind-type-alias"><a href="index.html#Descriptor" class="tsd-kind-icon">Descriptor</a></li><li class="tsd-kind-type-alias"><a href="index.html#DrawOptions" class="tsd-kind-icon">Draw<wbr/>Options</a></li><li class="tsd-kind-type-alias"><a href="index.html#Emotion" class="tsd-kind-icon">Emotion</a></li><li class="tsd-kind-type-alias"><a href="index.html#Events" class="tsd-kind-icon">Events</a></li><li class="tsd-kind-type-alias"><a href="index.html#ExternalCanvas" class="tsd-kind-icon">External<wbr/>Canvas</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceGesture" class="tsd-kind-icon">Face<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#FaceLandmark" class="tsd-kind-icon">Face<wbr/>Landmark</a></li><li class="tsd-kind-type-alias"><a href="index.html#Finger" class="tsd-kind-icon">Finger</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerCurl" class="tsd-kind-icon">Finger<wbr/>Curl</a></li><li class="tsd-kind-type-alias"><a href="index.html#FingerDirection" class="tsd-kind-icon">Finger<wbr/>Direction</a></li><li class="tsd-kind-type-alias"><a href="index.html#Gender" class="tsd-kind-icon">Gender</a></li><li class="tsd-kind-type-alias"><a href="index.html#GestureResult" class="tsd-kind-icon">Gesture<wbr/>Result</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandGesture" class="tsd-kind-icon">Hand<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#HandType" class="tsd-kind-icon">Hand<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#ImageObjects" class="tsd-kind-icon">Image<wbr/>Objects</a></li><li class="tsd-kind-type-alias"><a href="index.html#Input" class="tsd-kind-icon">Input</a></li><li class="tsd-kind-type-alias"><a href="index.html#IrisGesture" class="tsd-kind-icon">Iris<wbr/>Gesture</a></li><li class="tsd-kind-type-alias"><a href="index.html#ObjectType" class="tsd-kind-icon">Object<wbr/>Type</a></li><li class="tsd-kind-type-alias"><a href="index.html#Point" class="tsd-kind-icon">Point</a></li><li class="tsd-kind-type-alias"><a href="index.html#Race" class="tsd-kind-icon">Race</a></li><li class="tsd-kind-type-alias"><a href="index.html#TensorLike" class="tsd-kind-icon">Tensor<wbr/>Like</a></li><li class="tsd-kind-type-alias"><a href="index.html#WarmupType" class="tsd-kind-icon">Warmup<wbr/>Type</a></li><li class="tsd-kind-variable"><a href="index.html#defaults" class="tsd-kind-icon">defaults</a></li><li class="tsd-kind-variable"><a href="index.html#env" class="tsd-kind-icon">env</a></li></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="assets/main.js"></script></body></html>
\ No newline at end of file
diff --git a/typedoc/interfaces/BodyConfig.html b/typedoc/interfaces/BodyConfig.html
index 3dc577bc..4a7d8c9a 100644
--- a/typedoc/interfaces/BodyConfig.html
+++ b/typedoc/interfaces/BodyConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="BodyConfig.html">BodyConfig</a></li></ul><h1>Interface BodyConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="BodyConfig.html">BodyConfig</a></li></ul><h1>Interface BodyConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Configures all body detection specific options</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">BodyConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="BodyConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyConfig.html#maxDetected" class="tsd-kind-icon">max<wbr/>Detected</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyConfig.html#minConfidence" class="tsd-kind-icon">min<wbr/>Confidence</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="BodyConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="BodyConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="BodyConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/BodyKeypoint.html b/typedoc/interfaces/BodyKeypoint.html
index 10803968..841ea70c 100644
--- a/typedoc/interfaces/BodyKeypoint.html
+++ b/typedoc/interfaces/BodyKeypoint.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyKeypoint | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="BodyKeypoint.html">BodyKeypoint</a></li></ul><h1>Interface BodyKeypoint</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyKeypoint | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="BodyKeypoint.html">BodyKeypoint</a></li></ul><h1>Interface BodyKeypoint</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Body Result keypoints</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">BodyKeypoint</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyKeypoint.html#distance" class="tsd-kind-icon">distance</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyKeypoint.html#part" class="tsd-kind-icon">part</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyKeypoint.html#position" class="tsd-kind-icon">position</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyKeypoint.html#positionRaw" class="tsd-kind-icon">position<wbr/>Raw</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyKeypoint.html#score" class="tsd-kind-icon">score</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="distance" class="tsd-anchor"></a><h3 class="tsd-anchor-link"><span class="tsd-flag ts-flagOptional">Optional</span> distance<a href="#distance" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">distance<span class="tsd-signature-symbol">?:</span> <a href="../index.html#Point" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L94">src/result.ts:94</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>body part position relative to body center in meters</p>
diff --git a/typedoc/interfaces/BodyResult.html b/typedoc/interfaces/BodyResult.html
index d2c4f77c..69320cef 100644
--- a/typedoc/interfaces/BodyResult.html
+++ b/typedoc/interfaces/BodyResult.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyResult | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="BodyResult.html">BodyResult</a></li></ul><h1>Interface BodyResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>BodyResult | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="BodyResult.html">BodyResult</a></li></ul><h1>Interface BodyResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Body results</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">BodyResult</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyResult.html#annotations" class="tsd-kind-icon">annotations</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyResult.html#box" class="tsd-kind-icon">box</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyResult.html#boxRaw" class="tsd-kind-icon">box<wbr/>Raw</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyResult.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyResult.html#keypoints" class="tsd-kind-icon">keypoints</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="BodyResult.html#score" class="tsd-kind-icon">score</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="annotations" class="tsd-anchor"></a><h3 class="tsd-anchor-link">annotations<a href="#annotations" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">annotations<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#BodyAnnotation" class="tsd-signature-type" data-tsd-kind="Type alias">BodyAnnotation</a><span class="tsd-signature-symbol">, </span><a href="../index.html#Point" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L112">src/result.ts:112</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>detected body keypoints combined into annotated parts</p>
diff --git a/typedoc/interfaces/Config.html b/typedoc/interfaces/Config.html
index 36d2ec6c..5608a098 100644
--- a/typedoc/interfaces/Config.html
+++ b/typedoc/interfaces/Config.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Config | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Config.html">Config</a></li></ul><h1>Interface Config</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Config | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Config.html">Config</a></li></ul><h1>Interface Config</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Configuration interface definition for <strong>Human</strong> library
 Contains all configurable parameters
 Defaults: <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L262">config</a></p>
diff --git a/typedoc/interfaces/FaceAntiSpoofConfig.html b/typedoc/interfaces/FaceAntiSpoofConfig.html
index 752b3f63..678168f3 100644
--- a/typedoc/interfaces/FaceAntiSpoofConfig.html
+++ b/typedoc/interfaces/FaceAntiSpoofConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceAntiSpoofConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceAntiSpoofConfig.html">FaceAntiSpoofConfig</a></li></ul><h1>Interface FaceAntiSpoofConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceAntiSpoofConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceAntiSpoofConfig.html">FaceAntiSpoofConfig</a></li></ul><h1>Interface FaceAntiSpoofConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Anti-spoofing part of face configuration</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceAntiSpoofConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section tsd-is-inherited"><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceAntiSpoofConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceAntiSpoofConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceAntiSpoofConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceAntiSpoofConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group tsd-is-inherited"><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceConfig.html b/typedoc/interfaces/FaceConfig.html
index e7ab191c..c5acf5d6 100644
--- a/typedoc/interfaces/FaceConfig.html
+++ b/typedoc/interfaces/FaceConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceConfig.html">FaceConfig</a></li></ul><h1>Interface FaceConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceConfig.html">FaceConfig</a></li></ul><h1>Interface FaceConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#antispoof" class="tsd-kind-icon">antispoof</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#description" class="tsd-kind-icon">description</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#detector" class="tsd-kind-icon">detector</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#emotion" class="tsd-kind-icon">emotion</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#iris" class="tsd-kind-icon">iris</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#liveness" class="tsd-kind-icon">liveness</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceConfig.html#mesh" class="tsd-kind-icon">mesh</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="antispoof" class="tsd-anchor"></a><h3 class="tsd-anchor-link">antispoof<a href="#antispoof" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">antispoof<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceAntiSpoofConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceAntiSpoofConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L70">src/config.ts:70</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="description" class="tsd-anchor"></a><h3 class="tsd-anchor-link">description<a href="#description" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">description<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceDescriptionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceDescriptionConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L68">src/config.ts:68</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="detector" class="tsd-anchor"></a><h3 class="tsd-anchor-link">detector<a href="#detector" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">detector<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceDetectorConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceDetectorConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L65">src/config.ts:65</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="emotion" class="tsd-anchor"></a><h3 class="tsd-anchor-link">emotion<a href="#emotion" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">emotion<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Partial</span><span class="tsd-signature-symbol">&lt;</span><a href="FaceEmotionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceEmotionConfig</a><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L69">src/config.ts:69</a></li></ul></aside></section><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceDescriptionConfig.html b/typedoc/interfaces/FaceDescriptionConfig.html
index 171152c7..cd04f5ba 100644
--- a/typedoc/interfaces/FaceDescriptionConfig.html
+++ b/typedoc/interfaces/FaceDescriptionConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceDescriptionConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceDescriptionConfig.html">FaceDescriptionConfig</a></li></ul><h1>Interface FaceDescriptionConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceDescriptionConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceDescriptionConfig.html">FaceDescriptionConfig</a></li></ul><h1>Interface FaceDescriptionConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Description or face embedding part of face configuration</p>
 <ul>
 <li>also used by age and gender detection</li>
diff --git a/typedoc/interfaces/FaceDetectorConfig.html b/typedoc/interfaces/FaceDetectorConfig.html
index fbcde9bf..f93e6bfc 100644
--- a/typedoc/interfaces/FaceDetectorConfig.html
+++ b/typedoc/interfaces/FaceDetectorConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceDetectorConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceDetectorConfig.html">FaceDetectorConfig</a></li></ul><h1>Interface FaceDetectorConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceDetectorConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceDetectorConfig.html">FaceDetectorConfig</a></li></ul><h1>Interface FaceDetectorConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Detector part of face configuration</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceDetectorConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceDetectorConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceDetectorConfig.html#iouThreshold" class="tsd-kind-icon">iou<wbr/>Threshold</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceDetectorConfig.html#mask" class="tsd-kind-icon">mask</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceDetectorConfig.html#maxDetected" class="tsd-kind-icon">max<wbr/>Detected</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceDetectorConfig.html#minConfidence" class="tsd-kind-icon">min<wbr/>Confidence</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceDetectorConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceDetectorConfig.html#return" class="tsd-kind-icon">return</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceDetectorConfig.html#rotation" class="tsd-kind-icon">rotation</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceDetectorConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceDetectorConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceEmotionConfig.html b/typedoc/interfaces/FaceEmotionConfig.html
index 209abd07..0ffca15c 100644
--- a/typedoc/interfaces/FaceEmotionConfig.html
+++ b/typedoc/interfaces/FaceEmotionConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceEmotionConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceEmotionConfig.html">FaceEmotionConfig</a></li></ul><h1>Interface FaceEmotionConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceEmotionConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceEmotionConfig.html">FaceEmotionConfig</a></li></ul><h1>Interface FaceEmotionConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Emotion part of face configuration</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceEmotionConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceEmotionConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="FaceEmotionConfig.html#minConfidence" class="tsd-kind-icon">min<wbr/>Confidence</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceEmotionConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceEmotionConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceEmotionConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceIrisConfig.html b/typedoc/interfaces/FaceIrisConfig.html
index 19587501..c2ad7887 100644
--- a/typedoc/interfaces/FaceIrisConfig.html
+++ b/typedoc/interfaces/FaceIrisConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceIrisConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceIrisConfig.html">FaceIrisConfig</a></li></ul><h1>Interface FaceIrisConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceIrisConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceIrisConfig.html">FaceIrisConfig</a></li></ul><h1>Interface FaceIrisConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Iris part of face configuration</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceIrisConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section tsd-is-inherited"><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceIrisConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceIrisConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceIrisConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceIrisConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group tsd-is-inherited"><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceLivenessConfig.html b/typedoc/interfaces/FaceLivenessConfig.html
index b5893237..35e89bcf 100644
--- a/typedoc/interfaces/FaceLivenessConfig.html
+++ b/typedoc/interfaces/FaceLivenessConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceLivenessConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceLivenessConfig.html">FaceLivenessConfig</a></li></ul><h1>Interface FaceLivenessConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceLivenessConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceLivenessConfig.html">FaceLivenessConfig</a></li></ul><h1>Interface FaceLivenessConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Liveness part of face configuration</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceLivenessConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section tsd-is-inherited"><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceLivenessConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceLivenessConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceLivenessConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceLivenessConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group tsd-is-inherited"><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceMeshConfig.html b/typedoc/interfaces/FaceMeshConfig.html
index 8db668b0..397ed061 100644
--- a/typedoc/interfaces/FaceMeshConfig.html
+++ b/typedoc/interfaces/FaceMeshConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceMeshConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceMeshConfig.html">FaceMeshConfig</a></li></ul><h1>Interface FaceMeshConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceMeshConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceMeshConfig.html">FaceMeshConfig</a></li></ul><h1>Interface FaceMeshConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Mesh part of face configuration</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">FaceMeshConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section tsd-is-inherited"><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceMeshConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceMeshConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceMeshConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="FaceMeshConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group tsd-is-inherited"><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/FaceResult.html b/typedoc/interfaces/FaceResult.html
index 56bf2595..52212196 100644
--- a/typedoc/interfaces/FaceResult.html
+++ b/typedoc/interfaces/FaceResult.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceResult | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FaceResult.html">FaceResult</a></li></ul><h1>Interface FaceResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FaceResult | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FaceResult.html">FaceResult</a></li></ul><h1>Interface FaceResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Face results</p>
 <ul>
 <li>Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models</li>
diff --git a/typedoc/interfaces/FilterConfig.html b/typedoc/interfaces/FilterConfig.html
index 71a4cd1e..e738110c 100644
--- a/typedoc/interfaces/FilterConfig.html
+++ b/typedoc/interfaces/FilterConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FilterConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="FilterConfig.html">FilterConfig</a></li></ul><h1>Interface FilterConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>FilterConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="FilterConfig.html">FilterConfig</a></li></ul><h1>Interface FilterConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Run input through image filters before inference</p>
 <ul>
 <li>available only in Browser environments</li>
diff --git a/typedoc/interfaces/GenericConfig.html b/typedoc/interfaces/GenericConfig.html
index b92fffff..44e6dab3 100644
--- a/typedoc/interfaces/GenericConfig.html
+++ b/typedoc/interfaces/GenericConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GenericConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="GenericConfig.html">GenericConfig</a></li></ul><h1>Interface GenericConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GenericConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="GenericConfig.html">GenericConfig</a></li></ul><h1>Interface GenericConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Generic config type inherited by all module types</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GenericConfig</span><ul class="tsd-hierarchy"><li><a href="FaceDetectorConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceDetectorConfig</a></li><li><a href="FaceMeshConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceMeshConfig</a></li><li><a href="FaceIrisConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceIrisConfig</a></li><li><a href="FaceDescriptionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceDescriptionConfig</a></li><li><a href="FaceEmotionConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceEmotionConfig</a></li><li><a href="FaceAntiSpoofConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceAntiSpoofConfig</a></li><li><a href="FaceLivenessConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceLivenessConfig</a></li><li><a href="FaceConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">FaceConfig</a></li><li><a href="BodyConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyConfig</a></li><li><a href="HandConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">HandConfig</a></li><li><a href="ObjectConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">ObjectConfig</a></li><li><a href="SegmentationConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">SegmentationConfig</a></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GenericConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GenericConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GenericConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GenericConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/GestureConfig.html b/typedoc/interfaces/GestureConfig.html
index b8479cf4..e5e0abf9 100644
--- a/typedoc/interfaces/GestureConfig.html
+++ b/typedoc/interfaces/GestureConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GestureConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="GestureConfig.html">GestureConfig</a></li></ul><h1>Interface GestureConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>GestureConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="GestureConfig.html">GestureConfig</a></li></ul><h1>Interface GestureConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Controlls gesture detection</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">GestureConfig</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="GestureConfig.html#enabled" class="tsd-kind-icon">enabled</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L196">src/config.ts:196</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is gesture detection enabled?</p>
diff --git a/typedoc/interfaces/HandConfig.html b/typedoc/interfaces/HandConfig.html
index 4e71dc7c..98369f71 100644
--- a/typedoc/interfaces/HandConfig.html
+++ b/typedoc/interfaces/HandConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>HandConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="HandConfig.html">HandConfig</a></li></ul><h1>Interface HandConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>HandConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="HandConfig.html">HandConfig</a></li></ul><h1>Interface HandConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Configures all hand detection specific options</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">HandConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#detector" class="tsd-kind-icon">detector</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="HandConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#iouThreshold" class="tsd-kind-icon">iou<wbr/>Threshold</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#landmarks" class="tsd-kind-icon">landmarks</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#maxDetected" class="tsd-kind-icon">max<wbr/>Detected</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#minConfidence" class="tsd-kind-icon">min<wbr/>Confidence</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="HandConfig.html#modelPath-1" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#rotation" class="tsd-kind-icon">rotation</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandConfig.html#skeleton" class="tsd-kind-icon">skeleton</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="HandConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="HandConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="detector" class="tsd-anchor"></a><h3 class="tsd-anchor-link">detector<a href="#detector" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">detector<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-symbol">{ </span>modelPath<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span><span class="tsd-signature-symbol"> }</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L107">src/config.ts:107</a></li></ul></aside><div class="tsd-type-declaration"><h4>Type declaration</h4><ul class="tsd-parameters"><li class="tsd-parameter"><h5><span class="tsd-flag ts-flagOptional">Optional</span> model<wbr/>Path<span class="tsd-signature-symbol">?: </span><span class="tsd-signature-type">string</span></h5><div class="tsd-comment tsd-typography"><div class="lead">
 <p>path to hand detector model json</p>
diff --git a/typedoc/interfaces/HandResult.html b/typedoc/interfaces/HandResult.html
index df3bbb94..102ec6ab 100644
--- a/typedoc/interfaces/HandResult.html
+++ b/typedoc/interfaces/HandResult.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>HandResult | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="HandResult.html">HandResult</a></li></ul><h1>Interface HandResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>HandResult | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="HandResult.html">HandResult</a></li></ul><h1>Interface HandResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Hand results</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">HandResult</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#annotations" class="tsd-kind-icon">annotations</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#box" class="tsd-kind-icon">box</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#boxRaw" class="tsd-kind-icon">box<wbr/>Raw</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#boxScore" class="tsd-kind-icon">box<wbr/>Score</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#fingerScore" class="tsd-kind-icon">finger<wbr/>Score</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#keypoints" class="tsd-kind-icon">keypoints</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#label" class="tsd-kind-icon">label</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#landmarks" class="tsd-kind-icon">landmarks</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="HandResult.html#score" class="tsd-kind-icon">score</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="annotations" class="tsd-anchor"></a><h3 class="tsd-anchor-link">annotations<a href="#annotations" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">annotations<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Record</span><span class="tsd-signature-symbol">&lt;</span><a href="../index.html#Finger" class="tsd-signature-type" data-tsd-kind="Type alias">Finger</a><span class="tsd-signature-symbol">, </span><a href="../index.html#Point" class="tsd-signature-type" data-tsd-kind="Type alias">Point</a><span class="tsd-signature-symbol">[]</span><span class="tsd-signature-symbol">&gt;</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L139">src/result.ts:139</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>detected hand keypoints combined into annotated parts</p>
diff --git a/typedoc/interfaces/ObjectConfig.html b/typedoc/interfaces/ObjectConfig.html
index 118648eb..45c36dfc 100644
--- a/typedoc/interfaces/ObjectConfig.html
+++ b/typedoc/interfaces/ObjectConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>ObjectConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="ObjectConfig.html">ObjectConfig</a></li></ul><h1>Interface ObjectConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>ObjectConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="ObjectConfig.html">ObjectConfig</a></li></ul><h1>Interface ObjectConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Configures all object detection specific options</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><a href="GenericConfig.html" class="tsd-signature-type" data-tsd-kind="Interface">GenericConfig</a><ul class="tsd-hierarchy"><li><span class="target">ObjectConfig</span></li></ul></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="ObjectConfig.html#enabled" class="tsd-kind-icon">enabled</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectConfig.html#iouThreshold" class="tsd-kind-icon">iou<wbr/>Threshold</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectConfig.html#maxDetected" class="tsd-kind-icon">max<wbr/>Detected</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectConfig.html#minConfidence" class="tsd-kind-icon">min<wbr/>Confidence</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="ObjectConfig.html#modelPath" class="tsd-kind-icon">model<wbr/>Path</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="ObjectConfig.html#skipFrames" class="tsd-kind-icon">skip<wbr/>Frames</a></li><li class="tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a href="ObjectConfig.html#skipTime" class="tsd-kind-icon">skip<wbr/>Time</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface tsd-is-inherited"><a id="enabled" class="tsd-anchor"></a><h3 class="tsd-anchor-link">enabled<a href="#enabled" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">enabled<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">boolean</span></div><aside class="tsd-sources"><p>Inherited from <a href="GenericConfig.html">GenericConfig</a>.<a href="GenericConfig.html#enabled">enabled</a></p><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/config.ts#L7">src/config.ts:7</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>is module enabled?</p>
diff --git a/typedoc/interfaces/ObjectResult.html b/typedoc/interfaces/ObjectResult.html
index 0f909514..d8b31609 100644
--- a/typedoc/interfaces/ObjectResult.html
+++ b/typedoc/interfaces/ObjectResult.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>ObjectResult | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="ObjectResult.html">ObjectResult</a></li></ul><h1>Interface ObjectResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>ObjectResult | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="ObjectResult.html">ObjectResult</a></li></ul><h1>Interface ObjectResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Object results</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">ObjectResult</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectResult.html#box" class="tsd-kind-icon">box</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectResult.html#boxRaw" class="tsd-kind-icon">box<wbr/>Raw</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectResult.html#class" class="tsd-kind-icon">class</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectResult.html#id" class="tsd-kind-icon">id</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectResult.html#label" class="tsd-kind-icon">label</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="ObjectResult.html#score" class="tsd-kind-icon">score</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="box" class="tsd-anchor"></a><h3 class="tsd-anchor-link">box<a href="#box" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">box<span class="tsd-signature-symbol">:</span> <a href="../index.html#Box" class="tsd-signature-type" data-tsd-kind="Type alias">Box</a></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L162">src/result.ts:162</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
 <p>detected object box</p>
diff --git a/typedoc/interfaces/PersonResult.html b/typedoc/interfaces/PersonResult.html
index 790fe288..8ce961e4 100644
--- a/typedoc/interfaces/PersonResult.html
+++ b/typedoc/interfaces/PersonResult.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>PersonResult | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="PersonResult.html">PersonResult</a></li></ul><h1>Interface PersonResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>PersonResult | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="PersonResult.html">PersonResult</a></li></ul><h1>Interface PersonResult</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Person getter</p>
 <ul>
 <li>Triggers combining all individual results into a virtual person object</li>
diff --git a/typedoc/interfaces/Result.html b/typedoc/interfaces/Result.html
index c2ec38b2..50de3d59 100644
--- a/typedoc/interfaces/Result.html
+++ b/typedoc/interfaces/Result.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Result | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Result.html">Result</a></li></ul><h1>Interface Result</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Result | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Result.html">Result</a></li></ul><h1>Interface Result</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Result interface definition for <strong>Human</strong> library</p>
 </div><div><p>Contains all possible detection results</p>
 </div></div></section><section class="tsd-panel tsd-hierarchy"><h3>Hierarchy</h3><ul class="tsd-hierarchy"><li><span class="target">Result</span></li></ul></section><section class="tsd-panel-group tsd-index-group"><h2>Index</h2><section class="tsd-panel tsd-index-panel"><div class="tsd-index-content"><section class="tsd-index-section "><h3>Properties</h3><ul class="tsd-index-list"><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#body" class="tsd-kind-icon">body</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#canvas" class="tsd-kind-icon">canvas</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#error" class="tsd-kind-icon">error</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#face" class="tsd-kind-icon">face</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#gesture" class="tsd-kind-icon">gesture</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#hand" class="tsd-kind-icon">hand</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#object" class="tsd-kind-icon">object</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#performance" class="tsd-kind-icon">performance</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#persons" class="tsd-kind-icon">persons</a></li><li class="tsd-kind-property tsd-parent-kind-interface"><a href="Result.html#timestamp" class="tsd-kind-icon">timestamp</a></li></ul></section></div></section></section><section class="tsd-panel-group tsd-member-group "><h2>Properties</h2><section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface"><a id="body" class="tsd-anchor"></a><h3 class="tsd-anchor-link">body<a href="#body" aria-label="Permalink" class="tsd-anchor-icon"><svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-link" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M10 14a3.5 3.5 0 0 0 5 0l4 -4a3.5 3.5 0 0 0 -5 -5l-.5 .5"></path><path d="M14 10a3.5 3.5 0 0 0 -5 0l-4 4a3.5 3.5 0 0 0 5 5l.5 -.5"></path></svg></a></h3><div class="tsd-signature tsd-kind-icon">body<span class="tsd-signature-symbol">:</span> <a href="BodyResult.html" class="tsd-signature-type" data-tsd-kind="Interface">BodyResult</a><span class="tsd-signature-symbol">[]</span></div><aside class="tsd-sources"><ul><li>Defined in <a href="https://github.com/vladmandic/human/blob/main/src/result.ts#L207">src/result.ts:207</a></li></ul></aside><div class="tsd-comment tsd-typography"><div class="lead">
diff --git a/typedoc/interfaces/SegmentationConfig.html b/typedoc/interfaces/SegmentationConfig.html
index 507a8eea..e709cb1e 100644
--- a/typedoc/interfaces/SegmentationConfig.html
+++ b/typedoc/interfaces/SegmentationConfig.html
@@ -1,4 +1,4 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>SegmentationConfig | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="SegmentationConfig.html">SegmentationConfig</a></li></ul><h1>Interface SegmentationConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>SegmentationConfig | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="SegmentationConfig.html">SegmentationConfig</a></li></ul><h1>Interface SegmentationConfig</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"><section class="tsd-panel tsd-comment"><div class="tsd-comment tsd-typography"><div class="lead">
 <p>Configures all body segmentation module
 removes background from input containing person
 if segmentation is enabled it will run as preprocessing task before any other model
diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html
index 4b5382dd..8c2c5e3f 100644
--- a/typedoc/modules/Tensor.html
+++ b/typedoc/modules/Tensor.html
@@ -1 +1 @@
-<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Tensor | @vladmandic/human - v2.6.3</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.3"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.3</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.3</a></li><li><a href="Tensor.html">Tensor</a></li></ul><h1>Namespace Tensor</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class="current tsd-kind-namespace"><a href="Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
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+<!DOCTYPE html><html class="default"><head><meta charSet="utf-8"/><meta http-equiv="x-ua-compatible" content="IE=edge"/><title>Tensor | @vladmandic/human - v2.6.4</title><meta name="description" content="Documentation for @vladmandic/human - v2.6.4"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="../assets/style.css"/><link rel="stylesheet" href="../assets/highlight.css"/><script async src="../assets/search.js" id="search-script"></script></head><body><script>document.body.classList.add(localStorage.getItem("tsd-theme") || "os")</script><header><div class="tsd-page-toolbar"><div class="container"><div class="table-wrap"><div class="table-cell" id="tsd-search" data-base=".."><div class="field"><label for="tsd-search-field" class="tsd-widget search no-caption">Search</label><input type="text" id="tsd-search-field"/></div><ul class="results"><li class="state loading">Preparing search index...</li><li class="state failure">The search index is not available</li></ul><a href="../index.html" class="title">@vladmandic/human - v2.6.4</a></div><div class="table-cell" id="tsd-widgets"><div id="tsd-filter"><a href="#" class="tsd-widget options no-caption" data-toggle="options">Options</a><div class="tsd-filter-group"><div class="tsd-select" id="tsd-filter-visibility"><span class="tsd-select-label">All</span><ul class="tsd-select-list"><li data-value="public">Public</li><li data-value="protected">Public/Protected</li><li data-value="private" class="selected">All</li></ul></div> <input type="checkbox" id="tsd-filter-inherited" checked/><label class="tsd-widget" for="tsd-filter-inherited">Inherited</label></div></div><a href="#" class="tsd-widget menu no-caption" data-toggle="menu">Menu</a></div></div></div></div><div class="tsd-page-title"><div class="container"><ul class="tsd-breadcrumb"><li><a href="../index.html">@vladmandic/human - v2.6.4</a></li><li><a href="Tensor.html">Tensor</a></li></ul><h1>Namespace Tensor</h1></div></div></header><div class="container container-main"><div class="row"><div class="col-8 col-content"></div><div class="col-4 col-menu menu-sticky-wrap menu-highlight"><nav class="tsd-navigation primary"><ul><li class=""><a href="../index.html">Exports</a></li><li class="current tsd-kind-namespace"><a href="Tensor.html">Tensor</a></li></ul></nav><nav class="tsd-navigation secondary menu-sticky"><ul></ul></nav></div></div></div><footer class=""><div class="container"><h2>Legend</h2><div class="tsd-legend-group"><ul class="tsd-legend"><li class="tsd-kind-constructor tsd-parent-kind-class"><span class="tsd-kind-icon">Constructor</span></li><li class="tsd-kind-property tsd-parent-kind-class"><span class="tsd-kind-icon">Property</span></li><li class="tsd-kind-method tsd-parent-kind-class"><span class="tsd-kind-icon">Method</span></li></ul><ul class="tsd-legend"><li class="tsd-kind-property tsd-parent-kind-interface"><span class="tsd-kind-icon">Property</span></li></ul></div><h2>Settings</h2><p>Theme <select id="theme"><option value="os">OS</option><option value="light">Light</option><option value="dark">Dark</option></select></p></div></footer><div class="overlay"></div><script src="../assets/main.js"></script></body></html>
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